🚀 DAY 5 OF LAUNCH WEEK: Introducing Socket Firewall Enterprise.Learn more →
Socket
Book a DemoInstallSign in
Socket
Book a DemoInstallSign in

mealpy

Package Overview
Dependencies
Maintainers
1
Versions
60
Alerts
File Explorer

Advanced tools

License
Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

mealpy

MEALPY: An Open-source Library for Latest Meta-heuristic Algorithms in Python

pipPyPI
Version
3.0.3
Maintainers
1

MEALPY

GitHub release Wheel PyPI version PyPI - Python Version Downloads Tests & Publishes to PyPI GitHub Release Date Chat Documentation Status DOI License: MIT

MEALPY is the world's largest Python library, offering a comprehensive collection of cutting-edge meta-heuristic algorithms. These include nature-inspired algorithms, bio-inspired algorithms, black-box optimization, global search optimizers, iterative learning algorithms, continuous optimization, derivative-free optimization, gradient-free optimization, zeroth-order optimization, stochastic search optimization, and random search optimization. All these methods fall under the category of population-based metaheuristics (PBMs), which are among the most popular algorithms in the field of approximate optimization. For detailed updates in each new version, please refer to the ChangeLog file.

  • Free software: MIT license
  • Total algorithms: 233 (206 official (original, hybrid, variants), 27 developed)
  • Documentation: https://mealpy.readthedocs.io/en/latest/
  • Python versions: >=3.8x
  • Dependencies: numpy, scipy, pandas, matplotlib

📌 Goals

Our goals are to implement all classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy:

  • Analyse parameters of meta-heuristic algorithms.
  • Perform Qualitative and Quantitative Analysis of algorithms.
  • Analyse rate of convergence of algorithms.
  • Test and Analyse the scalability and the robustness of algorithms.
  • Save results in various formats (csv, json, pickle, png, pdf, jpeg)
  • Export and import models can also be done with Mealpy.
  • Solve any optimization problem

📄 Citation Request

Please include these citations if you plan to use this library:

@article{van2023mealpy,
  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal={Journal of Systems Architecture},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.sysarc.2023.102871}
}

@article{van2023groundwater,
  title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
  author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
  journal={Journal of Hydrology},
  volume={617},
  pages={129034},
  year={2023},
  publisher={Elsevier},
  doi={https://doi.org/10.1016/j.jhydrol.2022.129034}
}

@article{ahmed2021comprehensive,
  title={A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem},
  author={Ahmed, Ali Najah and Van Lam, To and Hung, Nguyen Duy and Van Thieu, Nguyen and Kisi, Ozgur and El-Shafie, Ahmed},
  journal={Applied Soft Computing},
  volume={105},
  pages={107282},
  year={2021},
  publisher={Elsevier},
  doi={10.1016/j.asoc.2021.107282}
}

⚙ Usage

đŸ› ïž Installation

 
$ pip install mealpy --upgrade
  • Install the alpha/beta version from PyPi
$ pip install mealpy==2.5.4a6
  • Install the pre-release version directly from the source code:
$ git clone https://github.com/thieu1995/mealpy.git
$ cd mealpy
$ python setup.py install
  • In case, you want to install the development version from Github:
$ pip install git+https://github.com/thieu1995/mealpy

After installation, check the version to ensure successful installation:

$ python
>>> import mealpy
>>> mealpy.__version__

>>> print(mealpy.get_all_optimizers())
>>> model = mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100, pop_size=50)

💬 Decision Variables

Before we dive into some examples, let's briefly consider the type of problem you're aiming to solve with MEALPY. Understanding your specific problem and its desired solution can help you select the most appropriate approach.

To assist you in choosing the right tools, refer to the table below. It outlines different types of decision variables available in MEALPY, along with their syntax and common problem applications. This will guide you in defining your search space effectively.

ClassSyntaxProblem Types
FloatVarFloatVar(lb=(-10., )*7, ub=(10., )*7, name="delta")Continuous Problem
IntegerVarIntegerVar(lb=(-10., )*7, ub=(10., )*7, name="delta")LP, IP, NLP, QP, MIP
StringVarStringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")ML, AI-optimize
BinaryVarBinaryVar(n_vars=11, name="delta")Networks
BoolVarBoolVar(n_vars=11, name="delta")ML, AI-optimize
PermutationVarPermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")Combinatorial Optimization
CategoricalVarCategoricalVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta")MIP, MILP
SequenceVarSequenceVar(valid_sets=((1, ), {2, 3}, [3, 5, 1]), return_type=list, name='delta')Hyper-parameter tuning
TransferBoolVarTransferBoolVar(n_vars=11, name="delta", tf_func="sstf_02")ML, AI-optimize, Feature
TransferBinaryVarTransferBinaryVar(n_vars=11, name="delta", tf_func="vstf_04")Networks, Feature Selection

📚 Optimizer Classification Table

  • Meta-heuristic Categories: (Based on this article)
    • Evolutionary-based: Algorithms inspired by Darwin's law of natural selection and evolutionary computing principles
    • Swarm-based: Algorithms drawing inspiration from the collective movement and interaction of swarms (e.g., birds, social insects).
    • Physics-based: Algorithms derived from physical laws and phenomena (e.g., Newton's law of universal gravitation, black holes, multiverse theory).
    • Human-based: Algorithms inspired by human interactions and behaviors (e.g., queuing search, teaching-learning processes).
    • Biology-based: Algorithms based on biological creatures or microorganisms.
    • System-based: Algorithms inspired by ecological systems, immune systems, or network systems.
    • Math-based: Algorithms developed from mathematical forms or laws (e.g., sine-cosine functions).
    • Music-based: Algorithms drawing inspiration from musical instruments or compositions.

MEALPY3-0-0

  • Difficulty - Difficulty Level (Personal Opinion): Objective observation from author. Depend on the number of parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC).
    • Easy: A few paras, few equations, SLOC very short
    • Medium: more equations than Easy level, SLOC longer than Easy level
    • Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read.
    • Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read.

** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level.

GroupNameModuleClassYearParasDifficulty
EvolutionaryEvolutionary ProgrammingEPOriginalEP19643easy
Evolutionary**LevyEP*3easy
EvolutionaryEvolution StrategiesESOriginalES19713easy
Evolutionary**LevyES*3easy
Evolutionary**CMA_ES20032hard
Evolutionary**Simple_CMA_ES20232medium
EvolutionaryMemetic AlgorithmMAOriginalMA19897easy
EvolutionaryGenetic AlgorithmGABaseGA19924easy
Evolutionary**SingleGA*7easy
Evolutionary**MultiGA*7easy
Evolutionary**EliteSingleGA*10easy
Evolutionary**EliteMultiGA*10easy
EvolutionaryDifferential EvolutionDEBaseDE19975easy
Evolutionary**JADE20096medium
Evolutionary**SADE20052medium
Evolutionary**SAP_DE20063medium
EvolutionarySuccess-History Adaptation Differential EvolutionSHADEOriginalSHADE20134medium
Evolutionary**L_SHADE20144medium
EvolutionaryFlower Pollination AlgorithmFPAOriginalFPA20144medium
EvolutionaryCoral Reefs OptimizationCROOriginalCRO201411medium
Evolutionary**OCRO201912medium
*********************
SwarmParticle Swarm OptimizationPSOOriginalPSO19956easy
Swarm**PPSO20192medium
Swarm**HPSO_TVAC20174medium
Swarm**C_PSO20156medium
Swarm**CL_PSO20066medium
SwarmBacterial Foraging OptimizationBFOOriginalBFO200210hard
Swarm**ABFO20198medium
SwarmBees AlgorithmBeesAOriginalBeesA20058medium
Swarm**ProbBeesA20155medium
Swarm**CleverBookBeesA20068medium
SwarmCat Swarm OptimizationCSOOriginalCSO200611hard
SwarmArtificial Bee ColonyABCOriginalABC20078medium
SwarmAnt Colony OptimizationACOROriginalACOR20085easy
SwarmCuckoo Search AlgorithmCSAOriginalCSA20093medium
SwarmFirefly Algorithm FFAOriginalFFA20098easy
SwarmFireworks AlgorithmFAOriginalFA20107medium
SwarmBat AlgorithmBAOriginalBA20106medium
Swarm**AdaptiveBA20108medium
Swarm**ModifiedBA*5medium
SwarmFruit-fly Optimization AlgorithmFOAOriginalFOA20122easy
Swarm**BaseFOA*2easy
Swarm**WhaleFOA20202medium
SwarmSocial Spider OptimizationSSpiderOOriginalSSpiderO20184hard*
SwarmSpider Monkey OptimizationSMODevSMO20144hard
SwarmGrey Wolf OptimizerGWOOriginalGWO20142easy
Swarm**RW_GWO20192easy
Swarm**GWO_WOA20222easy
Swarm**IGWO20184easy
Swarm**ChaoticGWO20184easy
Swarm**FuzzyGWO20173medium
Swarm**IncrementalGWO20213medium
Swarm**ExGWO20212medium
Swarm**DS_GWO20224medium
Swarm**IOBL_GWO20212medium
Swarm**OGWO20214medium
Swarm**ER_GWO20205medium
Swarm**CG_GWO20222hard
SwarmSocial Spider AlgorithmSSpiderAOriginalSSpiderA20155medium
SwarmAnt Lion OptimizerALOOriginalALO20152easy
Swarm**BaseALO*2easy
SwarmMoth Flame OptimizationMFOOriginalMFO20152easy
Swarm**BaseMFO*2easy
SwarmElephant Herding OptimizationEHOOriginalEHO20155easy
SwarmJaya AlgorithmJAOriginalJA20162easy
Swarm**BaseJA*2easy
Swarm**LevyJA20212easy
SwarmWhale Optimization AlgorithmWOAOriginalWOA20162medium
Swarm**HI_WOA20193medium
SwarmDragonfly OptimizationDOOriginalDO20162medium
SwarmBird Swarm AlgorithmBSAOriginalBSA20169medium
SwarmSpotted Hyena OptimizerSHOOriginalSHO20174medium
SwarmSalp Swarm OptimizationSSOOriginalSSO20172easy
SwarmSwarm Robotics Search And RescueSRSROriginalSRSR20172hard*
SwarmGrasshopper Optimisation AlgorithmGOAOriginalGOA20174easy
SwarmCoyote Optimization AlgorithmCOAOriginalCOA20183medium
SwarmMoth Search AlgorithmMSAOriginalMSA20185easy
SwarmSquirrel Search AlgorithmSquirrelSAOriginalSquirrelSA20197medium
SwarmFitness Dependent OptimizerFDOOriginalFDO20193medium
SwarmSea Lion OptimizationSLOOriginalSLO20192medium
Swarm**ModifiedSLO*2medium
Swarm**ImprovedSLO20224medium
SwarmEmperor Penguins Colony/td> EPCDevEPC20196hard
SwarmNake Mole*Rat AlgorithmNMRAOriginalNMRA20193easy
Swarm**ImprovedNMRA*4medium
SwarmPathfinder AlgorithmPFAOriginalPFA20192medium
SwarmSailfish OptimizerSFOOriginalSFO20195easy
Swarm**ImprovedSFO*3medium
SwarmHarris Hawks OptimizationHHOOriginalHHO20192medium
SwarmManta Ray Foraging OptimizationMRFOOriginalMRFO20203medium
SwarmBald Eagle SearchBESOriginalBES20207easy
SwarmSparrow Search AlgorithmSSAOriginalSSA20205medium
Swarm**BaseSSA*5medium
SwarmHunger Games SearchHGSOriginalHGS20214medium
SwarmAquila OptimizerAOOriginalAO20212easy
Swarm**AAO20244easy
SwarmHybrid Grey Wolf * Whale Optimization AlgorithmGWOGWO_WOA20222easy
SwarmMarine Predators AlgorithmMPAOriginalMPA20202medium
SwarmHoney Badger AlgorithmHBAOriginalHBA20222easy
SwarmSand Cat Swarm OptimizationSCSOOriginalSCSO20222easy
SwarmTuna Swarm OptimizationTSOOriginalTSO20212medium
SwarmAfrican Vultures Optimization AlgorithmAVOAOriginalAVOA20227medium
SwarmArtificial Gorilla Troops OptimizationAGTOOriginalAGTO20215medium
Swarm**MGTO20233medium
SwarmArtificial Rabbits OptimizationAROOriginalARO20222easy
Swarm**LARO20222easy
Swarm**IARO20222easy
SwarmEgret Swarm Optimization AlgorithmESOAOriginalESOA20222medium
SwarmFox OptimizerFOXOriginalFOX20234easy
SwarmGolden Jackal OptimizationGJOOriginalGJO20222easy
SwarmGiant Trevally OptimizationGTOOriginalGTO20224medium
Swarm**Matlab101GTO20222medium
Swarm**Matlab102GTO20232hard
SwarmMountain Gazelle OptimizerMGOOriginalMGO20222easy
SwarmSea-Horse OptimizationSeaHOOriginalSeaHO20222medium
*********************
PhysicsSimulated AnneallingSAOriginalSA19839medium
Physics**GaussianSA*5medium
Physics**SwarmSA19879medium
PhysicsWind Driven OptimizationWDOOriginalWDO20137easy
PhysicsMulti*Verse OptimizerMVOOriginalMVO20164easy
Physics**BaseMVO*4easy
PhysicsTug of War OptimizationTWOOriginalTWO20162easy
Physics**OppoTWO*2medium
Physics**LevyTWO*2medium
Physics**EnhancedTWO20202medium
PhysicsElectromagnetic Field OptimizationEFOOriginalEFO20166easy
Physics**BaseEFO*6medium
PhysicsNuclear Reaction OptimizationNROOriginalNRO20192hard*
PhysicsHenry Gas Solubility OptimizationHGSOOriginalHGSO20193medium
PhysicsAtom Search OptimizationASOOriginalASO20194medium
PhysicsEquilibrium OptimizerEOOriginalEO20192easy
Physics**ModifiedEO20202medium
Physics**AdaptiveEO20202medium
PhysicsArchimedes Optimization AlgorithmArchOAOriginalArchOA20218medium
PhysicsChernobyl Disaster OptimizationCDOOriginalCDO20232easy
PhysicsEnergy Valley OptimizationEVOOriginalEVO20232medium
PhysicsFick's Law AlgorithmFLAOriginalFLA20238hard
PhysicsPhysical Phenomenon of RIME-iceRIMEOriginalRIME20233easy
PhysicsElectrical Storm OptimizationESOOriginalESO20252hard
*********************
HumanCulture AlgorithmCAOriginalCA19943easy
HumanImperialist Competitive AlgorithmICAOriginalICA20078hard*
HumanTeaching Learning*based OptimizationTLOOriginalTLO20112easy
Human**BaseTLO20122easy
Human**ITLO20133medium
HumanBrain Storm OptimizationBSOOriginalBSO20118medium
Human**ImprovedBSO20177medium
HumanQueuing Search AlgorithmQSAOriginalQSA20192hard
Human**BaseQSA*2hard
Human**OppoQSA*2hard
Human**LevyQSA*2hard
Human**ImprovedQSA20212hard
HumanSearch And Rescue OptimizationSAROOriginalSARO20194medium
Human**BaseSARO*4medium
HumanLife Choice*Based Optimization LCOOriginalLCO20193easy
Human**BaseLCO*3easy
Human**ImprovedLCO*2easy
HumanSocial Ski*Driver OptimizationSSDOOriginalSSDO20192easy
HumanGaining Sharing Knowledge*based AlgorithmGSKAOriginalGSKA20196medium
Human**BaseGSKA*4medium
HumanCoronavirus Herd Immunity OptimizationCHIOOriginalCHIO20204medium
Human**BaseCHIO*4medium
HumanForensic*Based Investigation OptimizationFBIOOriginalFBIO20202medium
Human**BaseFBIO*2medium
HumanBattle Royale OptimizationBROOriginalBRO20203medium
Human**BaseBRO*3medium
HumanStudent Psychology Based OptimizationSPBOOriginalSPBO20202medium
Human**DevSPBO*2medium
HumanHeap-based OptimizationHBOOriginalHBO20203medium
HumanHuman Conception OptimizationHCOOriginalHCO20226medium
HumanAli baba and the Forty ThievesAFTOriginalAFT20222easy
HumanChild Drawing Development OptimizationCDDOOriginalCDDO20224easy
HumanDwarf Mongoose Optimization AlgorithmDMOAOriginalDMOA20224medium
Human**DevDMOA*3medium
HumanWar Strategy OptimizationWarSOOriginalWarSO20223easy
*********************
BioInvasive Weed OptimizationIWOOriginalIWO20067easy
BioBiogeography*Based OptimizationBBOOriginalBBO20084easy
Bio**BaseBBO*4easy
BioVirus Colony SearchVCSOriginalVCS20164hard*
Bio**BaseVCS*4hard*
BioSatin Bowerbird OptimizerSBOOriginalSBO20175easy
Bio**BaseSBO*5easy
BioEarthworm Optimisation AlgorithmEOAOriginalEOA20188medium
BioWildebeest Herd OptimizationWHOOriginalWHO201912hard
BioSlime Mould AlgorithmSMAOriginalSMA20203easy
Bio**BaseSMA*3easy
BioBarnacles Mating OptimizerBMOOriginalBMO20183easy
BioTunicate Swarm AlgorithmTSAOriginalTSA20202easy
BioSymbiotic Organisms SearchSOSOriginalSOS20142medium
BioSeagull Optimization AlgorithmSOAOriginalSOA20193easy
Bio**DevSOA*3easy
BioBrown-Bear Optimization AlgorithmBBOAOriginalBBOA20232medium
BioTree Physiology OptimizationTPOOriginalTPO20175medium
*********************
SystemGerminal Center OptimizationGCOOriginalGCO20184medium
System**BaseGCO*4medium
SystemWater Cycle AlgorithmWCAOriginalWCA20125medium
SystemArtificial Ecosystem*based OptimizationAEOOriginalAEO20192easy
System**EnhancedAEO20202medium
System**ModifiedAEO20202medium
System**ImprovedAEO20212medium
System**AugmentedAEO20222medium
*********************
MathHill ClimbingHCOriginalHC19933easy
Math**SwarmHC*3easy
MathCross-Entropy Method CEMOriginalCEM19974easy
MathTabu SearchTSOriginalTS20045easy
MathSine Cosine AlgorithmSCAOriginalSCA20162easy
Math**BaseSCA*2easy
Math**QLE-SCA20224hard
MathGradient-Based OptimizerGBOOriginalGBO20205medium
MathArithmetic Optimization AlgorithmAOAOrginalAOA20216easy
MathChaos Game OptimizationCGOOriginalCGO20212easy
MathPareto-like Sequential SamplingPSSOriginalPSS20214medium
MathweIghted meaN oF vectOrsINFOOriginalINFO20222medium
MathRUNge Kutta optimizerRUNOriginalRUN20212hard
MathCircle Search AlgorithmCircleSAOriginalCircleSA20223easy
MathSuccess History Intelligent OptimizationSHIOOriginalSHIO20222easy
*********************
MusicHarmony SearchHSOriginalHS20014easy
Music**BaseHS*4easy
SOTAEnsemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhoodLSHADEcnEpSinOriginalLSHADEcnEpSin20179hard
SOTAImproved Multi-operator Differential Evolution AlgorithmIMODEOriginalIMODE20204hard

❌ Warning: Algorithms Suspected of Plagiarism

During our implementation and classification of metaheuristic optimization algorithms, we identified a set of methods that raise serious concerns regarding scientific integrity and originality. These algorithms are typically published under different names, but they appear to share:

  • The same core mathematical models, equations, and update rules.
  • Only superficial changes in naming, metaphors, or biological analogies.
  • Publications authored by the same or overlapping research groups.
  • Heavy criticism on public academic forums such as PubPeer, where many of these papers are flagged for self-plagiarism, redundant publication, or lack of novelty.
  • Some of these papers may be withdrawn or retracted in the future, as investigations unfold.

For these reasons, we strongly advise the exclusion of the following algorithms from scientific benchmarking, comparative studies, or any applications unless their originality is transparently validated.

I have personally implemented these algorithms, which is why I can confidently say that they are nearly identical and likely cases of plagiarism. For this reason, I will no longer spend time coding such algorithms in the future. This warning is intended to help others avoid using or relying on these methods in their work.

GroupNameModuleClassYearParasDifficulty
SwarmCoati Optimization AlgorithmCoatiOAOriginalCoatiOA20232easy
SwarmFennec For OptimizationFFOOriginalFFO20222easy
SwarmNorthern Goshawk OptimizationNGOOriginalNGO20212easy
SwarmOsprey Optimization AlgorithmOOAOriginalOOA20232easy
SwarmPelican Optimization Algorithm POAOriginalPOA20232easy
SwarmServal Optimization AlgorithmServalOAOriginalServalOA20222easy
SwarmSiberian Tiger OptimizationSTOOriginalSTO20222easy
SwarmTasmanian Devil OptimizationTDOOriginalTDO20222easy
SwarmWalrus Optimization AlgorithmWaOAOriginalWaOA20222easy
SwarmZebra Optimization Algorithm ZOAOriginalZOA20222easy
HumanTeamwork Optimization AlgorithmTOAOriginalTOA20212easy

⚠ Ethical Reminder

Researchers and students are urged to exercise caution when referencing or applying the algorithms listed above. Using unoriginal or unethical work can compromise the scientific credibility of any downstream research and introduce misleading experimental results.

🔗 Check PubPeer1 and PubPeer2 to find ongoing discussions and critiques from the academic community.

đŸ’» Define All Optimizers

 

from mealpy import (StringVar, FloatVar, BoolVar, PermutationVar, CategoricalVar, IntegerVar, BinaryVar,
                    TransferBinaryVar, TransferBoolVar)
from mealpy import Tuner, Multitask, Problem, Optimizer, Termination, ParameterGrid
from mealpy import get_all_optimizers, get_optimizer_by_name

from mealpy import BBO, PSO, GA, ALO, AO, ARO, AVOA, BA, BBOA, BMO, EOA, IWO
from mealpy import GJO, FOX, FOA, FFO, FFA, FA, ESOA, EHO, DO, DMOA, CSO, CSA, CoatiOA, COA, BSA
from mealpy import HCO, ICA, LCO, WarSO, TOA, TLO, SSDO, SPBO, SARO, QSA, ArchOA, ASO, CDO, EFO, EO, EVO, FLA
from mealpy import HGSO, MVO, NRO, RIME, SA, WDO, TWO, ABC, ACOR, AGTO, BeesA, BES, BFO, ZOA, WOA, WaOA, TSO
from mealpy import PFA, OOA, NGO, NMRA, MSA, MRFO, MPA, MGO, MFO, JA, HHO, HGS, HBA, GWO, GTO, GOA
from mealpy import SBO, SMA, SOA, SOS, TPO, TSA, VCS, WHO, AOA, CEM, CGO, CircleSA, GBO, HC, INFO, PSS, RUN, SCA
from mealpy import SHIO, TS, HS, AEO, GCO, WCA, CRO, DE, EP, ES, FPA, MA, SHADE, BRO, BSO, CA, CHIO, FBIO, GSKA, HBO
from mealpy import TDO, STO, SSpiderO, SSpiderA, SSO, SSA, SRSR, SLO, SHO, SFO, ServalOA, SeaHO, SCSO, POA

## Newly added module in version 3.0.3
from mealpy import ESO, EPC, SMO, AFT, CDDO, SquirrelSA, FDO, LSHADEcnEpSin, IMODE


if __name__ == "__main__":
    model = BBO.OriginalBBO(epoch=10, pop_size=30, p_m=0.01, n_elites=2)
    model = PSO.OriginalPSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w=0.4)
    model = PSO.LDW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w_min=0.4, w_max=0.9)
    model = PSO.AIW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, alpha=0.4)
    model = PSO.P_PSO(epoch=100, pop_size=50)
    model = PSO.HPSO_TVAC(epoch=100, pop_size=50, ci=0.5, cf=0.1)
    model = PSO.C_PSO(epoch=100, pop_size=50, c1=2.05, c2=2.05, w_min=0.4, w_max=0.9)
    model = PSO.CL_PSO(epoch=100, pop_size=50, c_local=1.2, w_min=0.4, w_max=0.9, max_flag=7)
    model = GA.BaseGA(epoch=100, pop_size=50, pc=0.9, pm=0.05, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap")
    model = GA.SingleGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap")
    model = GA.MultiGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap")
    model = GA.EliteSingleGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1,
                             elite_worst=0.3, strategy=0)
    model = GA.EliteMultiGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1,
                            elite_worst=0.3, strategy=0)
    model = ABC.OriginalABC(epoch=1000, pop_size=50, n_limits=50)
    model = ACOR.OriginalACOR(epoch=1000, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0)
    model = AGTO.OriginalAGTO(epoch=1000, pop_size=50, p1=0.03, p2=0.8, beta=3.0)
    model = AGTO.MGTO(epoch=1000, pop_size=50, pp=0.03)
    model = ALO.OriginalALO(epoch=100, pop_size=50)
    model = ALO.DevALO(epoch=100, pop_size=50)
    model = AO.OriginalAO(epoch=100, pop_size=50)
    model = ARO.OriginalARO(epoch=100, pop_size=50)
    model = ARO.LARO(epoch=100, pop_size=50)
    model = ARO.IARO(epoch=100, pop_size=50)
    model = AVOA.OriginalAVOA(epoch=100, pop_size=50, p1=0.6, p2=0.4, p3=0.6, alpha=0.8, gama=2.5)
    model = BA.OriginalBA(epoch=100, pop_size=50, loudness=0.8, pulse_rate=0.95, pf_min=0.1, pf_max=10.0)
    model = BA.AdaptiveBA(epoch=100, pop_size=50, loudness_min=1.0, loudness_max=2.0, pr_min=-2.5, pr_max=0.85, pf_min=0.1, pf_max=10.)
    model = BA.DevBA(epoch=100, pop_size=50, pulse_rate=0.95, pf_min=0., pf_max=10.)
    model = BBOA.OriginalBBOA(epoch=100, pop_size=50)
    model = BMO.OriginalBMO(epoch=100, pop_size=50, pl=4)
    model = EOA.OriginalEOA(epoch=100, pop_size=50, p_c=0.9, p_m=0.01, n_best=2, alpha=0.98, beta=0.9, gama=0.9)
    model = IWO.OriginalIWO(epoch=100, pop_size=50, seed_min=3, seed_max=9, exponent=3, sigma_start=0.6, sigma_end=0.01)
    model = SBO.DevSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02)
    model = SBO.OriginalSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02)
    model = SMA.OriginalSMA(epoch=100, pop_size=50, p_t=0.03)
    model = SMA.DevSMA(epoch=100, pop_size=50, p_t=0.03)
    model = SOA.OriginalSOA(epoch=100, pop_size=50, fc=2)
    model = SOA.DevSOA(epoch=100, pop_size=50, fc=2)
    model = SOS.OriginalSOS(epoch=100, pop_size=50)
    model = TPO.DevTPO(epoch=100, pop_size=50, alpha=0.3, beta=50., theta=0.9)
    model = TSA.OriginalTSA(epoch=100, pop_size=50)
    model = VCS.OriginalVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3)
    model = VCS.DevVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3)
    model = WHO.OriginalWHO(epoch=100, pop_size=50, n_explore_step=3, n_exploit_step=3, eta=0.15, p_hi=0.9, local_alpha=0.9, local_beta=0.3, global_alpha=0.2,
                            global_beta=0.8, delta_w=2.0, delta_c=2.0)
    model = AOA.OriginalAOA(epoch=100, pop_size=50, alpha=5, miu=0.5, moa_min=0.2, moa_max=0.9)
    model = CEM.OriginalCEM(epoch=100, pop_size=50, n_best=20, alpha=0.7)
    model = CGO.OriginalCGO(epoch=100, pop_size=50)
    model = CircleSA.OriginalCircleSA(epoch=100, pop_size=50, c_factor=0.8)
    model = GBO.OriginalGBO(epoch=100, pop_size=50, pr=0.5, beta_min=0.2, beta_max=1.2)
    model = HC.OriginalHC(epoch=100, pop_size=50, neighbour_size=50)
    model = HC.SwarmHC(epoch=100, pop_size=50, neighbour_size=10)
    model = INFO.OriginalINFO(epoch=100, pop_size=50)
    model = PSS.OriginalPSS(epoch=100, pop_size=50, acceptance_rate=0.8, sampling_method="LHS")
    model = RUN.OriginalRUN(epoch=100, pop_size=50)
    model = SCA.OriginalSCA(epoch=100, pop_size=50)
    model = SCA.DevSCA(epoch=100, pop_size=50)
    model = SCA.QleSCA(epoch=100, pop_size=50, alpha=0.1, gama=0.9)
    model = SHIO.OriginalSHIO(epoch=100, pop_size=50)
    model = TS.OriginalTS(epoch=100, pop_size=50, tabu_size=5, neighbour_size=20, perturbation_scale=0.05)
    model = HS.OriginalHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05)
    model = HS.DevHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05)
    model = AEO.OriginalAEO(epoch=100, pop_size=50)
    model = AEO.EnhancedAEO(epoch=100, pop_size=50)
    model = AEO.ModifiedAEO(epoch=100, pop_size=50)
    model = AEO.ImprovedAEO(epoch=100, pop_size=50)
    model = AEO.AugmentedAEO(epoch=100, pop_size=50)
    model = GCO.OriginalGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25)
    model = GCO.DevGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25)
    model = WCA.OriginalWCA(epoch=100, pop_size=50, nsr=4, wc=2.0, dmax=1e-6)
    model = CRO.OriginalCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5)
    model = CRO.OCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5, restart_count=50)
    model = DE.OriginalDE(epoch=100, pop_size=50, wf=0.7, cr=0.9, strategy=0)
    model = DE.JADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5, pt=0.1, ap=0.1)
    model = DE.SADE(epoch=100, pop_size=50)
    model = DE.SAP_DE(epoch=100, pop_size=50, branch="ABS")
    model = EP.OriginalEP(epoch=100, pop_size=50, bout_size=0.05)
    model = EP.LevyEP(epoch=100, pop_size=50, bout_size=0.05)
    model = ES.OriginalES(epoch=100, pop_size=50, lamda=0.75)
    model = ES.LevyES(epoch=100, pop_size=50, lamda=0.75)
    model = ES.CMA_ES(epoch=100, pop_size=50)
    model = ES.Simple_CMA_ES(epoch=100, pop_size=50)
    model = FPA.OriginalFPA(epoch=100, pop_size=50, p_s=0.8, levy_multiplier=0.2)
    model = MA.OriginalMA(epoch=100, pop_size=50, pc=0.85, pm=0.15, p_local=0.5, max_local_gens=10, bits_per_param=4)
    model = SHADE.OriginalSHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5)
    model = SHADE.L_SHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5)
    model = BRO.OriginalBRO(epoch=100, pop_size=50, threshold=3)
    model = BRO.DevBRO(epoch=100, pop_size=50, threshold=3)
    model = BSO.OriginalBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.2, p2=0.8, p3=0.4, p4=0.5, slope=20)
    model = BSO.ImprovedBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.25, p2=0.5, p3=0.75, p4=0.6)
    model = CA.OriginalCA(epoch=100, pop_size=50, accepted_rate=0.15)
    model = CHIO.OriginalCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10)
    model = CHIO.DevCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10)
    model = FBIO.OriginalFBIO(epoch=100, pop_size=50)
    model = FBIO.DevFBIO(epoch=100, pop_size=50)
    model = GSKA.OriginalGSKA(epoch=100, pop_size=50, pb=0.1, kf=0.5, kr=0.9, kg=5)
    model = GSKA.DevGSKA(epoch=100, pop_size=50, pb=0.1, kr=0.9)
    model = HBO.OriginalHBO(epoch=100, pop_size=50, degree=3)
    model = HCO.OriginalHCO(epoch=100, pop_size=50, wfp=0.65, wfv=0.05, c1=1.4, c2=1.4)
    model = ICA.OriginalICA(epoch=100, pop_size=50, empire_count=5, assimilation_coeff=1.5, revolution_prob=0.05, revolution_rate=0.1, revolution_step_size=0.1,
                            zeta=0.1)
    model = LCO.OriginalLCO(epoch=100, pop_size=50, r1=2.35)
    model = LCO.ImprovedLCO(epoch=100, pop_size=50)
    model = LCO.DevLCO(epoch=100, pop_size=50, r1=2.35)
    model = WarSO.OriginalWarSO(epoch=100, pop_size=50, rr=0.1)
    model = TOA.OriginalTOA(epoch=100, pop_size=50)
    model = TLO.OriginalTLO(epoch=100, pop_size=50)
    model = TLO.ImprovedTLO(epoch=100, pop_size=50, n_teachers=5)
    model = TLO.DevTLO(epoch=100, pop_size=50)
    model = SSDO.OriginalSSDO(epoch=100, pop_size=50)
    model = SPBO.OriginalSPBO(epoch=100, pop_size=50)
    model = SPBO.DevSPBO(epoch=100, pop_size=50)
    model = SARO.OriginalSARO(epoch=100, pop_size=50, se=0.5, mu=50)
    model = SARO.DevSARO(epoch=100, pop_size=50, se=0.5, mu=50)
    model = QSA.OriginalQSA(epoch=100, pop_size=50)
    model = QSA.DevQSA(epoch=100, pop_size=50)
    model = QSA.OppoQSA(epoch=100, pop_size=50)
    model = QSA.LevyQSA(epoch=100, pop_size=50)
    model = QSA.ImprovedQSA(epoch=100, pop_size=50)
    model = ArchOA.OriginalArchOA(epoch=100, pop_size=50, c1=2, c2=5, c3=2, c4=0.5, acc_max=0.9, acc_min=0.1)
    model = ASO.OriginalASO(epoch=100, pop_size=50, alpha=50, beta=0.2)
    model = CDO.OriginalCDO(epoch=100, pop_size=50)
    model = EFO.OriginalEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45)
    model = EFO.DevEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45)
    model = EO.OriginalEO(epoch=100, pop_size=50)
    model = EO.AdaptiveEO(epoch=100, pop_size=50)
    model = EO.ModifiedEO(epoch=100, pop_size=50)
    model = EVO.OriginalEVO(epoch=100, pop_size=50)
    model = FLA.OriginalFLA(epoch=100, pop_size=50, C1=0.5, C2=2.0, C3=0.1, C4=0.2, C5=2.0, DD=0.01)
    model = HGSO.OriginalHGSO(epoch=100, pop_size=50, n_clusters=3)
    model = MVO.OriginalMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0)
    model = MVO.DevMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0)
    model = NRO.OriginalNRO(epoch=100, pop_size=50)
    model = RIME.OriginalRIME(epoch=100, pop_size=50, sr=5.0)
    model = SA.OriginalSA(epoch=100, pop_size=50, temp_init=100, step_size=0.1)
    model = SA.GaussianSA(epoch=100, pop_size=50, temp_init=100, cooling_rate=0.99, scale=0.1)
    model = SA.SwarmSA(epoch=100, pop_size=50, max_sub_iter=5, t0=1000, t1=1, move_count=5, mutation_rate=0.1, mutation_step_size=0.1,
                       mutation_step_size_damp=0.99)
    model = WDO.OriginalWDO(epoch=100, pop_size=50, RT=3, g_c=0.2, alp=0.4, c_e=0.4, max_v=0.3)
    model = TWO.OriginalTWO(epoch=100, pop_size=50)
    model = TWO.EnhancedTWO(epoch=100, pop_size=50)
    model = TWO.OppoTWO(epoch=100, pop_size=50)
    model = TWO.LevyTWO(epoch=100, pop_size=50)
    model = ABC.OriginalABC(epoch=100, pop_size=50, n_limits=50)
    model = ACOR.OriginalACOR(epoch=100, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0)
    model = AGTO.OriginalAGTO(epoch=100, pop_size=50, p1=0.03, p2=0.8, beta=3.0)
    model = AGTO.MGTO(epoch=100, pop_size=50, pp=0.03)
    model = BeesA.OriginalBeesA(epoch=100, pop_size=50, selected_site_ratio=0.5, elite_site_ratio=0.4, selected_site_bee_ratio=0.1, elite_site_bee_ratio=2.0,
                                dance_radius=0.1, dance_reduction=0.99)
    model = BeesA.CleverBookBeesA(epoch=100, pop_size=50, n_elites=16, n_others=4, patch_size=5.0, patch_reduction=0.985, n_sites=3, n_elite_sites=1)
    model = BeesA.ProbBeesA(epoch=100, pop_size=50, recruited_bee_ratio=0.1, dance_radius=0.1, dance_reduction=0.99)
    model = BES.OriginalBES(epoch=100, pop_size=50, a_factor=10, R_factor=1.5, alpha=2.0, c1=2.0, c2=2.0)
    model = BFO.OriginalBFO(epoch=100, pop_size=50, Ci=0.01, Ped=0.25, Nc=5, Ns=4, d_attract=0.1, w_attract=0.2, h_repels=0.1, w_repels=10)
    model = BFO.ABFO(epoch=100, pop_size=50, C_s=0.1, C_e=0.001, Ped=0.01, Ns=4, N_adapt=2, N_split=40)
    model = ZOA.OriginalZOA(epoch=100, pop_size=50)
    model = WOA.OriginalWOA(epoch=100, pop_size=50)
    model = WOA.HI_WOA(epoch=100, pop_size=50, feedback_max=10)
    model = WaOA.OriginalWaOA(epoch=100, pop_size=50)
    model = TSO.OriginalTSO(epoch=100, pop_size=50)
    model = TDO.OriginalTDO(epoch=100, pop_size=50)
    model = STO.OriginalSTO(epoch=100, pop_size=50)
    model = SSpiderO.OriginalSSpiderO(epoch=100, pop_size=50, fp_min=0.65, fp_max=0.9)
    model = SSpiderA.OriginalSSpiderA(epoch=100, pop_size=50, r_a=1.0, p_c=0.7, p_m=0.1)
    model = SSO.OriginalSSO(epoch=100, pop_size=50)
    model = SSA.OriginalSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1)
    model = SSA.DevSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1)
    model = SRSR.OriginalSRSR(epoch=100, pop_size=50)
    model = SLO.OriginalSLO(epoch=100, pop_size=50)
    model = SLO.ModifiedSLO(epoch=100, pop_size=50)
    model = SLO.ImprovedSLO(epoch=100, pop_size=50, c1=1.2, c2=1.5)
    model = SHO.OriginalSHO(epoch=100, pop_size=50, h_factor=5.0, n_trials=10)
    model = SFO.OriginalSFO(epoch=100, pop_size=50, pp=0.1, AP=4.0, epsilon=0.0001)
    model = SFO.ImprovedSFO(epoch=100, pop_size=50, pp=0.1)
    model = ServalOA.OriginalServalOA(epoch=100, pop_size=50)
    model = SeaHO.OriginalSeaHO(epoch=100, pop_size=50)
    model = SCSO.OriginalSCSO(epoch=100, pop_size=50)
    model = POA.OriginalPOA(epoch=100, pop_size=50)
    model = PFA.OriginalPFA(epoch=100, pop_size=50)
    model = OOA.OriginalOOA(epoch=100, pop_size=50)
    model = NGO.OriginalNGO(epoch=100, pop_size=50)
    model = NMRA.OriginalNMRA(epoch=100, pop_size=50, pb=0.75)
    model = NMRA.ImprovedNMRA(epoch=100, pop_size=50, pb=0.75, pm=0.01)
    model = MSA.OriginalMSA(epoch=100, pop_size=50, n_best=5, partition=0.5, max_step_size=1.0)
    model = MRFO.OriginalMRFO(epoch=100, pop_size=50, somersault_range=2.0)
    model = MRFO.WMQIMRFO(epoch=100, pop_size=50, somersault_range=2.0, pm=0.5)
    model = MPA.OriginalMPA(epoch=100, pop_size=50)
    model = MGO.OriginalMGO(epoch=100, pop_size=50)
    model = MFO.OriginalMFO(epoch=100, pop_size=50)
    model = JA.OriginalJA(epoch=100, pop_size=50)
    model = JA.LevyJA(epoch=100, pop_size=50)
    model = JA.DevJA(epoch=100, pop_size=50)
    model = HHO.OriginalHHO(epoch=100, pop_size=50)
    model = HGS.OriginalHGS(epoch=100, pop_size=50, PUP=0.08, LH=10000)
    model = HBA.OriginalHBA(epoch=100, pop_size=50)
    model = GWO.OriginalGWO(epoch=100, pop_size=50)
    model = GWO.RW_GWO(epoch=100, pop_size=50)
    model = GTO.OriginalGTO(epoch=100, pop_size=50, A=0.4, H=2.0)
    model = GTO.Matlab101GTO(epoch=100, pop_size=50)
    model = GTO.Matlab102GTO(epoch=100, pop_size=50)
    model = GOA.OriginalGOA(epoch=100, pop_size=50, c_min=0.00004, c_max=1.0)
    model = GJO.OriginalGJO(epoch=100, pop_size=50)
    model = FOX.OriginalFOX(epoch=100, pop_size=50, c1=0.18, c2=0.82)
    model = FOA.OriginalFOA(epoch=100, pop_size=50)
    model = FOA.WhaleFOA(epoch=100, pop_size=50)
    model = FOA.DevFOA(epoch=100, pop_size=50)
    model = FFO.OriginalFFO(epoch=100, pop_size=50)
    model = FFA.OriginalFFA(epoch=100, pop_size=50, gamma=0.001, beta_base=2, alpha=0.2, alpha_damp=0.99, delta=0.05, exponent=2)
    model = FA.OriginalFA(epoch=100, pop_size=50, max_sparks=50, p_a=0.04, p_b=0.8, max_ea=40, m_sparks=50)
    model = ESOA.OriginalESOA(epoch=100, pop_size=50)
    model = EHO.OriginalEHO(epoch=100, pop_size=50, alpha=0.5, beta=0.5, n_clans=5)
    model = DO.OriginalDO(epoch=100, pop_size=50)
    model = DMOA.OriginalDMOA(epoch=100, pop_size=50, n_baby_sitter=3, peep=2)
    model = DMOA.DevDMOA(epoch=100, pop_size=50, peep=2)
    model = CSO.OriginalCSO(epoch=100, pop_size=50, mixture_ratio=0.15, smp=5, spc=False, cdc=0.8, srd=0.15, c1=0.4, w_min=0.4, w_max=0.9)
    model = CSA.OriginalCSA(epoch=100, pop_size=50, p_a=0.3)
    model = CoatiOA.OriginalCoatiOA(epoch=100, pop_size=50)
    model = COA.OriginalCOA(epoch=100, pop_size=50, n_coyotes=5)
    model = BSA.OriginalBSA(epoch=100, pop_size=50, ff=10, pff=0.8, c1=1.5, c2=1.5, a1=1.0, a2=1.0, fc=0.5)

    ## Newly added algorithms in version 3.0.3
    model = GWO.GWO_WOA(epoch=100, pop_size=50)
    model = GWO.IGWO(epoch=1000, pop_size=50, a_min = 0.02, a_max = 2.2)
    model = GWO.ChaoticGWO(epoch=1000, pop_size=50, chaotic_name="chebyshev", initial_chaotic_value=0.7)
    model = GWO.FuzzyGWO(epoch=1000, pop_size=50, fuzzy_name="increase")
    model = GWO.IncrementalGWO(epoch=1000, pop_size=50, explore_factor=1.5)
    model = GWO.ExGWO(epoch=1000, pop_size=50)
    model = GWO.DS_GWO(epoch=1000, pop_size=50, explore_ratio=0.4, n_groups=5)
    model = GWO.IOBL_GWO(epoch=1000, pop_size=50)
    model = GWO.OGWO(epoch=1000, pop_size=50, miu_factor=2.0, jumping_rate=0.05)
    model = GWO.ER_GWO(epoch=1000, pop_size=50, a_initial=2.0, a_final=0.0, miu_factor=1.0001)
    model = GWO.CG_GWO(epoch=1000, pop_size=50)
    
    model = ESO.OriginalESO(epoch=1000, pop_size=50)
    model = AO.AAO(epoch=1000, pop_size=50, sharpness=10.0, sigmoid_midpoint=0.5)
    model = EPC.DevEPC(epoch=1000, pop_size=50, heat_damping_factor=0.95, mutation_factor=0.1, spiral_a=1.0, spiral_b=0.5) 
    model = SMO.DevSMO(epoch=1000, pop_size=50, max_groups = 5, perturbation_rate = 0.7)
    model = SquirrelSA.OriginalSquirrelSA(epoch=1000, pop_size=50, n_food_sources=4, predator_prob=0.1, gliding_constant=1.9, scaling_factor=18, beta=1.5)
    model = AFT.OriginalAFT(epoch=1000, pop_size=50)
    model = CDDO.OriginalCDDO(epoch=1000, pop_size=50, pattern_size=10, creativity_rate=0.1)
    model = FDO.OriginalFDO(epoch=1000, pop_size=50, weight_factor=0.1)
    model = LSHADEcnEpSin.OriginalLSHADEcnEpSin(epoch=1000, pop_size=50, miu_f = 0.5, miu_cr = 0.5, freq = 0.5, memory_size = 5, ps = 0.5, pc = 0.4, pop_size_min = 10)
    model = IMODE.OriginalIMODE(epoch=1000, pop_size=50, memory_size=5, archive_size=20)

✅ Examples

Simple Benchmark Function

MEALPY allows you to define your optimization problem in a couple of ways.

1. Define Problem as a Dictionary

You can quickly define your problem using a Python dictionary. However, this approach is only valid for problems with float decision variables.

from mealpy import FloatVar, SMA
import numpy as np

def objective_function(solution):
    return np.sum(solution**2)

problem = {
    "obj_func": objective_function,
    "bounds": FloatVar(lb=(-100., )*30, ub=(100., )*30),
    "minmax": "min",
    "log_to": "console",
}

## Run the algorithm
model = SMA.OriginalSMA(epoch=100, pop_size=50, pr=0.03)
g_best = model.solve(problem)
print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}")

2. Define a Custom Problem Class

For more complex scenarios, especially when your decision variables are not exclusively FloatVar, we recommend defining a custom class that inherits from the Problem class. Let's demonstrate this with a simple "Squared" class.

In the __init__ method of your custom Problem class (e.g., Squared class), you must set the bounds and minmax attributes of the problem.

  • bounds: Defines the search space and the type of decision variables (e.g., FloatVar, IntegerVar).

  • minmax: A string indicating whether the problem is a minimization ("min") or maximization ("max") problem.

After defining the initialization, you must override the abstract method obj_func(). This method is the core of your problem definition:

  • It takes a single parameter: solution (the encoded solution vector generated by the optimizer).

  • It must return the objective function value (or fitness) for the given solution.

The resulting code structure for a custom problem class would look similar to the snippet below. You can include any additional parameters you need in your custom class (like 'data' or 'name' in this example).

from mealpy import Problem, FloatVar, BBO 
import numpy as np

# Our custom problem class
class Squared(Problem):
    def __init__(self, bounds=None, minmax="min", data=None, **kwargs):
        super().__init__(bounds, minmax, **kwargs)
        self.data = data     # This is additional variable use for passing data to objective function

    def obj_func(self, solution):
        return np.sum(solution ** 2)

    
## Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. 
bound = FloatVar(lb=(-10., )*20, ub=(10., )*20, name="my_var")      # The `name` of variable is important when decoding.
problem = Squared(bounds=bound, minmax="min", name="Squared", data="Amazing")
model = BBO.OriginalBBO(epoch=100, pop_size=20)
g_best = model.solve(problem)

## Show some attributes
print(g_best.solution)
print(g_best.target.fitness)
print(g_best.target.objectives)
print(g_best)
print(model.get_parameters())
print(model.get_name())
print(model.get_attributes()["g_best"])
print(model.problem.get_name())
print(model.problem.n_dims)
print(model.problem.bounds)
print(model.problem.lb)
print(model.problem.ub)

We provide many examples for complicated applications that can use Mealpy to solve.

🚀 Mealpy Applications

MEALPY is a versatile library capable of solving a wide array of complex optimization problems across various domains. Below are examples showcasing its diverse applications.

1. General Optimization Problems

These examples demonstrate MEALPY's use in common optimization scenarios.

  • Large-Scale Optimization example
  • Distributed Optimization / Parallelization Optimization example
  • Constrained Benchmark Function example
  • Multi-objective Benchmark Function example

2. Machine Learning & AI Optimization

MEALPY can be effectively used to optimize various aspects of Machine Learning and AI models.

  • Optimize Machine Learning Model (SVM) Hyperparameters example
  • Optimize Linear Regression Model with Pytorch: example

3. Combinatorial Optimization Problems

MEALPY excels at solving complex combinatorial problems, which involve finding an optimal object from a finite set of objects.

  • Traveling Salesman Problem (TSP) example
  • Job Shop Scheduling Problem example
  • Shortest Path Problem example
  • Location Optimization example
  • Supply Chain Optimization example
  • Healthcare Workflow Optimization Problem example
  • Production Optimization Problem example
  • Employee Rostering Problem example
  • Maintenance Scheduling example
  • Cloud task scheduling example

4. Advanced Integration Examples

MEALPY's flexibility allows for integration into more specialized systems and workflows.

MEALPY + Neural Networks (Replacing Gradient Descent)

  • Time-series Problem:
    • Traditional MLP Link
    • Hybrid code (Mealpy + MLP): Link
  • Classification Problem:
    • Traditional MLP Link
    • Hybrid code (Mealpy + MLP): Link

MEALPY + Neural Network (Optimize Neural Network Hyper-parameter)

Code: Link

5. Dedicated Utility Classes

MEALPY includes specialized classes to streamline common optimization tasks.

  • Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning) example
  • Multitask class (Multitask solver) example
  • Visualization Tutorials

6. External Projects & More Examples

Explore additional advanced examples and dedicated projects showcasing MEALPY's capabilities.

  • Travelling Salesman Problem: link
  • Feature selection problem: link

For more usage examples please look at examples folder. More advanced examples can also be found in the Mealpy-examples repository.

7. Tutorial Videos & Resources

All tutorial videos: Link

All code examples: Link

All visualization examples: Link

📚 Documents

📎 Official channels

🌟 MEALPY ecosystem

References

A

  • ABC - Artificial Bee Colony

    • OriginalABC: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.

  • ACOR - Ant Colony Optimization.

    • OriginalACOR: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173.

  • ALO - Ant Lion Optimizer

    • OriginalALO: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: 10.1016/j.advengsoft.2015.01.010
    • BaseALO: The developed version

  • AEO - Artificial Ecosystem-based Optimization

    • OriginalAEO: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43.
    • AugmentedAEO: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034.
    • ImprovedAEO: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy.
    • EnhancedAEO: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513.
    • ModifiedAEO: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909.

  • ASO - Atom Search Optimization

    • OriginalASO: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304.

  • ArchOA - Archimedes Optimization Algorithm

    • OriginalArchOA: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551.

  • AOA - Arithmetic Optimization Algorithm

    • OriginalAOA: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.

  • AO - Aquila Optimizer

    • OriginalAO: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250.
    • AAO: Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., Ragab, M. G., Alqushaibi, A., & Sumiea, E. H. (2024). Smart grid stability prediction using adaptive aquila optimizer and ensemble stacked bilstm. Results in Engineering, 24, 103261.

  • AVOA - African Vultures Optimization Algorithm

    • OriginalAVOA: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.

  • AGTO - Artificial Gorilla Troops Optimization

    • OriginalAGTO: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958.

  • ARO - Artificial Rabbits Optimization:

    • OriginalARO: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.

  • AFT - Ali baba and the Forty Thieves:

    • OriginalAFT: Braik, M., Ryalat, M. H., & Al-Zoubi, H. (2022). A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves. Neural Computing and Applications, 34(1), 409-455.

B

  • BFO - Bacterial Foraging Optimization

    • OriginalBFO: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67.
    • ABFO: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.

  • BeesA - Bees Algorithm

    • OriginalBeesA: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.
    • ProbBeesA: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.

  • BBO - Biogeography-Based Optimization

    • OriginalBBO: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.
    • BaseBBO: The developed version

  • BA - Bat Algorithm

    • OriginalBA: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.
    • AdaptiveBA: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg.
    • ModifiedBA: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46.

  • BSO - Brain Storm Optimization

    • OriginalBSO: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg.
    • ImprovedBSO: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44.

  • BSA - Bird Swarm Algorithm

    • OriginalBSA: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687.

  • BMO - Barnacles Mating Optimizer:

    • OriginalBMO: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE.

  • BES - Bald Eagle Search

    • OriginalBES: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28.

  • BRO - Battle Royale Optimization

    • OriginalBRO: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19.
    • BaseBRO: The developed version

C

  • CA - Culture Algorithm

    • OriginalCA: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific.

  • CEM - Cross Entropy Method

    • OriginalCEM: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190.

  • CSO - Cat Swarm Optimization

    • OriginalCSO: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.

  • CSA - Cuckoo Search Algorithm

    • OriginalCSA: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via LĂ©vy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.

  • CRO - Coral Reefs Optimization

    • OriginalCRO: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-LĂłpez, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.
    • OCRO: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.

  • COA - Coyote Optimization Algorithm

    • OriginalCOA: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.

  • CHIO - Coronavirus Herd Immunity Optimization

    • OriginalCHIO: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042.
    • BaseCHIO: The developed version

  • CGO - Chaos Game Optimization

    • OriginalCGO: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004.

  • CSA - Circle Search Algorithm

    • OriginalCSA: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-VĂ©liz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626.

  • CDDO - Child Drawing Development Optimization

    • OriginalCDDO: Abdulhameed, S., Rashid, T.A. Child Drawing Development Optimization Algorithm Based on Child’s Cognitive Development. Arab J Sci Eng 47, 1337–1351 (2022). https://doi.org/10.1007/s13369-021-05928-6

D

  • DE - Differential Evolution

    • BaseDE: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
    • JADE: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958.
    • SADE: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE.
    • SHADE: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE.
    • L_SHADE: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE.
    • SAP_DE: Teo, J. (2006). Exploring dynamic cls-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686.

  • DSA - Differential Search Algorithm (not done)

    • BaseDSA: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.

  • DO - Dragonfly Optimization

    • OriginalDO: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.

  • DMOA - Dwarf Mongoose Optimization Algorithm

    • OriginalDMOA: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570.
    • DevDMOA: The developed version

E

  • ES - Evolution Strategies .

    • OriginalES: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167.
    • LevyES: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006.

  • EP - Evolutionary programming .

    • OriginalEP: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life.
    • LevyEP: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lĂ©vy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE.

  • EHO - Elephant Herding Optimization .

    • OriginalEHO: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE.

  • EFO - Electromagnetic Field Optimization .

    • OriginalEFO:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22.
    • BaseEFO: The developed version

  • EOA - Earthworm Optimisation Algorithm .

    • OriginalEOA: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22.

  • EO - Equilibrium Optimizer .

    • OriginalEO: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems.
    • ModifiedEO: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542.
    • AdaptiveEO: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836.

  • ESO - Electrical Storm Optimization .

    • OriginalESO: Soto Calvo, M., & Lee, H. S. (2025). Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems. Machine Learning and Knowledge Extraction, 7(1), 24. https://doi.org/10.3390/make7010024

  • EPC - Emperor Penguins Colony .

    • DevEPC: Harifi, S., Khalilian, M., Mohammadzadeh, J. and Ebrahimnejad, S., 2019. Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evolutionary intelligence, 12(2), pp.211-226.

F

  • FFA - Firefly Algorithm

    • OriginalFFA: Ɓukasik, S., & Ć»ak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg.

  • FA - Fireworks algorithm

    • OriginalFA: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg.

  • FPA - Flower Pollination Algorithm

    • OriginalFPA: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.

  • FOA - Fruit-fly Optimization Algorithm

    • OriginalFOA: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74.
    • BaseFOA: The developed version
    • WhaleFOA: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502.

  • FDO - Fitness Dependent Optimizer

    • OriginalFDO: Abdullah, J. M., & Ahmed, T. (2019). Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEe Access, 7, 43473-43486.

  • FBIO - Forensic-Based Investigation Optimization

    • OriginalFBIO: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339.
    • BaseFBIO: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992.

  • FHO - Fire Hawk Optimization

    • OriginalFHO: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77.

G

  • GA - Genetic Algorithm

    • BaseGA: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
    • SingleGA: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299.
    • MultiGA: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26.
    • EliteSingleGA: Elite version of Single-point mutation GA
    • EliteMultiGA: Elite version of Multiple-point mutation GA

  • GWO - Grey Wolf Optimizer

    • OriginalGWO: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
    • RW_GWO: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112.
    • GWO_WOA: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003.
    • IGWO: Kaveh, A. & Zakian, P.. (2018). Improved GWO algorithm for optimal design of truss structures. Engineering with Computers. 34. 10.1007/s00366-017-0567-1.
    • ChaoticGWO: Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of computational design and engineering, 5(4), 458-472.
    • FuzzyGWO: RodrĂ­guez, Luis, Oscar Castillo, JosĂ© Soria, Patricia Melin, Fevrier Valdez, Claudia I. Gonzalez, Gabriela E. Martinez, and Jesus Soto. "A fuzzy hierarchical operator in the grey wolf optimizer algorithm." Applied Soft Computing 57 (2017): 315-328.
    • IncrementalGWO: Seyyedabbasi, A., & Kiani, F. (2021). I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers, 37(1), 509-532.
    • ExGWO: Seyyedabbasi, A., & Kiani, F. (2021). I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers, 37(1), 509-532.
    • DS_GWO: Jiang, Jianhua, Ziying Zhao, Yutong Liu, Weihua Li, and Huan Wang. "DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms." Knowledge-Based Systems 250 (2022): 109100.
    • IOBL_GWO: Bansal, J. C., & Singh, S. (2021). A better exploration strategy in Grey Wolf Optimizer. Journal of Ambient Intelligence and Humanized Computing, 12(1), 1099-1118.
    • OGWO: Yu, X., Xu, W., & Li, C. (2021). Opposition-based learning grey wolf optimizer for global optimization. Knowledge-Based Systems, 226, 107139.
    • ER_GWO: Long, W., Cai, S., Jiao, J. et al. An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization. Soft Comput 24, 997–1026 (2020).
    • CG_GWO: Li, K., Li, S., Huang, Z. et al. Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy. Sci Rep 12, 18961 (2022).

  • GOA - Grasshopper Optimisation Algorithm

    • OriginalGOA: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.

  • GCO - Germinal Center Optimization

    • OriginalGCO: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., LĂłpez-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27.
    • BaseGCO: The developed version

  • GSKA - Gaining Sharing Knowledge-based Algorithm

    • OriginalGSKA: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29.
    • BaseGSKA: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.

  • GBO - Gradient-Based Optimizer

    • OriginalGBO: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159.

H

  • HC - Hill Climbing .

    • OriginalHC: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE.
    • SwarmHC: The developed version based on swarm-based idea (Original is single-solution based method)

  • HS - Harmony Search .

    • OriginalHS: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68.
    • BaseHS: The developed version

  • HHO - Harris Hawks Optimization .

    • OriginalHHO: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.

  • HGSO - Henry Gas Solubility Optimization .

    • OriginalHGSO: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.

  • HGS - Hunger Games Search .

    • OriginalHGS: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.

  • HHOA - Horse Herd Optimization Algorithm (not done) .

    • BaseHHOA: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711.

  • HBA - Honey Badger Algorithm:

    • OriginalHBA: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110.

I

  • IWO - Invasive Weed Optimization .

    • OriginalIWO: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.

  • ICA - Imperialist Competitive Algorithm

    • OriginalICA: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.

  • IMODE - Improved Multi-operator Differential Evolution Algorithm:

    • OriginalIMODE: Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020, July). Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.

  • INFO - weIghted meaN oF vectOrs:

    • OriginalINFO: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.

J

  • JA - Jaya Algorithm
    • OriginalJA: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
    • BaseJA: The developed version
    • LevyJA: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902.

K

L

  • LSHADEcnEpSin - Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood

    • OriginalLSHADEcnEpSin: Awad, N. H., Ali, M. Z., & Suganthan, P. N. (2017, June). Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE congress on evolutionary computation (CEC) (pp. 372-379). IEEE.

  • LCO - Life Choice-based Optimization

    • OriginalLCO: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21.
    • BaseLCO: The developed version
    • ImprovedLCO: The improved version using Gaussian distribution and Mutation Mechanism

M

  • MA - Memetic Algorithm

    • OriginalMA: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.

  • MFO - Moth Flame Optimization

    • OriginalMFO: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.
    • BaseMFO: The developed version

  • MVO - Multi-Verse Optimizer

    • OriginalMVO: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
    • BaseMVO: The developed version

  • MSA - Moth Search Algorithm

    • OriginalMSA: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164.

  • MRFO - Manta Ray Foraging Optimization

    • OriginalMRFO: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300.

  • MPA - Marine Predators Algorithm:

    • OriginalMPA: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377.

N

  • NRO - Nuclear Reaction Optimization

    • OriginalNRO: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.

  • NMRA - Nake Mole-Rat Algorithm

    • OriginalNMRA: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.
    • ImprovedNMRA: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178.

O

P

  • PSO - Particle Swarm Optimization

    • OriginalPSO: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee.
    • PPSO: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718.
    • HPSO_TVAC: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New cls-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362.
    • C_PSO: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271.
    • CL_PSO: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.

  • PFA - Pathfinder Algorithm

    • OriginalPFA: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.

  • PSS - Pareto-like Sequential Sampling

    • OriginalPSS: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096.

Q

  • QSA - Queuing Search Algorithm
    • OriginalQSA: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.
    • BaseQSA: The developed version
    • OppoQSA: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251.
    • LevyQSA: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447.
    • ImprovedQSA: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46.

R

  • RUN - RUNge Kutta optimizer:
    • OriginalRUN: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.

S

  • SA - Simulated Annealling OriginalSA: Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. GaussianSA: Van Laarhoven, P. J., Aarts, E. H., van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing (pp. 7-15). Springer Netherlands. SwarmSA: My developed version

  • SSpiderO - Social Spider Optimization

    • OriginalSSpiderO: Cuevas, E., Cienfuegos, M., ZaldĂ­Var, D., & PĂ©rez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384.

  • SMO - Spider Monkey Optimization

    • DevSMO: Bansal, J. C., Sharma, H., Jadon, S. S., & Clerc, M. (2014). Spider monkey optimization algorithm for numerical optimization. Memetic computing, 6(1), 31-47.

  • SOS - Symbiotic Organisms Search:

    • OriginalSOS: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.

  • SSpiderA - Social Spider Algorithm

    • OriginalSSpiderA: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.

  • SCA - Sine Cosine Algorithm

    • OriginalSCA: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
    • BaseSCA: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343.

  • SRSR - Swarm Robotics Search And Rescue

    • OriginalSRSR: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726.

  • SBO - Satin Bowerbird Optimizer

    • OriginalSBO: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15.
    • BaseSBO: The developed version

  • SHO - Spotted Hyena Optimizer

    • OriginalSHO: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70.

  • SSO - Salp Swarm Optimization

    • OriginalSSO: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.

  • SFO - Sailfish Optimizer

    • OriginalSFO: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34.
    • ImprovedSFO: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318.

  • SARO - Search And Rescue Optimization

    • OriginalSARO: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019.
    • BaseSARO: The developed version using Levy-flight

  • SSDO - Social Ski-Driver Optimization

    • OriginalSSDO: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14.

  • SLO - Sea Lion Optimization

    • OriginalSLO: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5).
    • ImprovedSLO: The developed version
    • ModifiedSLO: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems.

  • Seagull Optimization Algorithm

    • OriginalSOA: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196.
    • DevSOA: The developed version

  • Squirrel Search Algorithm

    • OriginalSquirrelSA: Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and evolutionary computation, 44, 148-175.

  • SMA - Slime Mould Algorithm

    • OriginalSMA: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems.
    • BaseSMA: The developed version

  • SSA - Sparrow Search Algorithm

    • OriginalSSA: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830
    • BaseSSA: The developed version

  • SPBO - Student Psychology Based Optimization

    • OriginalSPBO: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804.
    • DevSPBO: The developed version

  • SCSO - Sand Cat Swarm Optimization

    • OriginalSCSO: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25.

T

  • TLO - Teaching Learning Optimization

    • OriginalTLO: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
    • BaseTLO: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
    • ImprovedTLO: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.

  • TWO - Tug of War Optimization

    • OriginalTWO: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.
    • OppoTWO: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13.
    • LevyTWO: The developed version using Levy-flight
    • ImprovedTWO: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369.

  • TSA - Tunicate Swarm Algorithm

    • OriginalTSA: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.

  • TSO - Tuna Swarm Optimization

    • OriginalTSO: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021.

U

V

  • VCS - Virus Colony Search
    • OriginalVCS: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88.
    • BaseVCS: The developed version

W

  • WCA - Water Cycle Algorithm

    • OriginalWCA: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.

  • WOA - Whale Optimization Algorithm

    • OriginalWOA: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
    • HI_WOA: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE.

  • WHO - Wildebeest Herd Optimization

    • OriginalWHO: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14.

  • WDO - Wind Driven Optimization

    • OriginalWDO: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757.

X

Y

Z

List of papers used MEALPY
  • Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE.
  • Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343.
  • Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440.
  • SĂĄnchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en lĂ­neas de transmisiĂłn basada en algoritmos heurĂ­sticos de optimizaciĂłn comparados. Revista IngenierĂ­a UC, 29(2), 106-123.
  • Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563.
  • Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984.
  • Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE.
  • Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111.
  • Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125.
  • Doğan, E., & YörĂŒkeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm.
  • Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE.
  • Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers.
  • LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973.
  • Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE.
  • Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE.
  • Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193.
  • Biundini, I. Z., Melo, A. G., Coelho, F. O., HonĂłrio, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46.
  • Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022.
  • Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699.
  • Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms.
  • Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
  • Rustam, F., Aslam, N., De La Torre DĂ­ez, I., Khan, Y. D., MazĂłn, J. L. V., RodrĂ­guez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI.
  • Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491.
  • Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087.
  • He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16.
  • Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection.
  • Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler.
  • Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313.
  • Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size.
  • Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686.
  • Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371.
  • Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742.
  • Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034.
  • Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics.
  • Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849.
  • Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094.
  • Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313.
  • Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis.
  • Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus).
  • LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation.
  • Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energĂ©tica considerando transaçÔes locais em “peer-to-peer” (Doctoral dissertation).
  • Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519.
  • Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659.
  • Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA.
  • Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE.
  • Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28.
  • Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371.
  • Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802.
  • Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18.
  • Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823.
  • Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16.
  • Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596.
  • Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062.
  • ValdĂ©s, G. T. (2022). Algoritmo para la detecciĂłn de vehĂ­culos y peatones combinando CNNÂŽ sy tĂ©cnicas de bĂșsqueda.
  • Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66.

Developed by: Thieu @ 2022

Keywords

optimization
metaheuristics
MHA
mathematical optimization
nature-inspired algorithms
evolutionary computation

FAQs

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

The Changelog Podcast: Practical Steps to Stay Safe on npm

Security News

The Changelog Podcast: Practical Steps to Stay Safe on npm

Learn the essential steps every developer should take to stay secure on npm and reduce exposure to supply chain attacks.

By Sarah Gooding  -  Oct 31, 2025