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gradient-swarm-optimization

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gradient-swarm-optimization

A Python implementation of Gradient-guided Swarm Optimization algorithm

0.1.1
PyPI
Maintainers
1

GSO: Gradient-guided Swarm Optimization

A Python implementation of the Gradient-guided Swarm Optimization algorithm, designed for solving binary optimization problems. The algorithm uses a mathematically grounded approach combining gradient-based directional guidance with neighborhood learning mechanisms.

Features

  • Binary optimization for various problem types
  • Gradient-guided search for effective exploration
  • Neighborhood-based learning for local exploitation
  • Built-in command line interface
  • Automatic result saving and history tracking
  • Early stopping with known optimum
  • Automatic progress reporting
  • Built-in timeout mechanism
  • Reproducible results with seed setting

Installation

pip install gso

Quick Start

from gso import GSO
import numpy as np

def simple_fitness(solution):
    """Example fitness function: maximize sum of elements."""
    return np.sum(solution)

GSO.run(
    obj_func=simple_fitness,
    dim=100,
    pop_size=1000,
    max_iter=800,
    obj_type='max',
    neighbour_count=3,
    gradient_strength=0.8,
    base_learning_rate=0.1
)

Test Examples and Data

To run the test examples (UFLP and DUF problems):

  • Clone the GitHub repository:
git clone https://github.com/gazioglue/gso.git
cd gso
  • Run UFLP solver:
python examples/solve_uflp.py examples/data/uflp/test_instances/cap71.txt
  • Run DUF solver:
python examples/solve_dufs.py duf1

Available Command Line Options

For UFLP:

python solve_uflp.py cap71.txt --pop-size 2000 --max-iter 1000 --seed 42

For DUF:

python solve_dufs.py duf2 --dim 200 --pop-size 2000 --seed 42

Documentation

For more detailed usage instructions and examples, see USAGE.md.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Keywords

optimization

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