Gravitational Search Algorithm (GSA) Implementation

This repository contains a Python implementation of the Gravitational Search Algorithm (GSA), a physics-inspired metaheuristic optimization algorithm. The implementation includes several benchmark functions and visualization tools for analyzing the algorithm's performance.

Features

• Pure NumPy implementation of GSA
• Multiple benchmark functions (Sphere, Rastrigin, Rosenbrock, Ackley)
• Population size analysis tools
• Visualization of convergence behavior
• Comprehensive documentation and examples

Installation

git clone https://github.com/yourusername/gsa-optimization.git
cd gsa-optimization
pip install -r requirements.txt

Usage

Basic usage example:

from src.gsa import gravitational_search
import numpy as np

# Run GSA optimization
best_position, best_fitness = gravitational_search(
    n_particles=30,
    n_dimensions=30,
    fitness_function='sphere'
)

For more examples, check the examples/ directory. The examples show:

• simple_example.py: Basic usage of GSA

• compare_functions.py: How GSA performs on different optimization problems

• population_study.py: How population size affects optimization performance

  Run basic example

        python examples/simple_example.py

        # Compare different functions
        python examples/compare_functions.py

        # Study population sizes
        python examples/population_study.py

Benchmark Functions

The implementation includes several standard benchmark functions:

• Sphere Function (unimodal)
• Rastrigin Function (multimodal)
• Rosenbrock Function (valley-shaped)
• Ackley Function (multimodal)

Results

Example convergence analysis for different population sizes on the Rosenbrock function: [ ]

Requirements

• numpy
• matplotlib
• time

License

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

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request