Heuristic Optimization Algorithms

This project implements and compares various heuristic optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and their variations with local search, to solve complex optimization problems.

Table of Contents

• Features
• Project Structure
• Dependencies
• Setup
• Usage
• Results
• Contributing
• License

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Features

• Optimization Algorithms: Includes implementations for:
  • Genetic Algorithm (GA)
  • Genetic Algorithm with Local Search (GA_LS)
  • Particle Swarm Optimization (PSO)
  • Particle Swarm Optimization with Local Search (PSO_LS)
• Problem Selection: Dynamically select optimization problems to solve.
• Result Analysis: Compare algorithm performance using:
  • Best solution
  • Mean and standard deviation across multiple runs
• Visualization: Graphically compare algorithm performance.
• Result Saving: Automatically saves outputs for analysis.

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Project Structure

.
├── heuristic
│   ├── algorithms                  # Contains algorithm implementations
│   ├── problems                    # Problem definitions
│   └── utils                       # Utility functions
├── main.py                         # Entry point for running algorithms
├── README.md                       # Project documentation
├── requirements.txt                # Dependencies
├── results                         # Stores outputs and plots
├── .gitignore                      # Git ignore rules
└── .venv                           # Virtual environment (not included in repo)

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Dependencies

• Python 3.10 or higher
• Required libraries are listed in requirements.txt.

Key dependencies:

• numpy
• matplotlib

Install them using:

pip install -r requirements.txt

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Setup

1. Clone the repository:

   git clone https://github.com/Ibrahimghali/heuristic.git
   cd heuristic-optimization

2. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

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Usage

1. Run the main script:

   python main.py

2. Select a problem: Follow the prompts to select an optimization problem to solve.

3. View Results:

   • Best, mean, and standard deviation of solutions will be printed in the terminal.
   • Output files will be saved in the results/ directory.

4. Visualize Results:

   • Plots will be displayed and saved in results/plots.

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Results

• Text Results: Outputs for all algorithms are saved in results/ with filenames corresponding to the algorithm names.

• Plots: Comparative performance plots are saved in results/plots.

Example result plot:

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Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m "Add feature-name").
4. Push to your fork (git push origin feature-name).
5. Open a pull request.

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License

This project is licensed under the MIT License. See LICENSE for details.

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