Heuristic Algorithms

This repository contains implementations of various heuristic algorithms to solve combinatorial and continuous optimization problems. These algorithms are useful for finding approximate solutions in problems where exact methods are infeasible due to high computational costs.

📌 Implemented Algorithms

• A Star Search
• Simulated Annealing
• Genetic Algorithm
• Ant Colony Optimization (ACO)
• Particle Swarm Optimization (PSO)
• Tabu Search

📂 Repository Structure

heuristic-algorithms/
│── algorithms/
│   │── simulated_annealing.py
│   │── genetic_algorithm.py
│   │── ant_colony.py
│   │── particle_swarm.py
│   │── tabu_search.py
│── examples/
│   │── tsp_example.py
│   │── knapsack_example.py
│── README.md
│── requirements.txt
│── main.py

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/your-username/heuristic-algorithms.git
   cd heuristic-algorithms

2. Install dependencies:

   pip install -r requirements.txt

3. Run an example:

   python examples/tsp_example.py

🛠 Dependencies

• Python 3.8+
• NumPy
• SciPy
• Matplotlib (for visualization, optional)

📖 References

• "Metaheuristics: From Design to Implementation" - El-Ghazali Talbi
• "An Introduction to Metaheuristics for Optimization" - Bastien Chopard · Marco Tomassini

📜 License

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

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