Metaheuristic Optimization

Overview

This repository contains a collection of metaheuristic optimization algorithms implemented in Python. The repository includes implementations of various metaheuristic algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) on different functions and optimization problems.

Features

• Algorithm Comparisons: Compare the performance of different optimization algorithms on the same problem.
• Interactive Jupyter Notebooks: All implementations are provided in Jupyter Notebooks for easy experimentation and visualization.

Repository Contents

• Rosenbrock_Function_Optimization.ipynb: A Jupyter Notebook demonstrating the optimization of the Rosenbrock function using multiple metaheuristic algorithms including PSO, GA, ACO, and MMAS ACO.
• README.md: This file provides an overview of the repository.

Metaheuristic Algorithms Included

• Genetic Algorithm (GA): A population-based search algorithm that mimics the process of natural selection.
• Particle Swarm Optimization (PSO): A computational method that optimizes a problem by iteratively improving candidate solutions concerning a given quality measure.
• Ant Colony Optimization (ACO): A probabilistic technique inspired by the behavior of ants finding paths to food.
• Max-Min Ant System (MMAS ACO): An enhanced version of ACO that limits the pheromone values to a predefined range, improving the exploration and exploitation balance in optimization.

How to Use

1. Clone the Repository:

   git clone [email protected]:KimyaKheirkhah/optimization-algorithms.git
   cd optimization-algorithms