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Multi-Objective Optimization Algorithms

This repository provides an analysis of various optimization algorithms with a focus on multi-objective optimization. Each method includes an introduction, methodology, implementation, conclusion, and bibliography.

Problem Statement

In datasets where identifying Pareto-optimal points is essential, deterministic algorithms like Simple Cull can be effective. However, as dataset size increases, these algorithms become less efficient due to longer processing times. In such cases, stochastic algorithms offer a viable alternative, often providing near-constant execution times for specific parameters and problems.

Algorithms Compared

This analysis evaluates several algorithms, including their continuous versions and adaptations for the specified problem:

• Non-dominated Sorting Genetic Algorithm II (NSGA-II)
• Non-dominated Sorting Genetic Algorithm III (NSGA-III)
• Strength Pareto Evolutionary Algorithm 2 (SPEA2)
• Non-dominated Sorting Gravitational Search Algorithm (NSGSA)
• Simple Cull Algorithm
• Pareto Simulated Annealing (PSA)
• Binary Multi-Objective Tabu Search (BMOTS)
• Full Non-dominated Sorting and Ranking

Repository Structure

The repository is organized as follows:

• helpers/: Contains auxiliary scripts and functions used across various implementations.
• images/: Includes visual representations and figures related to the algorithms.
• jupyterCodes/: Houses Jupyter notebooks with detailed code implementations and analyses.
• pareto_front/: Stores data and scripts related to Pareto front calculations.
• pyCodes/: Contains Python scripts for algorithm implementations.
• Libraries to install.ipynb: Jupyter notebook listing required libraries and installation instructions.
• State of Art.ipynb: Notebook discussing the current state-of-the-art in multi-objective optimization algorithms.
• jupyterHelpers.ipynb: Notebook with helper functions for Jupyter notebooks.
• pyHelpers.py: Python script with helper functions for Python scripts.
• README.md: This file, providing an overview of the repository.
• État de l'art.ipynb: French version of the "State of Art" notebook.

Getting Started

To explore the algorithms and analyses:

1. Clone the repository:

   git clone https://github.com/Gabriel382/multi-objective-optimization-algorithms.git
   
   

2. Install the required libraries: Refer to the Libraries to install.ipynb notebook for a list of dependencies and installation instructions.

3. Explore the Jupyter notebooks: Navigate to the jupyterCodes directory and open the notebooks to review the implementations and analyses.

Contributing

Contributions are welcome. Please fork the repository, create a new branch for your feature or bug fix, and submit a pull request for review.

License

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

For more information, visit the repository: multi-objective-optimization-algorithms

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