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Metaheuristics

A set of classical metaheuristics that solve optimization problems.

Overview

Metaheuristics are a set of algorithms that employ some degree of randomness to try to find optimal solutions to difficult problems [1]. This repository holds a single Python script, which implements some classical metaheuristics and applies them to a set of optimization problems.

The metaheuristics implemented in this repository are:

  • Hill-Climbing (HC)
  • Steepest Ascent Hill-Climbing (SAHC)
  • Steepest Ascent Hill-Climbing with Replacement (SAHCR)
  • Simulated Annealing (SA)
  • Tabu Search (TS)
  • Iterated Local Search (ILS)

Whereas the suported optimization problems are:

  • Sphere
  • Schwefels
  • Rosenbrocks
  • Rastringins

For more details about these algorithms and this whole metaheuristics subject, please refer to this PDF file.

Usage

Clone this repository:

git clone https://github.com/michel-meneses/metaheuristics.git

Enter into the project directory:

cd metaheuristics

Use pip to install the Python dependencies:

pip install requeriments.txt

Run the main Python script:

python main.py [problem] [algorithm]

Where:

  • Problem: sphere, schwefels, rosenbrocks or rastringins
  • Algorithm: hc, sahc, sahcr, sa, tb or ils

Disclaimer

This repository was developed as an assignment for the Search and Optimization course offered by the graduation program PROCC during my master's degree in CS. If you are a student enrolled in this same graduation course, be aware that this repository is public, therefore it is crawlable by plagiarism detector tools. Be responsible and use this repository wisely. 😉

References

  1. https://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf

License

This repository is distributed under the terms of the MIT License.