Gauss Map-based Chaotic Particle Swarm Optimization

You can use the algorithm where stochastic optimization is needed.

With the aim of contributing to scientific research processes, I’m sharing the code related to same part of my study which provides global function optimization via Gauss Map-based Chaotic Particle Swarm Optimization.

You can use the algorithm where stochastic optimization is needed:

- Hyperparameter optimization,

- Global function optimization,

- Engineering design problems, etc…

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GM-CPSO: A New Viewpoint to Chaotic Particle Swarm Optimization via Gauss Map

For the usage of these codes, you may cite the following article:

• Koyuncu, H. (2020). GM-CPSO: A new viewpoint to chaotic particle swarm optimization via Gauss map. Neural Processing Letters, 52, 241-266.

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Gauss map-based Chaotic Particle Swarm Optimization(GM-CPSO)

(Global Optimization Problem)

Related Article: [1] Koyuncu, H. (2020). GM-CPSO: A new viewpoint to chaotic particle swarm optimization via Gauss map. Neural Processing Letters, 52, 241-266.

In the folder;

• ‘gmcpso_met.m’ involves the operation of Gauss map-based Chaotic Particle Swarm Optimization (GM-CPSO).
• ‘fit_fun.m’ generates the output for Griewank function.
• ‘main_part.m’ includes the main operation and parameter settings.