Gauss Map-based Chaotic Particle Swarm Optimization

版本 1.0.0 (20.8 KB) 作者: Hasan
You can use the algorithm where stochastic optimization is needed.
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更新 2026/1/23

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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.

引用格式

Koyuncu, Hasan. “GM-CPSO: A New Viewpoint to Chaotic Particle Swarm Optimization via Gauss Map.” Neural Processing Letters, vol. 52, no. 1, May 2020, pp. 241–66, https://doi.org/10.1007/s11063-020-10247-2.

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1.0.0