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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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.
| 版本 | 已发布 | 发行说明 | Action |
|---|---|---|---|
| 1.0.0 |
