doi: 10.4304/jsw.6.4.604-611
A Novel PSO Algorithm Based on Local Chaos & Simplex Search Strategy and its Application
- 1 Dept. of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou, China
- 2 Dept. of Control Science and Engineering Huazhong University of Science and Technology Wuhan, China
Abstract
To improve particle swarm optimization (PSO) computing performance, the centroid of particle swarm is firstly introduced in standard PSO model to enhance interparticle cooperation and information sharing capabilities, then combining randomness and ergodicity of the strong chaotic motion and fast convergence of the simplex method, a novel particle swarm optimization algorithm with adaptive space mutation (CSM-CPSO) is proposed to improve local optimum efficiency and global convergence performance of PSO algorithm. Results of Benchmark function simulation and the material balance computation (MBC) in alumina production show the new algorithm has not only steady convergence and better stability, but also higher precision and faster convergence speed, and also can avoid the premature convergence problem effectively.
Keywords
- Particle Swarm Optimization
- Centroid
- C
How to Cite
Shengli Song, Yong Gan, Li Kong, and Jingjing Cheng, "A Novel PSO Algorithm Based on Local Chaos & Simplex Search Strategy and its Application," Journal of Software, vol. 6, no. 4, pp. 604-611, 2011. https://doi.org/ 10.4304/jsw.6.4.604-611
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).