Particle Swarm Optimization Algorithm with Random Perturbation around Convergence Center

Abstract:

A new modified strategy is proposed based on the analysis of convergence of particle swarm optimization (PSO). Each particle is manipulated by its convergence center, and then randomly perturbed around it. Some mutation operators are used to retain diversity of population and avoid being plunged to local optimum. Moreover, theoretical analysis has been made to prove that it can more easily converge to the global optimum. Experiment results show that it is superior to basic particle swarm optimization in quality and efficiency.

• Periodical: Advanced Materials Research (Volumes 488-489)
• Pages: 729-734
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.488-489.729
• Online since: March 2012
• Authors: Wei Li Xiong, Deng Feng Liu, Jing Jing Zhao, Bao Guo Xu
• Keywords: Convergence Center, Fitness Value, Particle Swarm Optimization Algorithm (PSO), Random Perturbation

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