A Modified Particle Swarm Optimization Algorithm

Ai-Qin Mu, De-Xin Cao, Xiao-Hua Wang

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DOI: 10.4236/ns.2009.12019                

Abstract

Particle Swarm Optimization (PSO) is a new optimization algorithm, which is applied in many fields widely. But the original PSO is likely to cause the local optimization with premature convergence phenomenon. By using the idea of simulated annealing algo-rithm, we propose a modified algorithm which makes the most optimal particle of every time of iteration evolving continu-ously, and assign the worst particle with a new value to increase its disturbance. By the testing of three classic testing functions, we conclude the modified PSO algorithm has the better performance of convergence and global searching than the original PSO.

Keywords

PSO; Simulated Annealing Algorithm; Global Searching

Conflicts of Interest

The authors declare no conflicts of interest.

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