A Modified Particle Swarm Optimization Algorithm

• 作者: Ai-Qin Mu De-Xin Cao Xiao-Hua Wang
• 来源期刊: 自然科学期刊(英文)
• 学科: 工学
• 关键词: PSO SIMULATED ANNEALING ALGORITHM GLOBAL SEARCHING
• 年，卷（期）: 2009,（2）
• 页码范围: 151-155
• 页数: 5页
• 分类号: TP1

摘要

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.