Improved global-best-guided particle swarm optimization with learning operation for global optimization problems

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• 作者：Hai-bin Ouyang^a ; ^{oyhb1987@163.com} ; Li-qun Gao^b ; Steven Li^c ; Xiang-yong Kong^d
• 关键词：Particle swarm optimization ; Global exploration capability ; Convergence speed ; Accuracy
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：987-1008
• 全文大小：1535 K
• 卷排序：52

文摘

A new population partitioning strategy is employed to PSO algorithm. In the current population, global neighborhood exploration strategy is presented to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in historical best population. Stochastic learning and opposition based learning operations are employed to accelerate convergence speed and improve optimization accuracy in global best population. IGPSO performs better for engineering design optimization problems.