文献类型：会议论文

英文题名：Dynamic Optimization with an Improved θ-PSO based on Memory Recall

作者：Zhong, Weimin[1];Xing, Jianliang[1];Liang, Yi[1];Qian, Feng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：8th World Congress on Intelligent Control and Automation (WCICA)

会议日期：JUL 06-09, 2010

会议地点：Jinan, PEOPLES R CHINA

语种：英文

外文关键词：evolutionary algorithm; particle swarm optimization (PSO); memory recall; dynamic optimization

摘要：A comparative study of theta-PSO and its improved model with partial particles randomization strategy on their abilities of tracking extrema in dynamic environments was carried out in our earlier work. And the results shown that theta-PSO has better performance in dynamic optimization than standard PSO. In this paper, an improved theta-PSO with memory recall and varying scale randomization strategy (theta-PSO-MR) is put forward. The eligible memory particles are recalled when the landscape changes. And the vary scale randomization is introduced through the evolution to maintain the swarm diversity. The offline error in the non-trivial multimodal dynamic functions MPB indicates that this improved theta-PSO deals well with the complex dynamic tracking and optimization. And in some cases, theta-PSO-MR outperforms theta-PSO-Rn for the introduction of memory recall.