Dynamic ion motion optimization algorithm based on memory strategy

被引：0
|
作者：
Wang Y.-J. [1 ]
Ma C.-L. [1 ]
机构：
[1] School of Electrical Engineering, Northeast Electric Power University, Jilin
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 03期
关键词：
Computer application; Cosine similarity ranking rule; Dynamic optimization problem; Ion motion algorithm; Memory strategy;
D O I：
10.13229/j.cnki.jdxbgxb20190071
中图分类号：
学科分类号：
摘要：
To solve the problems of insufficient diversity and searching ability in solving dynamic optimization problems, a dynamic ion motion optimization algorithm based on memory strategy (MDIMO) is proposed. The algorithm introduces the role of global optimal individuals and repulsion. The start-up conditions and renewal methods of solid phase are improved. The selection of memory individuals based on probability is proposed, and positive and reverse memory populations (PRMPs) re constructed. According to the cosine similarity ranking rule, The evolutionary populations are renewed by using PRMPs, and the ability of evolutionary populations to track optimal solutions is accelerated. A method of increasing diversity after environmental change based on cosine similarity ranking is proposed. The proposed algorithm is tested on the international dynamic test function DF1 and MPB mobile peak problem, and compared with foure dynamic optimization algorithms which have better effect in rcent years. Simulation results show that the proposed algorithm is superior to the other algorithms in solving accuracy, convergence speed and stability. © 2020, Jilin University Press. All right reserved.
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页码：1047 / 1060
页数：13
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