Co-evolutionary competitive swarm optimizer with three-phase for large-scale complex optimization problem

被引:113
作者
Huang, Chen [1 ]
Zhou, Xiangbing [2 ]
Ran, Xiaojuan [2 ,3 ]
Liu, Yi [4 ]
Deng, Wuquan [5 ]
Deng, Wu [2 ,6 ]
机构
[1] Shenyang Aerosp Univ, Coll Civil Aviat, Shenyang 110136, Peoples R China
[2] Sichuan Tourism Univ, Sch Informat & Engn, Chengdu 610100, Peoples R China
[3] Chiang Mai Univ, Int Coll Digital Innovat, Chiang Mai 50200, Thailand
[4] Civil Aviat Management Inst China, Res Ctr Big Data & Informat Management, Beijing 100102, Peoples R China
[5] Chongqing Univ, Cent Hosp, Dept Endocrinol,Minist Educ, Key Lab Biorheol Sci & Technol, Chongqing 400014, Peoples R China
[6] Civil Aviat Univ China, Sch Elect Informat & Automat, Tianjin 300300, Peoples R China
基金
中国国家自然科学基金;
关键词
CSO; Three-phase co-evolutionary strategy; Update strategy; Optimization performance; Large-scale; COOPERATIVE COEVOLUTION; ALGORITHM;
D O I
10.1016/j.ins.2022.11.019
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Practical optimization problems often involve a large number of variables, and solving them in a reasonable amount of time becomes a challenge. Competitive swarm optimizer (CSO) is an efficient variant of particle swarm optimization (PSO) algorithm and has been applied extensively to deal with a variety of practical large-scale optimization problems. In this article, a novel co-evolutionary method with three-phase, namely TPCSO, is developed by incorporating a novel multi-phase cooperative evolutionary technique to enhance the convergence and the search ability of CSO. In the modified CSO, the population is evenly decomposed into two sub-populations, then the update strategy of each sub-population is adjusted by the requirements of the diversity and convergence during the evolution pro-cess. In the first phase, the diversity is paid more attention in order to explore more regions. And in the second phase, the promising area in two sub-populations are exploited by introducing excellent particles of two sub-populations. The third phase focuses on the convergence by learning from the global best solution. Finally, the performance of TPCSO is evaluated and proved by large-scale benchmark functions selected from CEC'2010 and CEC'2013. The experimental and statistical results show that TPCSO can effectively solve these large-scale problems and fast obtain the optimal results with higher accuracy by comparing with several algorithms.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:2 / 18
页数:17
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