Multimodal Optimization Using Particle Swarm Optimization Algorithms: CEC 2015 Competition on Single Objective Multi-Niche Optimization

被引:0
作者
Cheng, Shi [1 ]
Qin, Quande [2 ]
Wu, Zhou [3 ]
Shi, Yuhui [4 ]
Zhang, Qingyu [3 ]
机构
[1] Univ Nottingham Ningbo, Div Comp Sci, Ningbo, Zhejiang, Peoples R China
[2] Shenzhen Univ, Dept Management Sci, Shenzhen, Peoples R China
[3] Univ Pretoria, Dept Elect Elect & Comp Engn, Pretoria, South Africa
[4] Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou, Peoples R China
来源
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2015年
关键词
Multimodal optimization; particle swarm optimization; topology structure; population diversity; POPULATION DIVERSITY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. The results of seven variants of particle swarm optimization (PSO) algorithms on IEEE Congress on Evolutionary Computation (CEC) 2015 single objective multi-niche optimization problems are reported in this paper. The PSO algorithms include PSO with star structure, PSO with ring structure, PSO with four clusters structure, PSO with Von Neumann structure, social-only PSO with star structure, social-only PSO with ring structure, and cognition-only PSO. The experimental tests are conducted on fifteen benchmark functions. Based on the experimental results, the conclusions could be made that the PSO with ring structure performs better than the other PSO variants on multimodal optimization. To obtain good performance on the multimodal optimization problems, an algorithm needs to converge the candidate solutions to the global optima while keep the population diversity during whole search process.
引用
收藏
页码:1075 / 1082
页数:8
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