A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems

被引：0

作者：

Wu, Daqing ^{[1
,2
,3
,4
,5
]}

Tang, Lixiang ^{[6
]}

Li, Haiyan ^{[1
]}

Ouyang, LiJun ^{[1
]}

机构：

[1] Computer Science and Technology Institute, University of South China, Hangyang,Hunan, China

[2] Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai,200240, China

[3] Artificial Intelligence Key Laboratory of Sichuan Province (Sichuan University of Science and Engineering), Zigong,643000, China

[4] Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning,530006, China

[5] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei,Anhui Province,230039, China

[6] Department of Business Administration, Hunan University of Finance and Economics, Hunan,410205, China

来源：

Advances in Modelling and Analysis A | 2016年 / 53卷 / 01期

关键词：

Co-evolutionary particle swarm optimizations - Diversity - Multi objective algorithm - Multi-objective optimization problem - Multi-objective particle swarm optimization algorithms - Multi-objective problem - Particle swarm optimization algorithm - Particle swarm optimizers;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper proposes a multi-objective with dynamic topology particle swarm optimization (PSO) algorithm for solving multi-objective problems, named DTPSO. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, DTPSO uses two dynamic local best particles to lead the search particles with multiple populations to deal with multiple objectives, and maintains diversity of new found non-dominated solutions via partitioned the searching space into fixed number of cells. The proposed DTPSO is validated through comparisons with other two multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that DTPSO shows competitive, if not better, performance as compared to the other algorithms. © 2016, AMSE Press. All rights reserved.

引用

页码：145 / 159