Multiobjective Particle Swarm Optimization Without the Personal Best

doi:10.1007/s12204-014-1484-8

上海交通大学学报（英文版） ›› 2014, Vol. 19 ›› Issue (2): 155-159.doi: 10.1007/s12204-014-1484-8

Multiobjective Particle Swarm Optimization Without the Personal Best

WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣)

(1. Department of Computer Science and Technology, Shanghai University of Finance and Economics, Shanghai 200433, China; 2. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

出版日期:2014-04-30 发布日期:2014-04-29
通讯作者: XU He-ming (徐鹤鸣) E-mail:hemingqiubai@126.com

Multiobjective Particle Swarm Optimization Without the Personal Best

WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣)

(1. Department of Computer Science and Technology, Shanghai University of Finance and Economics, Shanghai 200433, China; 2. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

Online:2014-04-30 Published:2014-04-29
Contact: XU He-ming (徐鹤鸣) E-mail:hemingqiubai@126.com

摘要/Abstract

摘要： The personal best is an interesting topic, but little work has focused on whether it is still efficient for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective optimization problems, the diversity provided by the personal best is less optimal, whereas the global archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental results validate the efficiency of our strategy.

关键词: multiobjective optimization problems, particle swarm optimization (PSO), personal best, global best, global archive

Abstract: The personal best is an interesting topic, but little work has focused on whether it is still efficient for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective optimization problems, the diversity provided by the personal best is less optimal, whereas the global archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental results validate the efficiency of our strategy.

Key words: global best, global archive, multiobjective optimization problems, particle swarm optimization (PSO), personal best

中图分类号:

TP 181

WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣). Multiobjective Particle Swarm Optimization Without the Personal Best[J]. 上海交通大学学报（英文版）, 2014, 19(2): 155-159.

WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣). Multiobjective Particle Swarm Optimization Without the Personal Best[J]. Journal of shanghai Jiaotong University (Science), 2014, 19(2): 155-159.

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