A new algorithm based on PSO for Multi-Objective Optimization

Man Fai Leung, Sin Chun Ng, Chi Chung Cheung, Andrew K. Lui

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm that has two new components: leader selection and crossover. The new leader selection algorithm, called Space Expanding Strategy (SES), guides particles moving to the boundaries of the objective space in each generation so that the objective space can be expanded rapidly. Besides, crossover is adopted instead of mutation to enhance the convergence and maintain the stability of the generated solutions (exploitation). The performance of the proposed MOPSO algorithm was compared with three popular multi-objective algorithms in solving fifteen standard test functions. Their performance measures were hypervolume, spread and inverse generational distance. The performance investigation found that the performance of the proposed algorithm was generally better than the other three, and the performance of the proposed crossover was generally better than three popular mutation operators.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3156-3162
Number of pages7
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - 25 May 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 25 May 201528 May 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

ConferenceIEEE Congress on Evolutionary Computation, CEC 2015
CountryJapan
CitySendai
Period25/05/1528/05/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mathematics

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