An improved MOPSO with a crowding distance based external archive maintenance strategy

  • Wei Xing Li
  • , Qian Zhou
  • , Yu Zhu
  • , Feng Pan*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

For multi-objective optimization algorithms, the maintenance policy of external archive has a great impact on the performance of convergence and solution diversity. Considering the dilemma of large population and external archive, an improved strategy of external archive maintenance based on crowding distance is proposed, which requires less particle numbers and smaller archive size, resulting in the computation cost reduction. Furthermore, the information entropy of gbest is analyzed to emphasize the diversity improvement of non-dominant solutions and well-distribution on the Pareto-optimal front. Numerical experiments of benchmark functions demonstrate the effectiveness and efficiency of proposed multi-objective particle swarm optimization.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings
Pages74-82
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2012
Event3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China
Duration: 17 Jun 201220 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7331 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Swarm Intelligence, ICSI 2012
Country/TerritoryChina
CityShenzhen
Period17/06/1220/06/12

Keywords

  • Multi-objective optimization
  • Pareto-optimal front
  • Particle Swam Optimizer
  • information entropy

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