Group decision-making inspired particle swarm optimization in noisy environment

Ji Ma, Junqi Zhang, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Particle Swarm Optimizer (PSO) has gained wide applications in different fields. However, it loses its efficiency when facing an optimization problem in a noisy environment, since the inaccuracy of each particle's own "best" might mislead the entire swarm. Staying together is often of great selective advantage for social animals in nature. Social animals frequently make consensus decisions, and the decisions made by a majority of informed group members should be beneficial as they intend to avoid extreme outcomes or risky decisions. Inspired by this social behavior, a new particle swarm optimizer based on group decision-making (PSOGD) is developed for noisy optimization problems. Its significant feature is the elimination of resampling that is commonly used for noise optimization problems. The proposed algorithm is compared experimentally on 20 large-scale benchmark functions with various noise. The results demonstrate its superiority over other existing PSO variants.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages316-321
Number of pages6
ISBN (Electronic)9781479986965
DOIs
StatePublished - Jan 12 2016
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: Oct 9 2015Oct 12 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Other

OtherIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
CountryHong Kong
CityKowloon Tong
Period10/9/1510/12/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Information Systems and Management
  • Control and Systems Engineering

Keywords

  • Group Decision-mpaking
  • Noisy
  • Particle Swarm Optimization

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