Orthogonal experimental design method used in particle swarm optimization for multimodal problems

Geng Zhang, Yangmin Li

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

11 Citations (Scopus)

Abstract

Orthogonal Experimental Design (OED) method is usually used to study the effect of several factors simultaneously and the best combination of factor levels can be found in several tests. The Particle Swarm Optimization (PSO) can utilize OED to improve the searching ability. However, the main effect of OED holds only when no or weak interaction of factors exists. This limitation of OED makes PSO search effective on unimodal or simple problems but very vulnerable on complex multimodal problems. This paper presents an effective method utilizing OED on multimodal problems. A new vector is formed through learning particle's previous and neighborhood's best vector. Instead of treating the new vector as exemplar for others to follow, this new vector is treated as base vector which needs to be explored further. Experimental studies on a set of test functions show that OED method used in this way has better robustness and converges closer to the global optimum than several other peer algorithms.
Original languageEnglish
Title of host publication2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings
PublisherIEEE Computer Society
Pages183-188
Number of pages6
ISBN (Print)9781467363433
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Hangzhou, Zhejiang, China
Duration: 19 Oct 201321 Oct 2013

Conference

Conference2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013
CountryChina
CityHangzhou, Zhejiang
Period19/10/1321/10/13

Keywords

  • Multimodal Problem
  • Orthogonal Experimental Design
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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