Distributed learning particle swarm optimizer for global optimization of multimodal problems

Geng Zhang, Yangmin Li, Yuhui Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.
Original languageEnglish
Pages (from-to)122-134
Number of pages13
JournalFrontiers of Computer Science
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • orthogonal experimental design (OED)
  • particle swarm optimizer (PSO)
  • swarm intelligence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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