PSO and multi-funnel landscapes: How cooperation might limit exploration

Andrew M. Sutton, Darrell Whitley, Monte Lunacek, Adele Howe

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

34 Scopus citations

Abstract

Particle Swarm Optimization (PSO) is a population-based optimization method in which search points employ a cooperative strategy to move toward one another. In this paper we show that PSO appears to work well on "single-funnel" optimization functions. On more complex optimization problems, PSO tends to converge too quickly and then fail to make further progress. We contend that most benchmarks for PSO have classically been demonstrated on single-funnel functions. However, in practice, optimization tasks are more complex and possess higher problem dimensionality. We present empirical results that support our conjecture that PSO performs well on single-funnel functions but tends to stagnate on more complicated landscapes.

Original languageEnglish (US)
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
Pages75-82
Number of pages8
StatePublished - Oct 30 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: Jul 8 2006Jul 12 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume1

Other

Other8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA
Period7/8/067/12/06

Keywords

  • Evolution Strategies
  • Optimization
  • Swarm Intelligence

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