Hierarchical heterogeneous particle swarm optimization: algorithms and evaluations

Xinpei Ma, Hiroki Sayama

Research output: Contribution to journalArticle

Abstract

Particle swarm optimization (PSO) has recently been extended in several directions. Heterogeneous PSO (HPSO) is one of such recent extensions, which implements behavioural heterogeneity of particles. In this paper, we propose a further extended version, Hierarchcial Heterogeenous PSO (HHPSO), in which heterogeneous behaviors of particles are enforced through interactions among hierarchically structured particles. Two algorithms have been developed and studied: multi-layer HHPSO (ml-HHPSO) and multi-group HHPSO (mg-HHPSO). In each HHPSO algorithm, stagnancy and overcrowding detection mechanisms were implemented to avoid premature convergence. The algorithm performance was measured on a set of benchmark functions and compared with performances of standard PSO (SPSO) and HPSO. The results demonstrated that both ml-HHPSO and mg-HHPSO performed well on all testing problems and significantly outperformed SPSO and HPSO in terms of solution accuracy, convergence speed and diversity maintenance. Further computational experiments revealed the optimal frequencies of stagnation and overcrowding detection for each HHPSO algorithm.

Original languageEnglish
Pages (from-to)504-516
Number of pages13
JournalInternational Journal of Parallel, Emergent and Distributed Systems
Volume31
Issue number5
DOIs
Publication statusPublished - 2016 Sep 2
Externally publishedYes

Keywords

  • Heterogeneous behaviors
  • hierarchical heterogeneous particle swarm optimization
  • hierarchical structure
  • particle swarm optimization

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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