Hierarchical heterogeneous particle swarm optimization: algorithms and evaluations

Xinpei Ma, Hiroki Sayama

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)504-516
ページ数13
ジャーナルInternational Journal of Parallel, Emergent and Distributed Systems
31
5
DOI
出版ステータスPublished - 2016 9 2
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ネットワークおよび通信

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