A Simple Hybrid Particle Swarm Optimization

A Simple Hybrid Particle Swarm Optimization

by Wei-Chang Yeh

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A successful evolutionary algorithm is one with a proper balance between exploration (searching for good solutions), and exploitation (refining the solutions by combining information gathered during the exploration phase). In this study, a new hybrid version of PSO called HPSO is proposed. The HPSO constitutes a vector based PSO method with the linearly varying inertia weight, along with a local search. A novel, simpler, and efficient mechanism is employed to move the gBest to its next position…

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Title Statements

  • Full title :A Simple Hybrid Particle Swarm Optimization.

Summary

A successful evolutionary algorithm is one with a proper balance between exploration (searching for good solutions), and exploitation (refining the solutions by combining information gathered during the exploration phase). In this study, a new hybrid version of PSO called HPSO is proposed. The HPSO constitutes a vector based PSO method with the linearly varying inertia weight, along with a local search. A novel, simpler, and efficient mechanism is employed to move the gBest to its next position in the proposed HPSO. The HPSO combines the population-based evolutionary searching ability of PSO and local searching behavior to effciently balance the exploration and exploitation abilities. The result obtained by HPSO has been compared with those obtained from traditional simple PSO (SPSO) and improved PSO (IPSO) proposed recently. Computational results show that the proposed HPSO shows an enhancement in searching efficiency and improve the searching quality. In summary, the results presented in this work are encouraging and promising for the application of the proposed HPSO to other complex problems. Further analysis is necessary to see how other soft computing method (e.g., the genetic algorithm, the taboo search, etc.) react to local searches for future researchers who may want to develop their own heuristics and to make further improvements. Our research is still very active and under progress, and it opens the avenues for future efforts in this directions such as: how to adjust parameters, increase success rates, reduce running times, using other local search, and the aggregation of different and new concepts to PSO.

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Edition Statements

  • Publication Date:2008.

Publication Statements

The Physical Object

  • Description:1 online resource

Edition Identifiers

  • ISBN:9789537619114, 9537619117
  • oclc:ocn884023729

Subjects

Open Access Collection

Writers

Wei-Chang Yeh

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