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Natural Computing

Erschienen in:

01.09.2010

A particle swarm optimization based memetic algorithm for dynamic optimization problems

verfasst von: Hongfeng Wang, Shengxiang Yang, W. H. Ip, Dingwei Wang

Erschienen in: Natural Computing | Ausgabe 3/2010

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Abstract

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.

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Titel: A particle swarm optimization based memetic algorithm for dynamic optimization problems
verfasst von: Hongfeng Wang
Shengxiang Yang
W. H. Ip
Dingwei Wang
Publikationsdatum: 01.09.2010
Verlag: Springer Netherlands
Erschienen in: Natural Computing / Ausgabe 3/2010
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-009-9176-2

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