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Soft Computing

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01.07.2014 | Methodologies and Application

Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis

verfasst von: Yong Zhang, Dun-wei Gong, Xiao-yan Sun, Na Geng

Erschienen in: Soft Computing | Ausgabe 7/2014

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Abstract

Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation.

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Titel: Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis
verfasst von: Yong Zhang
Dun-wei Gong
Xiao-yan Sun
Na Geng
Publikationsdatum: 01.07.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1147-y

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