Hybridisation of particle swarm optimization and fast evolutionary programming

Jingsong HE; Zhengyu YANG; Xin YAO

doi:10.1007/11903697_50

Hybridisation of particle swarm optimization and fast evolutionary programming

Jingsong HE, Zhengyu YANG, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

6 Citations (Scopus)

Abstract

Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a novel hybridisation of PSO and FEP, i.e., fast PSO (FPSO), where the strength of PSO and FEP is combined. In particular, the ideas behind Gaussian and Cauchy mutations are incorporated into PSO. The new FPSO has been tested on a number of benchmark functions. The preliminary results have shown that FPSO outperformed both PSO and FEP significantly. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	392-399
Number of pages	8
Volume	4247 LNCS
DOIs	https://doi.org/10.1007/11903697_50
Publication status	Published - 2006
Externally published	Yes

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title = "Hybridisation of particle swarm optimization and fast evolutionary programming",

abstract = "Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a novel hybridisation of PSO and FEP, i.e., fast PSO (FPSO), where the strength of PSO and FEP is combined. In particular, the ideas behind Gaussian and Cauchy mutations are incorporated into PSO. The new FPSO has been tested on a number of benchmark functions. The preliminary results have shown that FPSO outperformed both PSO and FEP significantly. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

author = "Jingsong HE and Zhengyu YANG and Xin YAO",

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Hybridisation of particle swarm optimization and fast evolutionary programming. / HE, Jingsong; YANG, Zhengyu; YAO, Xin.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4247 LNCS 2006. p. 392-399.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Hybridisation of particle swarm optimization and fast evolutionary programming

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