A Hybrid Differential Evolution Algorithm for Solving Function Optimization

Abstract:

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One of the key points resulting in the success of differential evolution (DE) is its mechanism of different mutation strategies for generating mutant vectors. In this paper, we also present a novel mutation strategy inspired by the velocity updating scheme of particle swarm optimization (PSO). The proposed approach is called HDE, which conducts the mutation strategy on the global best vector for each generation. Experimental studies on 8 well-known benchmark functions show that HDE outperforms other three compared DE algorithms in most test cases.

Info:

Periodical:

Key Engineering Materials (Volumes 439-440)

Edited by:

Yanwen Wu

Pages:

315-320

DOI:

10.4028/www.scientific.net/KEM.439-440.315

Citation:

Z. G. Zhou "A Hybrid Differential Evolution Algorithm for Solving Function Optimization", Key Engineering Materials, Vols. 439-440, pp. 315-320, 2010

Online since:

June 2010

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Price:

$35.00

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