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2018 | OriginalPaper | Buchkapitel

A Hybrid Differential Evolution Algorithm and Particle Swarm Optimization with Alternative Replication Strategy

verfasst von : Lulu Zuo, Lei Liu, Hong Wang, Lijing Tan

Erschienen in: Advances in Swarm Intelligence

Verlag: Springer International Publishing

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Abstract

A new hybrid algorithm, combining Particle Swarm Optimization (PSO) and Differential Evolution (DE), is presented in this paper. In the proposed algorithm, an alternative replication strategy is introduced to avoid the individuals falling into the suboptimal. There are two groups at the initial process. One is generated by the position updating method of PSO, and the other is produced by the mutation strategy of DE. Based on the alternative replication strategy, those two groups are updated. The poorer half of the population is selected and replaced by the better half. A new group is composed and conducted throughout the optimization process of DE to improve the population diversity. Additionally, the scaling factor is used to enhance the search ability. Numerous simulations on eight benchmark functions show the superior performance of the proposed algorithm.

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Titel: A Hybrid Differential Evolution Algorithm and Particle Swarm Optimization with Alternative Replication Strategy
verfasst von: Lulu Zuo
Lei Liu
Hong Wang
Lijing Tan
Verlag: Springer International Publishing
Buch: Advances in Swarm Intelligence
Print ISBN: 978-3-319-93814-1

Electronic ISBN: 978-3-319-93815-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93815-8_46

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