Adaptive differential evolution with directional strategy and cloud model

Gou, Jianping; Guo, Wang-Ping; Feng, Huanqing; Wang, Cheng; Cai, Yiqiao

doi:10.1007/s10489-014-0592-3

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…In Zhang and Yuen (2015), with a difference vector pool containing good direction information, a directional mutation DE (DMDE) was proposed to speed up the convergence of DE. Based a distributed topology, a directional strategy was employed to enhance convergence speed by using the direction information in Gou et al (2015).…”

Section: Related Workmentioning

confidence: 99%

Cellular direction information based differential evolution for numerical optimization: an empirical study

et al. 2015

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Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

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Section: Related Workmentioning

confidence: 99%