Memetic Differential Evolution with an Improved Contraction Criterion
Joint Authors
Wang, Maocai
Peng, Lei
Zhang, Yanyun
Dai, Guangming
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-04
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization.
In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems.
The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation.
A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts.
The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests.
Moreover, sensitivity analyses for parameters in MDE are also made.
Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.
American Psychological Association (APA)
Peng, Lei& Zhang, Yanyun& Dai, Guangming& Wang, Maocai. 2017. Memetic Differential Evolution with an Improved Contraction Criterion. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139837
Modern Language Association (MLA)
Peng, Lei…[et al.]. Memetic Differential Evolution with an Improved Contraction Criterion. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1139837
American Medical Association (AMA)
Peng, Lei& Zhang, Yanyun& Dai, Guangming& Wang, Maocai. Memetic Differential Evolution with an Improved Contraction Criterion. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139837
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139837