NDLI: A Differential Evolution Algorithm Based on Local Search and Boundary Reflection for Global Optimization

Content Provider	IEEE Xplore Digital Library
Author	Lin, Hongwei
Copyright Year	2015
Abstract	Deferential evolution (DE) is a relatively simple dvolutionary algorithm (EA), which is an effective adaptive algorithm in the global optimization domain. In view of the two shortcomings of DE, the low convergence rate in the late evolution and easy to be trapped into the local optimums, a new DE based on local search and boundary reflection (LR-DE) is proposed in this paper. Local search is tantamount to speed up the convergence rate in the late evolution, boundary reflection is to maintain the diversity of the population. Therefore, DE is less able to fall into the local optimums in probability. The new LR-DE are used to some benchmark problems and the results are compared with of other algorithms. The results show that the LR-DE has superior performance in solving optimization problems.
Starting Page	253
Ending Page	257
File Size	452854
Page Count	5
File Format	PDF
e-ISBN	9781467386609
DOI	10.1109/CIS.2015.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Local search Global optimization Boundary reflection Sociology Linear programming Yttrium Statistics Deferential evolution Optimization Computational intelligence Convergence
Content Type	Text
Resource Type	Article

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A Differential Evolution Algorithm Based on Local Search and Boundary Reflection for Global Optimization