[1]DING Qingfeng,YIN Xiaoyu.Research survey of differential evolution algorithms[J].CAAI Transactions on Intelligent Systems,2017,12(4):431-442.[doi:10.11992/tis.201605015]
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Research survey of differential evolution algorithms

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 4 Page number: 431-442 Column: 学术论文—智能系统 Public date: 2017-08-25
Title:
Research survey of differential evolution algorithms
Author(s):
DING Qingfeng1;  YIN Xiaoyu2
1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;
2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China
Keywords:
differential evolution algorithm; heuristic parallel search; differential strategies; control parameter; population structure; mixed optimization; convergence rate; optimization efficiency
CLC:
TP301
DOI:
10.11992/tis.201605015
Abstract:
Due to its simple algorithm structure, ease of performance, high optimization efficiency, simple parameter setting, and excellent robustness, the differential evolution (DE) algorithm has attracted increasing attention from researchers. In this paper, we outline the basic concepts of the DE algorithm as well as its limitations, and review four improvement strategies, including a control parameter, differential strategy, population structure, and mixing it with other optimization algorithms. We discuss the advantages and disadvantages of these strategies and suggest directions for future improvements to the DE algorithm.
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