A new mutation operator for differential evolution algorithm

被引:0
作者
Mingcheng Zuo
Guangming Dai
Lei Peng
机构
[1] China University of Mining and Technology,Artificial Intelligence Research Institute
[2] China University of Geosciences (Wuhan),School of Computer Science
[3] China University of Geosciences,Hubei Key Laboratory of Intelligent Geo
[4] Key Laboratory of Geological Survey and Evaluation of Ministry of Education,Information Processing
来源
Soft Computing | 2021年 / 25卷
关键词
Differential evolution; Mutation operator; Scaling factor;
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学科分类号
摘要
The widely employed mutation operator DE/current-to-pbest/1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-pbest/1$$\end{document} in the differential evolution algorithm (DE) is further developed to a new version DE/current-to-pbest/1-X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-pbest/1-X$$\end{document} in this paper. To test its performance, it has been embedded in the novel successful history-based adaptive DE (L-SHADE) and compared with other recently proposed mutation operators. In DE/current-to-pbest/1-X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-pbest/1-X$$\end{document}, the updated parameter memories in each generation are not adopted when the initial value can still maintain an acceptable successful rate of finding better offspring. Also, the generated worse offsprings with acceptable fitness values are partially archived to generate differential vectors. The experimental results show that DE/current-to-pbest/1-X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-pbest/1-X$$\end{document} has a comparable performance than DE/current-to-pbest/1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-pbest/1$$\end{document}, DE/current-to-ord_pbest/1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-ord\_pbest/1$$\end{document} and DE/current-to-ord_best/1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DE/current-to-ord\_best/1$$\end{document}.
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页码:13595 / 13615
页数:20
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共 60 条
[1]  
Alcalá-Fdez J(2009)Keel: a software tool to assess evolutionary algorithms for data mining problems Soft Comput 13 307-318
[2]  
Sanchez L(2008)Population size reduction for the differential evolution algorithm Appl Intell 29 228-247
[3]  
Garcia S(2018)A+ evolutionary search algorithm and qr decomposition based rotation invariant crossover operator Expert Syst Appl 103 49-62
[4]  
del Jesus MJ(2019)Bernstain-search differential evolution algorithm for numerical function optimization Expert Syst Appl 138 112831-3937
[5]  
Ventura S(2020)Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms Neural Comput Appl 32 3923-298
[6]  
Garrell JM(2002)A parameter study for differential evolution Adv Intell Syst, Fuzzy Syst, Evolut Comput 10 293-220
[7]  
Otero J(2009)A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the cec’2005 special session on real parameter optimization J Heuristics 15 617-69
[8]  
Romero C(2013)Parameter extraction of solar cell models using repaired adaptive differential evolution Solar Energy 94 209-657
[9]  
Bacardit J(2008)Enhancing the performance of differential evolution using orthogonal design method Appl Math Comput 206 56-686
[10]  
Victor Mivas R(2006)Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems IEEE Trans Evolut Comput 10 646-958