文献类型：期刊文献

英文题名：On the equivalences and differences of evolutionary algorithms

作者：Ma, Haiping[1,3];Simon, Dan[2];Fei, Minrui[3];Chen, Zixiang[1]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing, Zhejiang, Peoples R China;[2]Cleveland State Univ, Dept Elect & Comp Engn, Cleveland, OH 44115 USA;[3]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China

年份：2013

卷号：26

期号：10

起止页码：2397

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:000326904500013)、、EI(收录号：20134516957379)、Scopus(收录号：2-s2.0-84887020873)、WOS

基金：This work is supported by the National Natural Science Foundation of China under Grant nos. 61074032, 61179041, the Zhejiang Provincial Natural Science Foundation of China under Grant no. Y1090866, and the Project of Science and Technology Commission of Shanghai Municipality under Grant no. 10JC1405000. The comments of the reviewers were instrumental in strengthening this paper from its first version.

语种：英文

外文关键词：Evolutionary algorithms; Genetic algorithm; Biogeography-based optimization; Differential evolution; Evolution strategy; Particle swarm optimization

外文摘要：Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels. (C) 2013 Elsevier Ltd. All fights reserved.