文献类型：期刊文献

英文题名：Hybrid biogeography-based evolutionary algorithms

作者：Ma, Haiping[1,3];Simon, Dan[2];Fei, Minrui[3];Shu, Xinzhan[4];Chen, Zixiang[1]

机构：[1]Shaoxing Univ, Dept Phys & Elect Engn, Shaoxing, Zhejiang, Peoples R China;[2]Cleveland State Univ, Dept Elect & Comp Engn, Cleveland, OH 44115 USA;[3]Shanghai Univ, Sch Mech Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China;[4]Zhejiang A&F Univ, Sch Informat Engn, Dept Comp, Hangzhou, Zhejiang, Peoples R China

年份：2014

卷号：30

起止页码：213

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:000334139600020)、、EI(收录号：20141317506261)、Scopus(收录号：2-s2.0-84896388046)、WOS

基金：This material was supported in part by the National Science Foundation under Grant no. 0826124, the National Natural Science Foundation of China under Grant nos. 61305078, 61179041 and the Shaoxing City Public Technology Applied Research Project under Grant no. 2013B70004. The suggestions of the anonymous reviewers were helpful in improving this paper after its original submission to the journal.

语种：英文

外文关键词：Evolutionary computation; Hybrid algorithms; Biogeography-based optimization; Global optimization; Traveling salesman problems

外文摘要：Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EM. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community. (C) 2014 Elsevier Ltd. All rights reserved.