文章基本信息

标题：DEGSO: Hybrid Group Search Optimizer with Differential Evolution Operator
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作者：Yu Xie ; Chunxia Zhao ; Haofeng Zhang 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：6
页码：285-296
DOI：10.14257/ijsip.2014.7.6.24
出版社：SERSC
摘要：In standard group search optimizer (GSO) algorithm, scroungers will converge to the similar position if the producer cannot find a better position than the old one in a number of successive iterations and the group may suffer from the premature convergence. In this paper, a hybrid GSO with differential evolution (DE) operator named DEGSO is proposed to enhance the diversity of standard group search optimizer. In this method, the standard GSO algorithm and the DE operator alternate at the odd iterations and at the even iterations. The results of the experiments indicate that DEGSO is competitive to some other evolutionary computation (EA) algorithms
关键词：Group Search Optimizer (GSO); differential evolution (DE); evolutionary ; computation (EC); function optimization