期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 183, 期 2, 页码 785-804出版社
ELSEVIER
DOI: 10.1016/j.ejor.2006.10.020
关键词
evolutionary computations; artificial intelligence; differential evolution; global optimization
Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) algorithm which eliminates the need for manual tuning of control parameters is empirically analyzed. The performance of SDE is investigated and compared with other well-known approaches. The experiments conducted show that SDE generally outperform other DE algorithms in all the benchmark functions. Moreover, the performance of SDE using the ring neighborhood topology is investigated. (c) 2006 Elsevier B.V. All rights reserved.
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