[1]YANG Zhenyu,TANG Ke.An overview of parameter control and adaptation strategiesin differential evolution algorithm[J].CAAI Transactions on Intelligent Systems,2011,6(5):415-423.
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An overview of parameter control and adaptation strategiesin differential evolution algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 5 Page number: 415-423 Column: 综述 Public date: 2011-10-30
Title:
An overview of parameter control and adaptation strategiesin differential evolution algorithm
Author(s):
YANG Zhenyu1;  TANG Ke2
1.Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China;
2.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
Keywords:
evolutionary computation;  differential evolution;  parameter control;  adaptation strategies;  selfadaptation
CLC:
TP18；O224
DOI:
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Abstract:
Differential evolution algorithms have gradually become one of the most popular types of stochastic search algorithms in the area of evolutionary computation. They have been successfully applied to solve various problems in realworld applications. Since their performance often depends heavily on the parameter settings, the design of parameter control and adaptation strategies is one of the current hot topics of research in differential evolution. Although numerous parameter control schemes have been proposed, systematic overviews and analysis are still lacking. In this paper, first the basic principles and operations of the differential evolution algorithm were briefly introduced, and then a detailed overview was provided on different parameter control and adaptation strategies by dividing them into the following four classes: empirical parameter settings, randomized parameter adaptation strategies, randomized parameter adaptation strategies with statistical learning, and parameter selfadaptation strategies. The overview emphasized the latter two classes. To study the efficacy of these parameter control and adaptation strategies, experiments with the background of realvalued function optimization were conducted to compare their efficiency and practicability further. The results showed that the parameter selfadaptation is one of the most effective strategies so far.
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