A parameter adaptive DE algorithm on real-parameter optimization

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 38; no. 5; pp. 5775 - 5786
Main Authors Pan, Jeng-Shyang, Yang, Cheng, Meng, Fanjia, Chen, Yuxin, Meng, Zhenyu
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2020
Subjects
Adaptive algorithms
Adaptive control
Algorithms
Evolutionary algorithms
Evolutionary computation
Floating point arithmetic
Global optimization
Mutation
Optimization
Parameters
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Summary:Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-179665