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Soft Computing

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07-02-2018 | Methodologies and Application

Adaptive differential evolution with multi-population-based mutation operators for constrained optimization

Authors: Bin Xu, Lili Tao, Xu Chen, Wushan Cheng

Published in: Soft Computing | Issue 10/2019

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Abstract

Constrained optimization problems (COPs) are most commonly encountered problems in science and engineering design field. To solve such kind of problem effectively, in this paper, we put forward a new approach named CAMDE which integrates adaptive differential evolution (DE) with new multi-population-based mutation operators. In CAMDE, some inferior solutions with low objective values are maintained in an external population. During mutation process, this external population is combined with main population to generate promising progress directions toward optimal region. Furthermore, DE’s control parameters F and CR are adaptively adjusted according to the statistical information learnt from the previous searches in generating improved solutions. The advantageous performance of CAMDE is validated by comparisons with some representatives of state of the art in constrained optimization over 24 test instances. Moreover, four widely used constrained mechanical engineering problems are selected to validate the search ability of CAMDE for real-world problems. The performance results show that CAMDE is an effective approach to solving COPs, which is basically enabled by the integration of multi-population-based mutation operators and adaptive control strategy for DE’s control parameters.

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We have made some experiments using different \(\mu _{F}\) and \(\mu _{\mathrm{CR}}\) values and found large values can achieve more comparative results. Therefore, to speed up the search process, we use \(\mu _{F}=0.7\) and \(\mu _{\mathrm{CR}}=0.8\) in CAMDE. Moreover, the discussion in Sect. 6.2 also supports that large F and CR are more suitable for solving COPs when our new mutation operators are used.

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Title: Adaptive differential evolution with multi-population-based mutation operators for constrained optimization
Authors: Bin Xu
Lili Tao
Xu Chen
Wushan Cheng
Publication date: 07-02-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 10/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-3001-0

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