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Soft Computing
Erschienen in: Soft Computing 7/2019

25.11.2017 | Methodologies and Application

An effective improved differential evolution algorithm to solve constrained optimization problems

verfasst von: Xiaobing Yu, Yiqun Lu, Xuming Wang, Xiang Luo, Mei Cai

Erschienen in: Soft Computing | Ausgabe 7/2019

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Abstract

An effective extended differential evolution algorithm is proposed to deal with constrained optimization problems. The proposed algorithm adopts a new mechanism to cope with constrained problems by transforming the equality into inequality first. Then, two kinds of offspring generation approaches are applied to balance the diversity and the convergence speed of the population during evolution, and seven criteria are designed to compare feasible solution over infeasible solution. The performance of the novel algorithm is evaluated on a set of well-known constrained problems from CEC2006. The experimental results are quite competitive when comparing the proposed algorithm against state-of-the-art optimization algorithms.
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Metadaten
Titel
An effective improved differential evolution algorithm to solve constrained optimization problems
verfasst von
Xiaobing Yu
Yiqun Lu
Xuming Wang
Xiang Luo
Mei Cai
Publikationsdatum
25.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 7/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2936-5

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