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Soft Computing

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27.07.2016 | Methodologies and Application

Many-objective optimization with dynamic constraint handling for constrained optimization problems

verfasst von: Xi Li, Sanyou Zeng, Changhe Li, Jiantao Ma

Erschienen in: Soft Computing | Ausgabe 24/2017

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Abstract

In real-world applications, the optimization problems are usually subject to various constraints. To solve constrained optimization problems (COPs), this paper presents a new methodology, which incorporates a dynamic constraint handling mechanism into many-objective evolutionary optimization. Firstly we convert a COP into a dynamic constrained many-objective optimization problem (DCMaOP), which is equivalent to the COP, then the proposed many-objective optimization evolutionary algorithm with dynamic constraint handling, called MaDC, is realized to solve the DCMaOP. MaDC uses the differential evolution (DE) to generate individuals, and a reference-point-based nondominated sorting approach to select individuals. The effectiveness of MaDC is verified on 22 test instances. The experimental results show that MaDC is competitive to several state-of-the-art algorithms, and it has better global search ability than its peer algorithms.

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Titel: Many-objective optimization with dynamic constraint handling for constrained optimization problems
verfasst von: Xi Li
Sanyou Zeng
Changhe Li
Jiantao Ma
Publikationsdatum: 27.07.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 24/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2286-8

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