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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems

verfasst von : Xueqiang Li, Shen Fu, Han Huang

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

Constrained multiobjective optimization problem (CMOP) is an important research topic in the field of evolutionary computation. In terms of constraint handling, most of the existing evolutionary algorithms consider more about the proportion of infeasible solutions in population, but less concern about the distribution of infeasible solutions. Therefore, we propose a constraint partitioning method based on minimax strategy (CPM/MS) to solve CMOP. Firstly, we analyze the impact of the distribution of infeasible solutions on selecting solutions and give a preconditioning method for infeasible solutions. Secondly, we divide the preconditioned solutions into different regions by minimax strategy. Finally, we update individuals based on feasibility criteria method in each region. The effectiveness of CPM/MS algorithm is extensively evaluated on a suite of 10 bound-constrained numerical optimization problems, where the results show that CPM/MS algorithm is able to obtain considerably better fronts for some of the problems compared with some the state-of-the-art multiobjective evolutionary algorithms.

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Vorheriges Kapitel An Elite Archive-Based MOEA/D Algorithm
Nächstes Kapitel A Fast Objective Reduction Algorithm Based on Dominance Structure for Many Objective Optimization
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Metadaten
Titel
A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems
verfasst von
Xueqiang Li
Shen Fu
Han Huang
Copyright-Jahr
2017
Buch
Simulated Evolution and Learning

Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68759-9_21