A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms

A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness...

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Published inApplications of Evolutionary Computation Vol. 10784; pp. 634 - 649
Main Authors Fukumoto, Hiroaki, Oyama, Akira
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Constrained multi-objective optimization problems
Constraint handling techniques
Multi-objective evolutionary algorithms
Online AccessGet full text
ISBN9783319775371
3319775375
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-77538-8_43

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Abstract A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness of the proposed framework, the performances of the combined algorithms of five CHTs and four MOEAs on eight constrained multi-objective optimization problems are investigated with the proposed framework. The experimental results show that the outperforming CHT can vary by constrained multi-objective optimization problems, as far as examined in this study.
AbstractList A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness of the proposed framework, the performances of the combined algorithms of five CHTs and four MOEAs on eight constrained multi-objective optimization problems are investigated with the proposed framework. The experimental results show that the outperforming CHT can vary by constrained multi-objective optimization problems, as far as examined in this study.
Author Oyama, Akira
Fukumoto, Hiroaki
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Sim, Kevin
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Snippet A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the...
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SubjectTerms Constrained multi-objective optimization problems
Constraint handling techniques
Multi-objective evolutionary algorithms
Title A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms
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