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On the Effect of Scalarising Norm Choice in a ParEGO implementation Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Weighted Stress Function Method for Multiobjective Evolutionary Algorithm Based on Decomposition

Authors : Roman Denysiuk, António Gaspar-Cunha

Published in: Evolutionary Multi-Criterion Optimization

Publisher: Springer International Publishing

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Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a well established state-of-the-art framework. Major concerns that must be addressed when applying MOEA/D are the choice of an appropriate scalarizing function and setting the values of main control parameters. This study suggests a weighted stress function method (WSFM) for fitness assignment in MOEA/D. WSFM establishes analogy between the stress-strain behavior of thermoplastic vulcanizates and scalarization of a multiobjective optimization problem. The experimental results suggest that the proposed approach is able to provide a faster convergence and a better performance of final approximation sets with respect to quality indicators when compared with traditional methods. The validity of the proposed approach is also demonstrated on engineering problems.

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previous chapter Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results
next chapter Timing the Decision Support for Real-World Many-Objective Optimization Problems
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Metadata
Title
Weighted Stress Function Method for Multiobjective Evolutionary Algorithm Based on Decomposition
Authors
Roman Denysiuk
António Gaspar-Cunha
Copyright Year
2017
Book
Evolutionary Multi-Criterion Optimization

Print ISBN: 978-3-319-54156-3

Electronic ISBN: 978-3-319-54157-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54157-0_13

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