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2017 | OriginalPaper | Chapter

An Elite Archive-Based MOEA/D Algorithm

Authors : Qingling Zhu, Qiuzhen Lin, Jianyong Chen

Published in: Simulated Evolution and Learning

Publisher: Springer International Publishing

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Abstract

MOEA/D is a novel multiobjective evolutionary algorithm based on decomposition approach, which has attracted much attention in recent years. However, when tackling the problems with irregular (e.g., disconnected or degenerated) Pareto fronts (PFs), MOEA/D is found to be ineffective and inefficient, as uniformly distributed weight vectors used in decomposition approach cannot guarantee the even distribution of the optimal solutions on PFs. In this paper, an elite archive-based MOEA/D algorithm (ArchMOEA/D) is proposed to tackle the above problem. An external archive is used to store non-dominated solutions that help to spread the population diversity. Moreover, this external archive is evolved and used to compensate the search area that decomposition-based approaches cannot reach. The external archive and the main population cooperate with each other using Pareto- and decomposition-based techniques during the evolutionary process. Some experiments in solving benchmark problems with various properties have been used to verify the efficiency and effectiveness of ArchMOEA/D. Experimental results demonstrate the superior performance of ArchMOEA/D over other kinds of MOEA/D variants.

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Metadata
Title
An Elite Archive-Based MOEA/D Algorithm
Authors
Qingling Zhu
Qiuzhen Lin
Jianyong Chen
Copyright Year
2017
Book
Simulated Evolution and Learning

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

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

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68759-9_20

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