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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2016

01.08.2016 | Original Article

An improved reference point based multi-objective optimization by decomposition

verfasst von: Huazheng Zhu, Zhongshi He, Yuanyuan Jia

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2016

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Abstract

Reference point based multi-objective evolutionary algorithm by decomposition (RMEAD) considers reference points as users’ preferences. RMEAD not only focuses on searching the region of interest to find a set of preferred solutions, but also economizes a significant amount of computing resources. However, the base weight vectors in RMEAD may not be well estimated when confronting to hard multi-objective optimization problems. This paper modifies RMEAD to improve its performance on two aspects: firstly, a novel and simple approach to finding the base weight vectors is developed, the correctness of which is proved mathematically; secondly, a new updating weight vectors method is proposed. Abundant experiments show that the improved RMEAD (IRMEAD) could obtain significantly better results than RMEAD on all the test cases in terms of convergence and diversity. Besides, compared with recent proposed preference-based approach MOEA/D-PRE, IRMEAD outperforms it on most of the test instances.
Vorheriger Artikel Certain types of fuzzy sets in a fuzzy graph
Nächster Artikel Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition

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Metadaten
Titel
An improved reference point based multi-objective optimization by decomposition
verfasst von
Huazheng Zhu
Zhongshi He
Yuanyuan Jia
Publikationsdatum
01.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0443-5

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