Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39486

TitleMOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood
Author(s)Denysiuk, Roman
Costa, L.
Espírito Santo, I. A. C. P.
KeywordsMultiobjective optimization
Evolutionary multiobjective optimization
Multiobjective evolutionary algorithms
Performance assessment
Issue date2015
PublisherAssociation for Computing Machinery (ACM)
Abstract(s)Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.
TypeConference paper
URIhttp://hdl.handle.net/1822/39486
ISBN978-1-4503-3472-3
DOI10.1145/2739480.2754722
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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