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Memetic Computing
Erschienen in: Memetic Computing 2/2015

01.06.2015 | Regular Research Paper

Many-objective optimization with corner-based search

verfasst von: Hélio Freire, P. B. de Moura Oliveira, E. J. Solteiro Pires, Maximino Bessa

Erschienen in: Memetic Computing | Ausgabe 2/2015

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Abstract

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.
Vorheriger Artikel An effective multiobjective approach for hard partitional clustering
Nächster Artikel A novel two-level particle swarm optimization approach for efficient multiple sequence alignment

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Metadaten
Titel
Many-objective optimization with corner-based search
verfasst von
Hélio Freire
P. B. de Moura Oliveira
E. J. Solteiro Pires
Maximino Bessa
Publikationsdatum
01.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 2/2015
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-015-0151-4

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