Artikel
A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
- Sprache
-
Englisch
- Erschienen in
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Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-14 ; Amsterdam: Elsevier
- Klassifikation
-
Wirtschaft
- Thema
-
Optimization via simulation
Metamodel
Multi-Objective optimization
Kriging
NSGA-II
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Baquela, Enrique Gabriel
Olivera, Ana Carolina
- Ereignis
-
Veröffentlichung
- (wer)
-
Elsevier
- (wo)
-
Amsterdam
- (wann)
-
2019
- DOI
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doi:10.1016/j.orp.2019.100098
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Baquela, Enrique Gabriel
- Olivera, Ana Carolina
- Elsevier
Entstanden
- 2019
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