A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems

We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information given a priori. We employ Kriging models to approximate expensive objective and c...

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Veröffentlicht in:Journal of global optimization Jg. 90; H. 2; S. 459 - 485
Hauptverfasser: Aghaei pour, Pouya, Hakanen, Jussi, Miettinen, Kaisa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2024
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Computer Science
Constraints
Evolutionary algorithms
Genetic algorithms
Mathematics
Mathematics and Statistics
Multiple objective analysis
Operations Research/Decision Theory
Optimization
Performance evaluation
Real Functions
Constraint handling
Multiple objectives
Surrogate-assisted optimization
A priori algorithms
Constrained problems
Model management
Computationally expensive problems
ISSN:0925-5001, 1573-2916
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Zusammenfassung:We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information given a priori. We employ Kriging models to approximate expensive objective and constraint functions, enabling us to introduce a new selection strategy that emphasizes the generation of feasible solutions throughout the optimization process. In our innovative model management, we perform expensive function evaluations to identify feasible solutions that best reflect the decision maker’s preferences provided before the process. To assess the performance of our proposed algorithm, we utilize two distinct parameterless performance indicators and compare them against existing algorithms from the literature using various real-world engineering and benchmark problems. Furthermore, we assemble new algorithms to analyze the effects of the selection strategy and the model management on the performance of the proposed algorithm. The results show that in most cases, our algorithm has a better performance than the assembled algorithms, especially when there is a restricted budget for expensive function evaluations.
Bibliographie:ObjectType-Article-1
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-024-01387-z