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

• Published in: Journal of global optimization Vol. 90; no. 2; pp. 459 - 485
• Main Authors: Aghaei pour, Pouya, Hakanen, Jussi, Miettinen, Kaisa
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer; Springer Nature B.V
• Subjects: 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; 1573-2916
• DOI: 10.1007/s10898-024-01387-z

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.