A1 Journal article (refereed)
A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems (2024)

Aghaei pour, P., Hakanen, J., & Miettinen, K. (2024). A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems. Journal of Global Optimization, 90, 459-485. https://doi.org/10.1007/s10898-024-01387-z

JYU authors or editors

Aghaei Pour, Pouya
Hakanen, Jussi
Miettinen, Kaisa

Publication details

All authors or editors: Aghaei pour, Pouya; Hakanen, Jussi; Miettinen, Kaisa

Journal or series: Journal of Global Optimization

ISSN: 0925-5001

eISSN: 1573-2916

Publication year: 2024

Publication date: 29/05/2024

Volume: 90

Pages range: 459-485

Publisher: Springer

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1007/s10898-024-01387-z

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/95574

Abstract

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.

Keywords: multi-objective optimisation; algorithms; evolutionary computation; kriging method

Free keywords: multiple objectives; model management; a priori algorithms; constraint handling; surrogate-assisted optimization; constrained problems; computationally expensive problems

Fields of science: