A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimization

Only a small number of function evaluations can be afforded in many real-world multiobjective optimization problems (MOPs) where the function evaluations are economically/computationally expensive. Such problems pose great challenges to most existing multiobjective evolutionary algorithms (EAs), whi...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 25; číslo 6; s. 1013 - 1027
Hlavní autoři: Song, Zhenshou, Wang, Handing, He, Cheng, Jin, Yaochu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Adaptive sampling strategy
Archives & records
Computational modeling
Convergence
Data models
Evolutionary algorithms
evolutionary algorithms (EAs)
Evolutionary computation
expensive multiobjective optimization
Genetic algorithms
Kriging
Mopping
Multiple objective analysis
Optimization
Predictive models
Sampling
surrogate assisted
Uncertainty
ISSN:1089-778X, 1941-0026
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Shrnutí:Only a small number of function evaluations can be afforded in many real-world multiobjective optimization problems (MOPs) where the function evaluations are economically/computationally expensive. Such problems pose great challenges to most existing multiobjective evolutionary algorithms (EAs), which require a large number of function evaluations for optimization. Surrogate-assisted EAs (SAEAs) have been employed to solve expensive MOPs. Specifically, a certain number of expensive function evaluations are used to build computationally cheap surrogate models for assisting the optimization process without conducting expensive function evaluations. The infill sampling criteria in most existing SAEAs take all requirements on convergence, diversity, and model uncertainty into account, which is, however, not the most efficient in exploiting the limited computational budget. Thus, this article proposes a Kriging-assisted two-archive EA for expensive many-objective optimization. The proposed algorithm uses one influential point-insensitive model to approximate each objective function. Moreover, an adaptive infill criterion that identifies the most important requirement on convergence, diversity, or uncertainty is proposed to determine an appropriate sampling strategy for reevaluations using the expensive objective functions. The experimental results on a set of expensive multi/many-objective test problems have demonstrated its superiority over five state-of-the-art SAEAs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3073648