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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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 25; H. 6; S. 1013 - 1027
Hauptverfasser:	Song, Zhenshou, Wang, Handing, He, Cheng, Jin, Yaochu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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