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
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Abstract	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.
AbstractList	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.
Author	Wang, Handing Jin, Yaochu He, Cheng Song, Zhenshou
Author_xml	– sequence: 1 givenname: Zhenshou orcidid: 0000-0003-3315-499X surname: Song fullname: Song, Zhenshou email: songzhenshou@gmail.com organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 2 givenname: Handing orcidid: 0000-0002-4805-3780 surname: Wang fullname: Wang, Handing email: hdwang@xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 3 givenname: Cheng orcidid: 0000-0003-4218-8454 surname: He fullname: He, Cheng email: chenghehust@gmail.com organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 4 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu email: yaochu.jin@surrey.ac.uk organization: Department of Computer Science, University of Surrey, Guildford, U.K
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SubjectTerms	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
Title	A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimization
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Volume	25
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