An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the...

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Published in:	IEEE transactions on evolutionary computation Vol. 26; no. 4; pp. 631 - 645
Main Authors:	Lin, Qiuzhen, Wu, Xunfeng, Ma, Lijia, Li, Jianqiang, Gong, Maoguo, Coello, Carlos A. Coello
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Computational modeling Ensemble surrogate Evolutionary algorithms Evolutionary computation Genetic algorithms Model accuracy model management multiobjective optimization Multiple objective analysis Optimization Predictive models Sorting algorithms Space exploration Subspaces Training Uncertainty
ISSN:	1089-778X, 1941-0026
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Abstract	Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases.
AbstractList	Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases.
Author	Ma, Lijia Li, Jianqiang Gong, Maoguo Wu, Xunfeng Coello, Carlos A. Coello Lin, Qiuzhen
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Cites_doi	10.1109/TEVC.2016.2555315 10.1007/s00158-006-0051-9 10.1007/s00500-017-2965-0 10.1109/TEVC.2008.925798 10.1109/CEC.2013.6557876 10.1109/TEVC.2005.861417 10.1109/TSMCC.2005.855506 10.1007/978-3-030-18764-4_8 10.1007/978-3-030-18764-4_10 10.1109/TEVC.2016.2622301 10.1109/TCYB.2018.2794503 10.1109/TEVC.2019.2925959 10.1007/978-3-540-24694-7_71 10.1109/TEVC.2017.2783441 10.1142/9789813148239_0005 10.1016/j.knosys.2020.106520 10.1109/CEC.2018.8477766 10.1016/j.swevo.2011.05.001 10.1023/A:1008202821328 10.1109/TCYB.2018.2811761 10.1109/TCYB.2018.2809430 10.1109/TEVC.2016.2519378 10.1016/j.ejor.2006.08.008 10.1109/TEVC.2018.2802784 10.1016/j.envsoft.2018.11.018 10.1080/01605682.2018.1468860 10.1109/CEC.2002.1007032 10.1145/3071178.3071276 10.1007/978-3-540-76931-6_23 10.1109/TEVC.2013.2281535 10.1080/00401706.2000.10485979 10.1007/s00158-013-1029-z 10.1109/TMTT.2016.2583427 10.1137/S1052623496307510 10.1109/TEVC.2005.859463 10.1016/j.knosys.2020.106418 10.1109/MCI.2017.2742868 10.1016/j.swevo.2019.03.005 10.1016/j.asoc.2012.02.025 10.1109/TEVC.2009.2027359 10.1109/TEVC.2005.851274 10.1109/TEVC.2009.2033671 10.1007/s40747-017-0039-7 10.1109/TEVC.2013.2281533 10.1007/978-3-642-17298-4_24 10.2514/1.12994 10.1109/TEVC.2018.2869001 10.1016/j.ins.2020.01.048 10.1109/TEVC.2017.2693320
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References	ref57 ref13 ref56 ref12 ref15 ref58 ref14 chugh (ref40) 2019; 23 ref52 ref11 ref54 ref17 ref16 ref18 ref50 ref45 ref48 ref47 ref42 ref41 ref44 ref43 deb (ref51) 1995; 9 ref49 deb (ref46) 2021; 26 ref8 guo (ref10) 2016 ref9 ref3 ref6 ref5 martínez (ref19) 2013 zhang (ref55) 2008 ref35 deb (ref1) 2001 ref34 ref37 ref36 zitzler (ref4) 2001 ref31 jin (ref27) 2019; 49 ref30 ref33 ref32 ref2 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref29 zitzler (ref7) 2004 deb (ref53) 1996; 26
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SubjectTerms	Computational modeling Ensemble surrogate Evolutionary algorithms Evolutionary computation Genetic algorithms Model accuracy model management multiobjective optimization Multiple objective analysis Optimization Predictive models Sorting algorithms Space exploration Subspaces Training Uncertainty
Title	An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
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