Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization

Genghui Li; Qingfu Zhang; Jianyong Sun; Zhonghua Han

doi:10.1109/CEC.2019.8790310

Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization

Genghui Li, Qingfu Zhang, Jianyong Sun, Zhonghua Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Scopus citations

Abstract

The surrogate-assisted optimization algorithms (SAOAs) are very promising for solving computationally expensive optimization problems (EOPs). Generally, the performance of a SAOA is determined by the quality of its surrogate model and the infill sampling criterion. In this paper, we propose a radial basis function (RBF) assisted optimization algorithm with batch infill sampling criterion for solving EOPs (short for RBFBS). In RBFBS, the quality of RBF model is adjusted by choosing a good shape parameter via solving a sub-expensive hyperparameter optimization problem. Moreover, a batch infill sampling criterion that includes a bi-objective-based sampling approach and a single-objective-based sampling approach is proposed to get a batch of samples for expensive evaluation. The experimental results on various benchmark problems show that RBFBS is very promising for expensive optimization.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1664-1671
Number of pages	8
ISBN (Electronic)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8790310
State	Published - Jun 2019
Externally published	Yes
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019

Publication series

Name	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19

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10.1109/CEC.2019.8790310

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Li, G., Zhang, Q., Sun, J., & Han, Z. (2019). Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 1664-1671). Article 8790310 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790310

Li, Genghui ; Zhang, Qingfu ; Sun, Jianyong et al. / Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1664-1671 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

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title = "Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization",

abstract = "The surrogate-assisted optimization algorithms (SAOAs) are very promising for solving computationally expensive optimization problems (EOPs). Generally, the performance of a SAOA is determined by the quality of its surrogate model and the infill sampling criterion. In this paper, we propose a radial basis function (RBF) assisted optimization algorithm with batch infill sampling criterion for solving EOPs (short for RBFBS). In RBFBS, the quality of RBF model is adjusted by choosing a good shape parameter via solving a sub-expensive hyperparameter optimization problem. Moreover, a batch infill sampling criterion that includes a bi-objective-based sampling approach and a single-objective-based sampling approach is proposed to get a batch of samples for expensive evaluation. The experimental results on various benchmark problems show that RBFBS is very promising for expensive optimization.",

author = "Genghui Li and Qingfu Zhang and Jianyong Sun and Zhonghua Han",

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year = "2019",

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Li, G, Zhang, Q, Sun, J & Han, Z 2019, Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8790310, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1664-1671, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8790310

Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization. / Li, Genghui; Zhang, Qingfu; Sun, Jianyong et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1664-1671 8790310 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Li G, Zhang Q, Sun J, Han Z. Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1664-1671. 8790310. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). doi: 10.1109/CEC.2019.8790310

Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization

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