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

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

23 Citations (Scopus)

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)
Publisher	IEEE
Pages	1664-1671
ISBN (Electronic)	978-1-7281-2153-6
ISBN (Print)	978-1-7281-2154-3
DOIs	https://doi.org/10.1109/CEC.2019.8790310
Publication status	Published - Jun 2019
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019 http://cec2019.org/index.html http://cec2019.org/assets/downloads/IEEE_CEC_2019_Program.pdf

Publication series

Name	Congress on Evolutionary Computation
Publisher	IEEE

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19
Internet address	http://cec2019.org/index.html http://cec2019.org/assets/downloads/IEEE_CEC_2019_Program.pdf

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10.1109/CEC.2019.8790310

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@inproceedings{9b888882c2f94331abd4de62b3bb31ed,

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",

year = "2019",

month = jun,

doi = "10.1109/CEC.2019.8790310",

language = "English",

isbn = "978-1-7281-2154-3",

series = "Congress on Evolutionary Computation",

publisher = "IEEE",

pages = "1664--1671",

booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",

address = "United States",

note = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 ; Conference date: 10-06-2019 Through 13-06-2019",

url = "http://cec2019.org/index.html, http://cec2019.org/assets/downloads/IEEE_CEC_2019_Program.pdf",

}

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)., 8790310, Congress on Evolutionary Computation, IEEE, 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). IEEE, 2019. p. 1664-1671 8790310 (Congress on Evolutionary Computation).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization

AU - Li, Genghui

AU - Zhang, Qingfu

AU - Sun, Jianyong

AU - Han, Zhonghua

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

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T3 - Congress on Evolutionary Computation

SP - 1664

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

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