基于聚类的昂贵多目标优化代理辅助进化算法

doi:10.15960/j.cnki.issn.1007-6093.2022.04.003

Abstract

Abstract: A clustering-based surrogate-assisted evolutionary algorithm is proposed for computationally expensive multi-objective optimization problems. Under the framework of MOEA/D, the population is partitioned into several clusters, and the population subsets are formed via the neighbourhood of the weights. Then the radial basis function surrogate-assisted differential evolution algorithm is used to generate new solution points from the formed subsets, and the population is updated using the generated new solution. Numerical experiments have been undertaken on 7 DTLZ test problems, and the computational results indicate that the proposed evolutionary algorithm has advantages over the newly developed multi-objective neighborhood regression optimization (MONRO) algorithm.

Key words: multi-objective optimization, surrogate-assisted evolutionary algorithm, radial basis function, clustering

CLC Number:

BAI Fusheng, CHEN Jiaoling. A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization[J]. Operations Research Transactions, 2022, 26(4): 31-42.

References

[1] Zhou A, Qu B Y, Li H, et al. Multiobjective evolutionary algorithms: A survey of the state of the art[J]. Swarm and Evolutionary Computation, 2011, 1(1): 32-49.
[2] Zhang Q, Li H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731.
[3] Jin Y. Surrogate-assisted evolutionary computation: Recent advances and future challenges[J]. Swarm and Evolutionary Computation, 2011, 1(2): 61-70.
[4] Chugh T, Sindhya K, Hakanen J, et al. A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms[J]. Soft Computing, 2019, 23: 3137-3166.
[5] Müller J, Day M. Surrogate optimization of computationally expensive black-box problems with hidden constraints[J]. INFORMS Journal on Computing, 2019, 31(4): 689-702.
[6] Chen Z, Zhou Y, He X. Handling expensive multi-objective optimization problems with a cluster-based neighborhood regression model[J]. Applied Soft Computing, 2019, 80: 211-225.
[7] Storn R, Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
[8] Gutmann H M. A radial basis function method for global optimization[J]. Journal of Global Optimization, 2001, 19(3): 201-227.
[9] Gutmann H M. On the semi-norm of radial basis function interpolants[J]. Journal of Approximation Theory, 2001, 111: 315-328.
[10] Wild S M, Shoemaker C. Global convergenve of radial basis function trust region derivative-free algorithms[J]. Society for Industrial and Applied Mathematics, 2011, 21(3): 761-781.
[11] Powell M J D. The theory of radial basis function approximation in 1990[M]//Advances in Numerical Analysis, Volume 2: Wavelets, Subdivision Algorithms and Radial Basis Functions, Oxford: Oxford University Press, 1992, 105-210.
[12] 张春美. 差分进化算法理论与应用[M]. 北京: 北京理工大学出版社, 2014.
[13] 丁青锋, 尹晓宇. 差分进化算法综述[J]. 智能系统学报, 2017, 12(4): 431-442.
[14] 周志华. 机器学习[M]. 北京:清华大学出版社, 2016.
[15] Deb K, Agrawal R. Simulated binary crossover for continuous search space[J]. Complex Systems, 1995, 9(2): 115-148.
[16] Deb K, Goyal M. A combined genetic adaptive search (GeneAS) for engineering design[J]. Computer Science and Informatics, 1996, 26: 30-45.
[17] Deb K, Thiele L, Laumanns M, et al. Scalable multi-objective optimization test problems[C]//Proceedings of the 2002 Congress on Evolutionary Computation, 2002: 825-830.
[18] Bosman P, Thierens D. The balance between proximity and diversity in multiobjective evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(2): 174-188.
[19] Deb K, Jain S. Running performance metrics for evolutionary multi-objective optimization[R]. KanGAL Report No. 2002004, Kanpur: Indian Institute of Technology, 2002.
[20] 郑金华, 邹娟. 多目标进化优化[M]. 北京:科学出版社, 2017.
[21] Tian Y, Cheng R, Zhang X, et al. PlatEMO: A MATLAB platform for evolutionary multiobjective optimization[J]. IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87.
[22] Wilcoxon F. Individual comparisons by ranking methods[J]. Biometrics Bulletin, 1945, 1(6): 80-83.

A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 6

Recommended Articles

Metrics

Comments

[1]	Yubo DAI, Yihong DUAN, Longcheng LIU, Zihao WANG. Randomized approximation algorithms for a class of one-dimensional online unit clustering problem [J]. Operations Research Transactions, 2022, 26(3): 143-150.
[2]	Fusheng BAI, Dan FENG, Ke ZHANG. Combined response surface method with adaptive sampling for expensive black-box global optimization [J]. Operations Research Transactions, 2021, 25(2): 1-14.
[3]	YU Ke, ZHANG Xiaoqing, ZHAO Kequan. Multi-objective optimization approach of the optimal performance allocation [J]. Operations Research Transactions, 2018, 22(4): 89-98.
[4]	WANG Yishui, XU Dachuan, WU Chenchen. A semidefinite programming rounding algorithm for correlation clustering problem [J]. Operations Research Transactions, 2018, 22(1): 67-76.
[5]	XU Dachuan, XU Yicheng, ZHANG Dongmei. A survey on algorithms for k-means problem and its variants [J]. Operations Research Transactions, 2017, 21(2): 101-109.
[6]	MU Lifeng, CAO Yan. A multi-objective method for supplier pre-selection of platform-based products [J]. Operations Research Transactions, 2015, 19(1): 31-44.