A surrogate-assisted variable grouping algorithm for general large-scale global optimization problems

被引:1
|
作者
Chen, An [1 ]
Ren, Zhigang [1 ]
Wang, Muyi [1 ]
Liang, Yongsheng [1 ]
Liu, Hanqing [1 ]
Du, Wenhao [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Automat Sci & Engn, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
Cooperative coevolution; Problem decomposition; Surrogate model; Large-scale global optimization; DIFFERENTIAL EVOLUTION; DECOMPOSITION METHOD; COEVOLUTION;
D O I
10.1016/j.ins.2022.11.117
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Problem decomposition plays an important role when applying cooperative coevolution (CC) to large-scale global optimization problems. However, most learning-based decompo-sition algorithms only apply to additively separable problems, while the others insensitive to problem type perform low decomposition accuracy and efficiency. Given this limitation, this study designs a general-separability-oriented detection criterion, and further proposes a novel decomposition algorithm called surrogate-assisted variable grouping (SVG). The new criterion detects the separability between a variable and some other variables by checking whether its optimum changes with the latter. Consistent with the definition of general separability, this criterion endows SVG with strong applicability and high accuracy. To reduce expensive fitness evaluations, SVG locates the optimum of a variable with the help of a surrogate model rather than the original high-dimensional model. Moreover, it converts the variable-grouping process into a search process in a binary tree by taking vari-able subsets as tree nodes. This facilitates the reutilization of historical separability infor-mation, thereby reducing separability detection times. Experimental results on a general benchmark suite indicate that compared with six state-of-the-art decomposition algo-rithms, SVG achieves higher accuracy and efficiency on both additively and nonadditively separable problems. Furthermore, it can significantly enhance the optimization perfor-mance of CC.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:437 / 455
页数:19
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