Merged Differential Grouping for Large-scale Global Optimization

Ma，Xiaoliang1; Huang，Zhitao2; Li，Xiaodong3; Wang，Lei4; Qi，Yutao5; Zhu，Zexuan6

2022

10.1109/TEVC.2022.3144684

IEEE Transactions on Evolutionary Computation   影响因子和分区

1941-0026

1941-0026

The divide-and-conquer strategy has been widely used in cooperative co-evolutionary algorithms to deal with large-scale global optimization problems, where a target problem is decomposed into a set of lower-dimensional and tractable sub -problems to reduce the problem complexity. However, such a strategy usually demands a large number of function evaluations to obtain an accurate variable grouping. To address this issue, a merged differential grouping (MDG) method is proposed in this article based on the subset-subset interaction and binary search. In the proposed method, each variable is first identified as either a separable variable or a nonseparable variable. Afterward, all separable variables are put into the same subset, and the non-separable variables are divided into multiple subsets using a binary-tree-based iterative merging method. With the proposed algorithm, the computational complexity of interaction detection is reduced to O(max{n, n(ns) x log(2) k}), where n, n(ns)(<= n), and k(< n) indicate the numbers of decision variables, nonseparable variables, and subsets of nonseparable variables, respectively. The experimental results on benchmark problems show that MDG is very competitive with the other state-of-the-art methods in termsof efficiency and accuracy of problem decomposition.

Cooperative co-evolution large-scale global optimization differential grouping problem decomposition.

EI ; SCI

英语

其他

National Natural Science Foundation of China["61976143","61871272","61772392","61975135"] ; International Cooperation and Exchanges NSFC[61911530218] ; Guangdong Basic and Applied Basic Research Foundation["2019A1515010869","2020A1515010946","2021A1515012637"] ; Shenzhen Fundamental Research Program[JCYJ20190808173617147] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Scientific Research Foundation of Shenzhen University[860/2110312] ; ARC[DP190101271] ; Science Basic Research Plan in Shaanxi Province of China[2018JM6009] ; BGI-Research Shenzhen Open Funds[BGIRSZ20200002]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000892933300020

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

COMPUTER SCIENCE

IEEE

1.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. (e-mail: maxiaoliang@yeah.net)
2.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
3.School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, VIC 3001, Australia.
4.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China.
5.School of Computer Science and Technology, Xidian University, Xi’an, 710071, China.
6.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China, also with Shenzhen Pengcheng Laboratory, Shenzhen 518055, China, and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen 518055.

Ma，Xiaoliang,Huang，Zhitao,Li，Xiaodong,et al. Merged Differential Grouping for Large-scale Global Optimization[J]. IEEE Transactions on Evolutionary Computation,2022,PP(99):1-1.

Ma，Xiaoliang,Huang，Zhitao,Li，Xiaodong,Wang，Lei,Qi，Yutao,&Zhu，Zexuan.(2022).Merged Differential Grouping for Large-scale Global Optimization.IEEE Transactions on Evolutionary Computation,PP(99),1-1.

Ma，Xiaoliang,et al."Merged Differential Grouping for Large-scale Global Optimization".IEEE Transactions on Evolutionary Computation PP.99(2022):1-1.