Graph-Based Deep Decomposition for Overlapping Large-Scale Optimization Problems

Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some well-performing decomposition methods have been designed based on the interactions among variables (IaV), their grouping accuracy is still limited...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 53; H. 4; S. 1 - 13
Hauptverfasser:	Zhang, Xin, Ding, Bo-Wen, Xu, Xin-Xin, Li, Jian-Yu, Zhan, Zhi-Hui, Qian, Pengjiang, Fang, Wei, Lai, Kuei-Kuei, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Computer science Cooperative co-evolutionary algorithms (CCEAs) Decomposition decomposition methods Evolutionary algorithms evolutionary computation Fault tolerance Fault tolerant systems Function generators large-scale optimization problems (LSOPs) Optimization Particle separators Research and development Roundoff error Roundoff errors Signal generators
ISSN:	2168-2216, 2168-2232
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Abstract	Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some well-performing decomposition methods have been designed based on the interactions among variables (IaV), their grouping accuracy is still limited due to the poor performance on the overlapping problems and the computational roundoff errors of IaV in the implementation. To deal with these limitations, a graph-based deep decomposition (GDD) method is proposed to obtain more accurate grouping results, especially for the overlapping problems. On the one hand, the GDD mines the IaV information and obtains the minimum vertex separator of the interaction graph of variables, so as to group variables deeply and recursively. On the other hand, the GDD has the ability of fault tolerance to deal with the computational roundoff errors of IaV and can improve the grouping accuracy. For better experimental studies of overlapping problems, a novel overlapping function generator is designed with the random and complicate overlap type, and two new metrics are proposed to evaluate the grouping accuracy. Comprehensive experiments show that GDD can greatly improve the grouping accuracy and help CCEAs perform better than other existing algorithms, especially on the overlapping problems. In addition, the GDD is highly fault tolerant and can divide problems accurately even on the inaccurate IaV.
AbstractList	Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some well-performing decomposition methods have been designed based on the interactions among variables (IaV), their grouping accuracy is still limited due to the poor performance on the overlapping problems and the computational roundoff errors of IaV in the implementation. To deal with these limitations, a graph-based deep decomposition (GDD) method is proposed to obtain more accurate grouping results, especially for the overlapping problems. On the one hand, the GDD mines the IaV information and obtains the minimum vertex separator of the interaction graph of variables, so as to group variables deeply and recursively. On the other hand, the GDD has the ability of fault tolerance to deal with the computational roundoff errors of IaV and can improve the grouping accuracy. For better experimental studies of overlapping problems, a novel overlapping function generator is designed with the random and complicate overlap type, and two new metrics are proposed to evaluate the grouping accuracy. Comprehensive experiments show that GDD can greatly improve the grouping accuracy and help CCEAs perform better than other existing algorithms, especially on the overlapping problems. In addition, the GDD is highly fault tolerant and can divide problems accurately even on the inaccurate IaV.
Author	Zhang, Xin Ding, Bo-Wen Xu, Xin-Xin Fang, Wei Qian, Pengjiang Lai, Kuei-Kuei Zhan, Zhi-Hui Li, Jian-Yu Zhang, Jun
Author_xml	– sequence: 1 givenname: Xin orcidid: 0000-0003-3636-6453 surname: Zhang fullname: Zhang, Xin organization: School of Artificial Intelligence and Computer Science and the Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China – sequence: 2 givenname: Bo-Wen surname: Ding fullname: Ding, Bo-Wen organization: School of Artificial Intelligence and Computer Science and the Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China – sequence: 3 givenname: Xin-Xin surname: Xu fullname: Xu, Xin-Xin organization: School of Computer Science and Technology, Ocean University of China, Qingdao, China – sequence: 4 givenname: Jian-Yu orcidid: 0000-0002-6143-9207 surname: Li fullname: Li, Jian-Yu organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 5 givenname: Zhi-Hui orcidid: 0000-0003-0862-0514 surname: Zhan fullname: Zhan, Zhi-Hui organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 6 givenname: Pengjiang orcidid: 0000-0002-5596-3694 surname: Qian fullname: Qian, Pengjiang organization: School of Artificial Intelligence and Computer Science and the Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China – sequence: 7 givenname: Wei orcidid: 0000-0001-8052-0994 surname: Fang fullname: Fang, Wei organization: School of Artificial Intelligence and Computer Science and the Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China – sequence: 8 givenname: Kuei-Kuei orcidid: 0000-0001-6049-1161 surname: Lai fullname: Lai, Kuei-Kuei organization: Department of Business Administration, Chaoyang University of Technology, Taichung City, Taiwan – sequence: 9 givenname: Jun orcidid: 0000-0001-7835-9871 surname: Zhang fullname: Zhang, Jun organization: Zhejiang Normal University, Jinhua, China
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Snippet	Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some...
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SubjectTerms	Accuracy Algorithms Computer science Cooperative co-evolutionary algorithms (CCEAs) Decomposition decomposition methods Evolutionary algorithms evolutionary computation Fault tolerance Fault tolerant systems Function generators large-scale optimization problems (LSOPs) Optimization Particle separators Research and development Roundoff error Roundoff errors Signal generators
Title	Graph-Based Deep Decomposition for Overlapping Large-Scale Optimization Problems
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