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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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 4; pp. 1 - 13
Main Authors: 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
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3212045