Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks

The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network sc...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 51; no. 7; pp. 3752 - 3766
Main Authors: Zhao, Tian-Fang, Chen, Wei-Neng, Kwong, Sam, Gu, Tian-Long, Yuan, Hua-Qiang, Zhang, Jie, Zhang, Jun
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Combinatorial analysis
Complex networks
Complexity
Computer science
Cooperative coevolution (CC)
Decomposition
evolutionary algorithm (EA)
Evolutionary algorithms
Fitness
Genetic algorithms
networked system
Networks
Optimization
Resource allocation
Resource management
Solution space
spreading control
Viruses
Viruses (medical)
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.2975530