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

Tian-Fang ZHAO, Wei-Neng CHEN, Sam KWONG, Tian-Long GU, Hua-Qiang YUAN, Jie ZHANG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

16 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)3752-3766
JournalIEEE Transactions on Cybernetics
Issue number7
Early online date13 Mar 2020
Publication statusPublished - Jul 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61976093 and Grant 61876111, in part by the Science and Technology Plan Project of Guangdong Province under Grant 2018B050502006, and in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003. The work of Tian-Fang Zhao and Wei-Neng Chen was supported by the Guangdong-Hong Kong Joint Innovation Platform of Big Data and Computational Intelligence under Grant 2018B050502006.


  • Cooperative coevolution (CC)
  • evolutionary algorithm (EA)
  • networked system
  • resource allocation
  • spreading control


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