Journal of System Simulation
Abstract
Abstract: According to the principle of simplifying a complex problem into sub-problems for solution, a resource allocation method for virus spreading control based on multi-granularity cooperative coevolution (MGCC) was proposed. According to the characteristics of human's social network structures, MGCC decomposed the network into sub-networks with different scales according to different decomposition granularities. A contribution-based decomposition granularity selection strategy was proposed. Historical archives were used to record the contribution of different decomposition granularities to optimization, and the appropriate decomposition granularity was selected according to the optimization status. A projection-based constraint repairing strategy was designed to ensure the feasibility of solutions. The results show that MGCC can decompose complex social network structures and ensure resource allocation by combining different evolutionary operators, improving the effectiveness of evolutionary operators in solving the resource allocation problem for virus spreading control.
Recommended Citation
Shi, Xuanli; Chen, Weineng; Song, An; and Zhao, Tianfang
(2025)
"Resource Allocation Method for Virus Spreading Control Based on Multi-granularity Cooperative Coevolution,"
Journal of System Simulation: Vol. 37:
Iss.
8, Article 12.
DOI: 10.16182/j.issn1004731x.joss.24-0315
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/12
First Page
2043
Last Page
2060
CLC
TP391.9
Recommended Citation
Shi Xuanli, Chen Weineng, Song An, et al. Resource Allocation Method for Virus Spreading Control Based on Multi-granularity Cooperative Coevolution[J]. Journal of System Simulation, 2025, 37(8): 2043-2060.
DOI
10.16182/j.issn1004731x.joss.24-0315
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons