A Binary Particle Swarm Optimizer With Priority Planning and Hierarchical Learning for Networked Epidemic Control
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Author(s)
Zhao, Tian-Fang
Chen, Wei-Neng
Liew, Alan Wee-Chung
Gu, Tianlong
Wu, Xiao-Kun
Zhang, Jun
Griffith University Author(s)
Year published
2019
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The control of epidemics taking place in complex networks has been an increasingly active topic in public health management. In this article, we propose an efficient networked epidemic control system, where a modified susceptible-exposed-infected-vigilant (SEIV) model is first built to simulate epidemic spreading. Then, different from existing continuous resource models which abstractly map resources to parameters of epidemic models, a concrete resource description model is built to simulate real-world goods/services and their allocation. Based on the two models, a cost-constraint subset selection problem in epidemic control ...
View more >The control of epidemics taking place in complex networks has been an increasingly active topic in public health management. In this article, we propose an efficient networked epidemic control system, where a modified susceptible-exposed-infected-vigilant (SEIV) model is first built to simulate epidemic spreading. Then, different from existing continuous resource models which abstractly map resources to parameters of epidemic models, a concrete resource description model is built to simulate real-world goods/services and their allocation. Based on the two models, a cost-constraint subset selection problem in epidemic control is identified. To solve the problem, a swarm-based stochastic optimization policy is proposed, where each particle in the swarm can determine its own solutions according to the guidance of its superior peers and historical searching experience of the whole swarm, without extra problem-relative information. Theoretical proof about system equilibrium is provided, which is consistent with experimental observations. The competitive performance of the proposed optimizer is validated by theoretical analysis and comparison experiments. Finally, an application case is provided to illustrate the practicability.
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View more >The control of epidemics taking place in complex networks has been an increasingly active topic in public health management. In this article, we propose an efficient networked epidemic control system, where a modified susceptible-exposed-infected-vigilant (SEIV) model is first built to simulate epidemic spreading. Then, different from existing continuous resource models which abstractly map resources to parameters of epidemic models, a concrete resource description model is built to simulate real-world goods/services and their allocation. Based on the two models, a cost-constraint subset selection problem in epidemic control is identified. To solve the problem, a swarm-based stochastic optimization policy is proposed, where each particle in the swarm can determine its own solutions according to the guidance of its superior peers and historical searching experience of the whole swarm, without extra problem-relative information. Theoretical proof about system equilibrium is provided, which is consistent with experimental observations. The competitive performance of the proposed optimizer is validated by theoretical analysis and comparison experiments. Finally, an application case is provided to illustrate the practicability.
View less >
Journal Title
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Copyright Statement
© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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This publication has been entered into Griffith Research Online as an Advanced Online Version
Subject
Software engineering