A Binary Particle Swarm Optimizer With Priority Planning and Hierarchical Learning for Networked Epidemic Control Article Swipe
Tian-Fang Zhao
,
Wei‐Neng Chen
,
Alan Wee‐Chung Liew
,
Tianlong Gu
,
Xiao-Kun Wu
,
Jun Zhang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tsmc.2019.2945055
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tsmc.2019.2945055
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.
Related Topics
Concepts
Computer science
Particle swarm optimization
Mathematical optimization
Constraint (computer-aided design)
Resource allocation
Control (management)
Swarm behaviour
Binary number
Operations research
Artificial intelligence
Machine learning
Engineering
Mathematics
Mechanical engineering
Arithmetic
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsmc.2019.2945055
- https://ieeexplore.ieee.org/ielx7/6221021/9488259/08887458.pdf
- OA Status
- hybrid
- Cited By
- 31
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2989184295
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2989184295Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tsmc.2019.2945055Digital Object Identifier
- Title
-
A Binary Particle Swarm Optimizer With Priority Planning and Hierarchical Learning for Networked Epidemic ControlWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-30Full publication date if available
- Authors
-
Tian-Fang Zhao, Wei‐Neng Chen, Alan Wee‐Chung Liew, Tianlong Gu, Xiao-Kun Wu, Jun ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/tsmc.2019.2945055Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6221021/9488259/08887458.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6221021/9488259/08887458.pdfDirect OA link when available
- Concepts
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Computer science, Particle swarm optimization, Mathematical optimization, Constraint (computer-aided design), Resource allocation, Control (management), Swarm behaviour, Binary number, Operations research, Artificial intelligence, Machine learning, Engineering, Mathematics, Mechanical engineering, Arithmetic, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 9, 2022: 8, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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