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- W4376486086 abstract "Epidemics like COVID-19 seriously threaten public health. How to control the spread of epidemics has long been an important topic that attracts a large amount of research effort. During the epidemic prevention, it is crucial to effectively reduce the number of infected people, and it is also important to make good use of epidemic prevention resources and reduce cost. To address this issue, we consider a multi-objective epidemic resource allocation problem in this paper. First, we define an optimization model for this problem with objectives to minimize the number of infected people and exposed people, and minimize the cost of resources. The model integrates the Susceptible-Exposed-Infected-Vigilant (SEIV) model to predict the spread of the epidemic and estimate the demand for resources. Second, to solve this intractable problem, we develop a multi-objective level-based learning swarm optimizer (MLLSO) by combining the concepts of level-based learning and nondominated sorting. Third, to further improve the search performance, a special initialization strategy and a preference-based mechanism are also introduced in the algorithm, leading to MLLSO-P. Finally, experimental results demonstrate the effectiveness of the proposed approaches." @default.
- W4376486086 created "2023-05-14" @default.
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- W4376486086 date "2023-01-01" @default.
- W4376486086 modified "2023-09-26" @default.
- W4376486086 title "A Multi-objective Level-Based Learning Swarm Optimization Algorithm with Preference for Epidemic Resource Allocation" @default.
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- W4376486086 doi "https://doi.org/10.1007/978-981-99-2385-4_23" @default.
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