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Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution | ||
| Journal of Mathematical Modeling | ||
| دوره 14، شماره 3، آبان 2026، صفحه 1197-1212 اصل مقاله (536.13 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2026.32034.2894 | ||
| نویسندگان | ||
| Bakhtawer Majeed* 1؛ Zuha Soomro2؛ Mansoor Ebrahim3 | ||
| 1Department of Computer Science, Faculty of Engineering, Science and Technology at IQRA University, Karachi, Pakistan | ||
| 2Department of Computer Science, Faculty of Engineering, Science and Technology, IQRA University Main Campus, Karachi, Pakistan | ||
| 3Department of Computer Science, Faculty of Engineering, Science & Technology, IQRA University, Kaarachi, Pakistan | ||
| چکیده | ||
| To understand and manage the spread of infectious diseases in epidemiological models such as the Susceptible-Infected-Recovered (SIR) framework, it is vital to accurately estimate the transmission (β) and recovery (γ) parameters. This study proposes the dynamic selection preference with adaptive mutation factor differential evolution (DSP-AMF-DE) algorithm. The algorithm implements an adaptive mutation factor that dynamically regulates the balance between exploration and exploitation in the population over generations, and dynamic selection preference mechanisms that focus the selection of better candidate solutions and maintain diversity. Seven Pakistani regions covering several epidemic waves over a period of 671 days have been included in a multi-regional dataset. Robustness evaluation for multiple independent runs demonstrate the superiority of the proposed algorithm, which considerably outperforms six competing algorithms. | ||
| کلیدواژهها | ||
| SIR model؛ DSP-AMF-DE؛ standard DE؛ COVID-19 pandemic | ||
| مراجع | ||
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