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Majeed, Bakhtawer, Soomro, Zuha, Ebrahim, Mansoor. (1405). Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution. فیزیولوژی و بیوتکنولوژی آبزیان, (), -. doi: 10.22124/jmm.2026.32034.2894
Bakhtawer Majeed; Zuha Soomro; Mansoor Ebrahim. "Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution". فیزیولوژی و بیوتکنولوژی آبزیان, , , 1405, -. doi: 10.22124/jmm.2026.32034.2894
Majeed, Bakhtawer, Soomro, Zuha, Ebrahim, Mansoor. (1405). 'Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution', فیزیولوژی و بیوتکنولوژی آبزیان, (), pp. -. doi: 10.22124/jmm.2026.32034.2894
Majeed, Bakhtawer, Soomro, Zuha, Ebrahim, Mansoor. Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution. فیزیولوژی و بیوتکنولوژی آبزیان, 1405; (): -. doi: 10.22124/jmm.2026.32034.2894

Parameter estimation in SIR epidemic model using dynamic selection preference with adaptive mutation factor enhanced differential evolution

Journal of Mathematical Modeling
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 19 اردیبهشت 1405    اصل مقاله (539.52 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
آمار
تعداد مشاهده مقاله: 3
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