Work

Collocation method with flood-based metaheuristic optimizer for optimal control on a multi-strain COVID-19 model

Year: 2025

Type: article

Abstract: This paper describes a new and powerful way to solve optimal control problems (OCPs) on a multi-strain COVID-19 model for strategies related to vaccination and amplification. We call it the collocation method with a flood-based metaheuristic optimizer (FBMO). We use a collocation method with Laguerre polynomials and their derivative operational matrices to turn the OCP into a nonlinear programming (NLP) problem. To address the NLP, the research employs the FBMO to determine the control variables ui for i = 1, 2, and 3, representing isolation, vaccination efficacy, and treatment enhancement, in conjunction with the state function of the multi-strain COVID-19 model. These strategies are executed within an SVIcIvR-type control model for COVID-19 in Morocco, designed to control the outbreak of multi-strain disease. The paper’s primary aim is to achieve a high-quality optimal solution for the given OCP, thereby contributing to the advancement of efficient strategies for managing the COVID-19 pandemic.

Source: An International Journal of Optimization and Control Theories & Applications (IJOCTA)

DOI: 10.36922/ijocta.1735

Authors: Asiyeh Ebrahimzadeh, Raheleh Khanduzi, Amin Jajarmi

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4409238996

FWCI:

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