Coronavirus Metamorphosis Optimization Algorithm and Collocation Method for Optimal Control Problem in COVID‐19 Vaccination Model

Abstract

This article addresses an optimal control problem (OCP) related to the COVID‐19 vaccination model. We transform the OCP into a nonlinear programming (NLP) problem by employing a collocation method that utilizes shifted Jacobian polynomials (SJPs) and their derivative operational matrices. To solve the NLP, we implement the Coronavirus Metamorphosis Optimization Algorithm (CMOA), which determines the control variables for , 2, and 3, corresponding to isolation, vaccination efficacy, and treatment enhancement. The CMOA is particularly suitable for the OCP concerning COVID‐19 due to its ability to navigate complex challenges and provide accurate solutions. Therefore, the primary aim of this paper is to identify a high‐quality optimal solution for the OCP, thereby contributing to the development of effective control strategies for managing the COVID‐19 pandemic.