Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

Abstract

Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.