MOCHIO: A Novel Multi-Objective Coronavirus Herd Immunity Optimization Algorithm for Solving Brushless Direct Current Wheel Motor Design Optimization Problem

Abstract A prominent and realistic problem in the domain of magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor such that it functions efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a novel optimization technique inspired by nature, called the Coronavirus Herd Immunity Optimizer (CHIO), is proposed. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of Coronavirus Herd Immunity Optimizer called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it has been directly used to optimize the BLDC motor design optimization problem. The BLDC motor design problem has two main objectives, such as minimization of the motor mass and maximization of the motor efficiency with five constraints and five design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. In addition, the experimental results are compared with the multi-objective whale optimization algorithm (MOWOA), multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimizer (MOPSO), multi-objective moth flame optimizer (MOMFO), and multi-objective bat algorithm (MOBA) are presented to confirm the viability and dominance of the proposed MOCHIO algorithm.