MOCHIO: a novel Multi-Objective Coronavirus Herd Immunity Optimization algorithm for solving brushless direct current wheel motor design optimization problem

A prominent and realistic problem in magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor to function efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a new metaheuristic optimization algorithm called the Coronavirus Herd Immunity Optimizer (CHIO) is reported for solving global optimization problems. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of CHIO called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it is applied to optimize the BLDC motor design optimization problem. A static penalty constraint handling is introduced to handle the constraints, and a fuzzy-based membership function has been introduced to find the best compromise results. The BLDC motor design problem has two main objectives: minimizing the motor mass and maximizing the efficiency with five constraints and five decision/design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. The experimental results are compared with other competitors are presented to confirm the viability and dominance of the MOCHIO. Further, six performance metrics are calculated for all algorithms to assess the performances.