Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem

The first step in the design phase of Brushless Direct Current (BLDC) motor is formulation mathematical framework and often used due to its analytical structure. Therefore, BLDC problem considered be an optimization problem. In this paper, model presented, it a basis for emphasizing methods. experimentation has 78 non-linear equations, two objective functions, five variables, six constraints, so as highly electromagnetic optimization. Multi-objective becomes forefront current research obtain global best solution using metaheuristic techniques. bio-inspired multi-objective grey wolf optimizer (MOGWO) presented formulated based on Pareto optimality, dominance, archiving external. performance MOGWO verified standard unconstraint benchmark functions applied results proved that proposed algorithm could handle nonlinear constraints problems. comparison terms Generational Distance, inversion GD, Hypervolume-matrix, scattered-matrix, coverage metrics proves can provide compared other selected algorithms. source code paper backed up with extra online support at .