Abstract
The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization. Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques. The bio-inspired multi-objective grey wolf optimizer (MOGWO) is presented in this paper, and it is formulated based on Pareto optimality, dominance, and archiving external. The performance of the MOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem. The results proved that the proposed MOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems. The performance comparison in terms of Generational Distance, inversion GD, Hypervolume-matrix, scattered-matrix, and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms. The source code of this paper is backed up with extra online support at and .
Highlights
The Brushless Direct Current (BLDC) motor is famous and preferable for real-world applications due to its electronic commutation feature
The Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm optimizes the BLDC motor design variables, and the results obtained by the MOGWO are discussed
With respect to the BLDC wheel motor design optimization problem, MOGWO can demonstrate to be competitive compared to other stateof-the-art optimization methods
Summary
The BLDC motor is famous and preferable for real-world applications due to its electronic commutation feature. The solution accuracy and the convergence speed are poor Various metaheuristic algorithms, such as Multi-objective Grey Wolf Algorithm (MOGWO) [20,21], NSGA-II, Pareto Archived Evolution Strategy (PAES) [22], Multi-objective Firefly Algorithm (MOFA) [11], Multi-objective Bat Algorithm (MOBA) [11], Multi-objective Flower Pollination (MOFPA) [11], Multi-objective Shuffle Frog Leaping Algorithm (MOSFLA) [23], Multi-objective Particle Swarm Optimizer (MOPSO), Multi-objective Ant Lion Algorithm (MOALO) [24], Multi-objective Grasshopper Algorithm (MOGOA) [25], Multi-objective Salp Swarm Algorithm (MOSSA) [26], multi-objective heat transfer search algorithm [27], multiobjective modified adaptive symbiotic organisms search [28], hybrid heat transfer search and passing vehicle search optimizer [29], multi-objective modified heat transfer search [30], multiobjective passing vehicle search algorithm [31], multi-objective slime mould algorithm [32], multiobjective gradient-based optimizer [33], and single-objective equilibrium optimizer [34] are applied for solving the unconstraint and constraint test benchmark problems. In this paper, the MOGWO algorithm is applied to a multi-objective BLDC motor design problem to enhance the solution accuracy with a high convergence speed, and the same has been comprehensively analyzed. The simulation results and performance comparison with other multi-objective algorithms are deliberated in Section 4, and Section 5 concludes the paper
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