Open Access

ARTICLE

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

M. Premkumar¹, Pradeep Jangir², B. Santhosh Kumar³, Mohammad A. Alqudah⁴, Kottakkaran Sooppy Nisar^5,*

1 Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam, 532127, Andhra Pradesh, India
2 Rajasthan Rajya Vidyut Prasaran Nigam, Sikar, 332025, Rajasthan, India
3 Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, 532127, Andhra Pradesh, India
4 German Jordanian University, Amman, 11180, Jordan
5 Department of Mathematics, College of Arts and Sciences, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser, 11991, Saudi Arabia

* Corresponding Author: Kottakkaran Sooppy Nisar. Email:

(This article belongs to the Special Issue: Role of Computer in Modelling & Solving Real-World Problems)

Computers, Materials & Continua 2022, 70(2), 2435-2452. https://doi.org/10.32604/cmc.2022.016488

Received 03 January 2021; Accepted 17 April 2021; Issue published 27 September 2021

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 .

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Citations