Optimizing brushless direct current motor design: An application of the multi-objective generalized normal distribution optimization
Sundaram B. Pandya,
Pradeep Jangir,
Miroslav Mahdal,
Kanak Kalita,
Jasgurpreet Singh Chohan,
Laith Abualigah
Affiliations
Sundaram B. Pandya
Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch 392 001, India
Pradeep Jangir
Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
Miroslav Mahdal
Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 70800 Ostrava, Czech Republic
Kanak Kalita
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India; Corresponding author.
Jasgurpreet Singh Chohan
Department of Mechanical Engineering and University Centre for Research & Development, Chandigarh University, Mohali, 140413, India
Laith Abualigah
Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan; MEU Research Unit, Middle East University, Amman, Jordan; School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.