Design optimization of BLDC motor using multi-objective plasma generation algorithm

作     者：Sundaram B. Pandya Om S. Patel Ajay Kumar Kanak Kalita Pradeep Jangir Jasgurpreet Singh Chohan Sarita Rathee Laith Abualigah 

作者机构：Electrical Engineering Department Shri K. J. Polytechnic Bholav Bharuch 392001 India Department Department of Mechanical Engineering JECRC University Jaipur 303905 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi 600062 India Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Department of Mechanical Engineering and University Centre for Research & Development Chandigarh University Mohali 140413 India Department of Electronics and Communication Engineering School of Engineering & Technology JECRC University Jaipur - 303 905 India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan 

出 版 物：《AIP Conference Proceedings》 

年 卷 期：2024年第3217卷第1期

摘      要：In this paper, a novel Multi-Objective Plasma Generation Optimization (MOPGO) algorithm is introduced. It integrates Non-Dominant Sorting (NDS) and Crowding Distance (CD) methods to solve multi- objective optimization problems effectively. The NDS approach preserves non-dominated solutions, while the CD method ensures diversity among solutions. MOPGO addresses key challenges in optimization such as Pareto dominance, solution diversity. This balanced approach achieves superior optimization performance by maintaining an equilibrium between global diversification and local intensification. The consequences for computational efficiency and solution quality are discussed and compared with other notable algorithms like Equilibrium Optimizer (EO), Thermal Exchange Optimization (TEO), Generalized Normal Distribution Optimization (GNDO) and Moth Flame Optimization (MFO).