Design Improvement of Permanent Magnet Motor Using Single and Multi-Objective Approach
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Ss. Cyril and Methodius University in Skopje
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Goga Cvetkovski   

Ss. Cyril and Methodius University in Skopje
Power Electronics and Drives 2024;9 (44)
Optimal design often involves solving intricate problems with various parameters and constraints. Engineers use different optimization methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimization approaches. Single-objective optimization focuses on optimizing a single objective, while multi-objective optimization considers multiple conflicting objectives. In optimization, objective functions are mathematical representations of what needs to be optimized. In this case, optimizing the efficiency of the motor, reducing cogging torque, and minimizing the total weight of active materials are defined as possible objective functions. Genetic algorithms are a type of optimization technique inspired by the process of natural selection. They are commonly used in engineering to find optimal solutions to complex problems. When multiple objectives are involved, engineers often seek a set of solutions that represent a trade-off between different goals. In this paper after conducting optimizations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. In this case, Finite Element Analysis (FEA) is used to validate and analyze selected optimization solutions to ensure they meet the desired characteristics and parameters.
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