An Adaptive Weight-Based Performance Optimization Algorithm for Motor Design Using GNN Representation

Abstract

Motor design involves a variety of complex parameters, and traditional approaches often rely on experience and experimentation, which can be inefficient and challenging to optimize. With the rapid growth of industries such as electric vehicles and intelligent manufacturing, the demand for improved motor performance continues to rise. Optimizing motor designs under multi-objective and multi-constraint conditions has become a critical challenge. To address this, this paper introduces a motor design performance optimization algorithm that leverages Graph Neural Networks (GNN) and adaptive weighting techniques. GNN, a deep learning model adept at handling complex structured data, is capable of modeling the relationships between multiple parameters in motor design. Its feature propagation mechanism allows for automatic extraction of essential features, effectively addressing the limitations of traditional methods in capturing parameter dependencies. Additionally, Mixed-Integer Linear Programming (MILP) serves as a robust global optimization tool, capable of finding the optimal solution even in the presence of complex decision variables and constraints, overcoming the global convergence issues associated with conventional optimization algorithms. The adaptive weighting mechanism further enhances the algorithm by dynamically adjusting the weights based on the parameters' influence on motor performance, ensuring more accurate and adaptable optimization results across different scenarios. By combining these three techniques, this paper aims to resolve issues related to inefficiency, poor global convergence, and the static nature of parameter weighting in traditional motor design optimization. This approach integrates advanced machine learning models and optimization algorithms to create an efficient framework for motor design performance optimization.