GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning

Motor design complexity arises from intricate parameter interdependencies, where traditional experience-driven approaches prove inefficient and difficult to optimize through experimentation alone. Growing demands for enhanced motor performance in electric vehicles and intelligent manufacturing necessitate advanced solutions for multi-objective, multi-constraint optimization challenges. This paper presents a performance optimization algorithm integrating Graph Neural Networks (GNN) with adaptive weighting to overcome these limitations. GNNs excel at modeling structured parameter relationships through feature propagation, automatically extracting critical design features that conventional methods fail to capture effectively. Complementing this, Mixed-Integer Linear Programming (MILP) provides robust global optimization under complex decision variables and constraints, resolving convergence issues inherent in traditional algorithms. An adaptive weighting mechanism dynamically prioritizes parameters based on their performance impact, ensuring context-sensitive optimization. By synthesizing GNN representation learning, MILP optimization, and adaptive weighting, our framework addresses three core deficiencies of existing methods: computational inefficiency, poor global convergence, and static parameter valuation. This integrated machine learning and optimization approach establishes an efficient paradigm for next-generation motor design.