Data Analytics-Based Evolutionary Algorithm for Structure Topology Optimization with Graph-Based Encoding

Abstract

This paper proposes a novel graph-based encoding and population initialization strategy tailored for structural topology optimization. While gradient-based methods are widely employed, their practical utility is often hindered by high sensitivity to hyperparameters, the persistence of intermediate densities, and a propensity for stagnating in local optima. Despite the emergence of non-gradient algorithms, research that intrinsically integrates fundamental topology optimization principles remains scarce. The encoding strategy presented herein provides an explicit representation of structural topological features, which, when integrated into a Differential Evolution framework, enables direct structural search at the topological level. Furthermore, Delaunay triangulation is embedded into the initialization and decoding phases to enhance initial solution quality. Additionally, clustering analysis is utilized to preserve the multi-modal characteristics of the optimization process. Experimental results demonstrate that the proposed methodology effectively reduces invalid crossover and mutation operations, thereby facilitating the discovery of high-potential configurations and significantly improving global search efficiency.