A Dynamic and Heterogeneous Representation for Topology Optimization Using Evolutionary Algorithms [Research Frontier]

Topology optimization (TO) emerges as a pivotal technique in engineering design, which aims at optimizing material distribution within given design spaces. TO often presents large-scale combinatorial optimization problems, which pose challenges to gradient methods in handling discrete variables and evolutionary algorithms in handling high-dimensional search spaces. This paper proposes a dynamic and heterogeneous representation of TO solutions, and designs novel crossover and mutation operators for searching for TO solutions. The representation features low granularity at early stages to explore optimal regions and high granularity at later stages to exploit optimal solutions, and the operators support evolutionary algorithms in effectively evolving populations using the representation. Experimental results verify that the proposed method significantly outperforms the state-of-the-art gradient methods and evolutionary algorithms across various TO tasks.