Data-driven topology design with persistent homology for enhancing population diversity

This paper proposes a selection strategy for enhancing population diversity in data-driven topology design (DDTD), a topology optimization framework based on evolutionary algorithms (EAs) using a deep generative model. While population diversity is essential for global search with EAs, conventional selection operators that preserve diverse solutions based on objective values may still lead to a loss of population diversity in topology optimization problems due to the high dimensionality of design variable space and strong nonlinearity of evaluation functions. Motivated by the idea that topology is what characterizes the inherent diversity among material distributions, we employ a topological data analysis method called persistent homology. As a specific operation, a Wasserstein distance sorting between persistence diagrams is introduced into a selection algorithm to maintain the intrinsic population diversity. We apply the proposed selection operation incorporated into DDTD to a stress-based topology optimization problem as a numerical example. The results confirm that topological features extracted via persistent homology can be appropriately quantified using the Wasserstein distance, and incorporating them into the selection operation significantly enhances the search performance of DDTD. • Proposed selection enhances topological variety in data-driven topology design. • Persistent homology captures key topological features in material distributions. • Wasserstein distance effectively distinguishes diverse material topologies. • Selection via topological metrics leads to unique and high-performing designs.