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

Kii, Taisei; Yaji, Kentaro; Teramoto, Hiroshi et al.

International Journal of Mechanical Sciences, 2025, 301, 110493

Number of Access:17（2025-10-05 09:44 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102626

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Title
Data-driven topology design with persistent homology for enhancing population diversity
Creator
ORCID
0009-0001-8856-2467 0009-0001-8856-2467
Scopus Author ID
58753546300 58753546300
CreatorName
Kii, Taisei
NRID
1000090779373 1000090779373
ORCID
0000-0002-4309-2043 0000-0002-4309-2043
researchmap permalink
yajiken0609 yajiken0609
Scopus Author ID
55787947500 55787947500
CreatorName
Yaji, Kentaro
NRID
1000090463728 1000090463728
CreatorName
Teramoto, Hiroshi
NRID
1000010228992 1000010228992
ORCID
0000-0002-2197-1012 0000-0002-2197-1012
researchmap permalink
read0014111 read0014111
Scopus Author ID
7404057902 7404057902
CreatorName
Fujita, Kikuo
Subject
Data-driven design
Evolutionary algorithm
Persistent homology
Population diversity
Topology optimization
Wasserstein distance
Description
Kii T., Yaji K., Teramoto H., et al. Data-driven topology design with persistent homology for enhancing population diversity. International Journal of Mechanical Sciences 301, 110493 (2025); https://doi.org/10.1016/j.ijmecsci.2025.110493.
Abstract
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.
Publisher
Elsevier Ltd
Source Title
International Journal of Mechanical Sciences
Volume (Issue)
301
Page
110493
Date of Issued
2025-06-28
Language
English
Handle URL
https://hdl.handle.net/11094/102626
PISSN
00207403
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1016/j.ijmecsci.2025.110493
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
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Version of Record
Category
学術雑誌論文 Journal Article