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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24667 |
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| _version_ | 1866915521879867392 |
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| author | De Weer, Tom Cool, Vanessa Deckers, Elke |
| author_facet | De Weer, Tom Cool, Vanessa Deckers, Elke |
| contents | Solving dynamic topology optimization problems often yields low-performing local optima. Instead of converging towards a design that exploits dynamic mechanisms, a less interesting, mass-driven solution is often generated. This necessitates repeated and computationally expensive optimization reruns before a suitable optimum is found. In this work, an overview of three strategy classes that reduce the need for such reruns is presented: exclusion strategies, frequency shift methods and relaxation strategies. Novel variants for each strategy class are developed, implemented and compared via Monte Carlo sampling on a benchmark problem, namely the sound transmission loss optimization of a sandwich panel. Probabilities of achieving high-performing optima are estimated and all investigated strategies demonstrate quantifiable improvements and trade-offs. The study offers furthermore a quantitative comparison of the presented strategies, supporting researchers in making an informed choice when addressing convergence to poor local optima in dynamic topology optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continuation strategies to mitigate convergence to low-performing local optima in dynamic topology optimization De Weer, Tom Cool, Vanessa Deckers, Elke Optimization and Control Solving dynamic topology optimization problems often yields low-performing local optima. Instead of converging towards a design that exploits dynamic mechanisms, a less interesting, mass-driven solution is often generated. This necessitates repeated and computationally expensive optimization reruns before a suitable optimum is found. In this work, an overview of three strategy classes that reduce the need for such reruns is presented: exclusion strategies, frequency shift methods and relaxation strategies. Novel variants for each strategy class are developed, implemented and compared via Monte Carlo sampling on a benchmark problem, namely the sound transmission loss optimization of a sandwich panel. Probabilities of achieving high-performing optima are estimated and all investigated strategies demonstrate quantifiable improvements and trade-offs. The study offers furthermore a quantitative comparison of the presented strategies, supporting researchers in making an informed choice when addressing convergence to poor local optima in dynamic topology optimization. |
| title | Continuation strategies to mitigate convergence to low-performing local optima in dynamic topology optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.24667 |