{"ID":2863540,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24667","arxiv_id":"2509.24667","title":"Continuation strategies to mitigate convergence to low-performing local optima in dynamic topology optimization","abstract":"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.","short_abstract":"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 op...","url_abs":"https://arxiv.org/abs/2509.24667","url_pdf":"https://arxiv.org/pdf/2509.24667v1","authors":"[\"Tom De Weer\",\"Vanessa Cool\",\"Elke Deckers\"]","published":"2025-09-29T12:13:26Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}