Surrogate-assisted multi-objective design of complex multibody systems
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arXiv
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| Format: | Preprint |
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2024
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| author | Amakor, Augustina C. Berkemeier, Manuel B. Wohlleben, Meike Sextro, Walter Peitz, Sebastian |
| author_facet | Amakor, Augustina C. Berkemeier, Manuel B. Wohlleben, Meike Sextro, Walter Peitz, Sebastian |
| contents | The optimization of large-scale multibody systems is a numerically challenging task, in particular when considering multiple conflicting criteria at the same time. In this situation, we need to approximate the Pareto set of optimal compromises, which is significantly more expensive than finding a single optimum in single-objective optimization. To prevent large costs, the usage of surrogate models, constructed from a small but informative number of expensive model evaluations, is a very popular and widely studied approach. The central challenge then is to ensure a high quality (that is, near-optimality) of the solutions that were obtained using the surrogate model, which can be hard to guarantee with a single pre-computed surrogate. We present a back-and-forth approach between surrogate modeling and multi-objective optimization to improve the quality of the obtained solutions. Using the example of an expensive-to-evaluate multibody system, we compare different strategies regarding multi-objective optimization, sampling and also surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surrogate-assisted multi-objective design of complex multibody systems Amakor, Augustina C. Berkemeier, Manuel B. Wohlleben, Meike Sextro, Walter Peitz, Sebastian Optimization and Control Machine Learning The optimization of large-scale multibody systems is a numerically challenging task, in particular when considering multiple conflicting criteria at the same time. In this situation, we need to approximate the Pareto set of optimal compromises, which is significantly more expensive than finding a single optimum in single-objective optimization. To prevent large costs, the usage of surrogate models, constructed from a small but informative number of expensive model evaluations, is a very popular and widely studied approach. The central challenge then is to ensure a high quality (that is, near-optimality) of the solutions that were obtained using the surrogate model, which can be hard to guarantee with a single pre-computed surrogate. We present a back-and-forth approach between surrogate modeling and multi-objective optimization to improve the quality of the obtained solutions. Using the example of an expensive-to-evaluate multibody system, we compare different strategies regarding multi-objective optimization, sampling and also surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality. |
| title | Surrogate-assisted multi-objective design of complex multibody systems |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2412.14854 |