Surrogate-assisted multi-objective design of complex multibody systems
This work addresses the problem of computationally expensive multi-objective optimization for engineers and researchers in multibody systems, representing an incremental improvement by refining existing surrogate-assisted methods.
The paper tackled the challenge of optimizing large-scale multibody systems with multiple conflicting criteria by developing a back-and-forth approach between surrogate modeling and multi-objective optimization to improve solution quality, achieving computational efficiency and high-quality solutions as demonstrated on an expensive-to-evaluate multibody system.
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.