Towards efficient multidisciplinary design optimization for car body structures

The aim of the here presented research activity is to contribute to the identification and development of efficient strategies for multidisciplinary design optimization of vehicle structures involving, crashworthiness, vibro-acoustic and lightweight design criteria. The literature survey at the start of this activity, identified: that although a large variety of optimization strategies and methods are described in the literature, only few comparisons or guidelines are available for the selection of efficient optimization algorithms or methods for vehicle optimization related problems, involving the mentioned design criteria. In this work, several state of the art optimization algorithms for multidisciplinary design optimization have been selected and are systematically compared for their efficiency on applications that typically occur within a car body design optimization context. Although these comparisons mainly involved existing methods, the resulting comparisons on the industrially relevant application of vehicle design related optimization problems extended the currently available literature. The results could serve as initial guidelines for practitioners in industry and as a starting point for further research. In the optimization literature, there are many test functions/problems available that are commonly used for comparative assessments of optimization algorithms. These test problems are however difficult to relate to industrially relevant problems and vice versa. A novel Representative Surrogate Problem approach is developed in the scope of this work, which could be summarized as a strategy to construct optimization test problems, based on response characteristics of real-world problems. The approach is presented and investigated for its application to car body design problems. Inspired by the response characterization strategies and results, a novel test function generation method based on the composition of random fields is presented. The resulting method is a contribution to the field op global optimization in general and not restricted to automotive applications. This method enables the construction of synthetic optimization problems with various interesting function features. Due to the parameterized nature of the method, these test functions enable structured investigations on the influence of particular problem features on the performance of optimization algorithms. Compared to existing test functions the method provides a further step towards systematic problem feature orientated performance analysis of meta-heuristic optimization methods, which contributes to the analysis, selection and development of optimization algorithms for non-convex optimization problems. The overall results of the performed comparisons and case studies with the developed methods showed that significant gains in optimization efficiency can be achieved by selecting suitable optimization algorithms, and tuned parameter settings for optimization problem formulations relevant to car body design. The comparison results, stressed the need to take into account optimization efficiency, whereas in many case studies in the literature, optimization algorithms are selected without proper justification. The presented results and methods are relevant for practitioners in industry and for further research on the development of optimization algorithms for complex problems.