@inproceedings {pub4628,
	title = {Cooperative framework for many objective crash optimization},
	author = {Nivesh Dommaraju AND Mariusz Bujny AND Stefan Menzel AND Markus Olhofer AND Fabian Duddeck},
	year = {2021},
	month = {June},
	abstract = {Topology optimization with multiple objectives may yield a large set of Pareto optimal designs. Among other methods used to analyze and summarize the optimal solutions, clustering methods can be used to identify a few representative designs, which can be more easily reviewed by a designer. For example, Dommaraju et al. propose to select diverse designs based on geometric features. To generate the Pareto front, well-studied evolutionary algorithms such as NSGA2 are useful but they are expensive. In contrast, methods such as HCA-SEW which vary the weights, expressing the relative preference, for the objectives are more economical in steering the optimization process towards a Pareto optimal design. A natural extension would be to guide HCA-SEW to find designs on the Pareto front with certain geometric features, e.g., designs similar to a specific reference needs to be selected, or designs within a certain performance range. Since the engineer may not know the possible performance ranges and geometrical diversity, we describe a more useful framework for cooperative optimization by supporting the designer to formulate their preferences.},
	publisher = {WCSMO-14},
	booktitle = {14th World Congress of Structural and Multidisciplinary Optimization}
}