An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering

• This paper proposes a post-optimality task of analyzing multiple trade-off solutions obtained using a multi-objective optimizer for hidden solutions principles.
• The recently proposed “automated innovization” procedure is applied to three engineering design problems to show the usefulness of the procedure.
• On each of the three problems, new and innovative design principles (which were not known before) are achieved.

Computational optimization methods are most often used to find a single or multiple optimal or near-optimal solutions to the underlying optimization problem describing the problem at hand. In this paper, we elevate the use of optimization to a higher level in arriving at useful problem knowledge associated with the optimal or near-optimal solutions to a problem. In the proposed innovization process, first a set of trade-off optimal or near-optimal solutions are found using an evolutionary algorithm. Thereafter, the trade-off solutions are analyzed to decipher useful relationships among problem entities automatically so as to provide a better understanding of the problem to a designer or a practitioner. We provide an integrated algorithm for the innovization process and demonstrate the usefulness of the procedure to three real-world engineering design problems. New and innovative design principles obtained in each case should clearly motivate engineers and practitioners for its further application to more complex problems and its further development as a more efficient data analysis procedure.

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