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Adding Implicit Measurement Methods to Interactive Optimizations in Industrial Design - A Concept, First Tests, and Comparison Using Two Simple Case Studies

Part of: Knowledge-based Engineering

Published online by Cambridge University Press:  26 July 2019

Martin Wiesner ,
Andreas Petrow  and
Sándor Vajna
Martin Wiesner*
Affiliation:
Otto-von-Guericke-University Magdeburg
Andreas Petrow
Affiliation:
Otto-von-Guericke-University Magdeburg
Sándor Vajna
Affiliation:
Otto-von-Guericke-University Magdeburg
*
Contact: Wiesner, Martin, Otto-von-Guericke-University Magdeburg, IMK, Germany, [email protected]

Abstract

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In this article, a new approach to interactive optimization in industrial design is presented in which, for the first time, implicit preference acquisition methods are integrated. Suitable methods for preference acquisition will be selected, adapted and combined with an own PSO-inspired algorithm. The application of implicit preferences as well as the combined application of implicit and explicit preferences in an interactive optimization represents the main novelty of this contribution since this has not yet been carried out according to the current state of knowledge.Two case studies will be used to test this new approach with regard to convergence and acceptance, and a comparison will be made between the three different kind of optimization (implicit, explicit as well as a combination of both) in terms of their results.

Keywords

Interactive OptimizationImplicit MeasuresIndustrial designOptimisationCrowdsourcing
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2447 - 2456
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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