Yuka Ogino
ABSTRACT
In this work we investigated the concept of desirability functions for a many-objective optimization of a hybrid car controller with five objectives from different domains. We study this problem from the perspective of preference expression. Specifically we are looking at the impact of wrongly defined desirabilities and how this can be corrected using a Graphical User Interface (GUI). Overall we find that a desirability-based many-objective optimization approach could be well suited for real-world problems with objectives from many domains as it is becoming more and more common in industrial settings.
- Dimo Brockhoff, Johannes Bader, Lothar Thiele, and Eckart Zitzler. 2013. Directed multiobjective optimization based on the weighted hypervolume indicator. Journal of Multi-Criteria Decision Analysis 20, 5--6 (2013), 291--317.Google Scholar
Cross Ref
- EC Harrington. 1965. The desirability function. Industrial quality control 21, 10 (1965), 494--498.Google Scholar
- Ben Paechter, RC Rankin, Andrew Cumming, and Terence C Fogarty. 1998. Timetabling the classes of an entire university with an evolutionary algorithm. In International Conference on Parallel Problem Solving from Nature. Springer Berlin Heidelberg, 865--874. Google ScholarDigital Library
- Tobias Rodemann, Kaname Narukawa, Michael Fischer, and Mohammed Awada. 2015. Many-objective optimization of a hybrid car controller. In European Conference on the Applications of Evolutionary Computation. Springer, 593--603.Google ScholarCross Ref
- Tobias Wagner and Heike Trautmann. 2010. Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functions. Evolutionary Computation, IEEE Transactions on 14, 5 (2010), 688--701. Google ScholarDigital Library
- K Wonggasem, T Wagner, H Trautmann, D Biermann, and C Weihs. 2013. Multi-objective Optimization of Hard Turning of AISI 6150 Using PCA-based Desirability Index for Correlated Objectives. Procedia CIRP 12 (2013), 13--18.Google ScholarCross Ref
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