Integration of Expert's Preferences in Pareto Optimization by Desirability Function Techniques

Mehnen, J.1, a; Trautmann, H.

1)
Institut für Spanende Fertigung, Universität Dortmund, Baroper Str. 301, 44227 Dortmund

a) mehnen@isf.de

Kurzfassung

Many real-world problems have a multiobjective character. A-posteriori techniques such as multiobjective evolutionary algorithms (MOEA) generate best compromise solution sets, i.e. Pareto fronts and Pareto sets. Classic MOEA are able to find quite efficiently good approximations of the complete Pareto fronts also for very complex problems. In real-world applications only small sections of the complete front are of practical interest. Desirability functions are a flexible, intuitive and mathematically sound technique to focus on Pareto front segments without loosing the power of the a-posteriori population search methods. A systematic analysis of typical multiobjective evolutionary algorithms applied to a set of test functions and a real-world problem, the mold temperature control design problem, using the desirability function technique is presented.

Veröffentlichung

In: Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '06), 2006, Ischia, Italy, Teti, R. (Hrsg.), ISBN 978-88-95028-01-9, S. 293-298

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