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Obtaining Optimal Pareto Front Approximations using Scalarized Preference Information

Obtaining Optimal Pareto Front Approximations using Scalarized Preference Information

Published: 2015 Juli

Buchtitel: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference
Reihe: GECCO '15
Seiten: 631-638
Verlag: ACM
Erscheinungsort: New York

Referierte Veröffentlichung


Scalarization techniques are a popular method for articulating preferences in solving multi-objective optimization problems. These techniques, however, have so far proven to be ill-suited in finding a preference-driven approximation that still captures the Pareto front in its entirety. Therefore, we propose a new concept that defines an optimal distribution of points on the front given a specific scalarization function. It is proven that such an approximation exists for every real-valued problem irrespective of the shape of the corresponding front under some very mild conditions. We also show that our approach works well in obtaining an equidistant approximation of the Pareto front if no specific preference is articulated. Our analysis is complemented by the presentation of a new algorithm that implements the aforementioned concept. We provide in-depth simulation results to demonstrate the performance of our algorithm. The analysis also reveals that our algorithm is able to outperform current state-of-the-art algorithms on many popular benchmark problems.

ISBN: 978-1-4503-3472-3
Weitere Informationen unter: Link
DOI Link: 10.1145/2739480.2754674


Effiziente Algorithmen


Multikriterielle Optimierung