A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets
Abstract
The incorporation of expert knowledge into multiobjective optimization is an important issue which in this paper is reflected in terms of an aspiration set consisting of multiple reference points. The behaviour of the recently introduced evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively studied for biobjective optimization problems w.r.t. R-NSGA2 and a respective variant. It will be shown that the averaged Hausdorff distance, integrated into AS-EMOA, is an effcient means to accurately approximate the desired aspiration set.
Keywords
multi-objective optimization; aspiration set; preferences
Cite as
Rudolph, G., Schütze, O., Grimme, C., & Trautmann, H. (2014). A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In Tantar, A., Tantar, E., Sun, J., Zhang, W., Ding, Q., Schütze, O., Emmerich, M., Legrand, P., Del, M. P., & Coello, C. C. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (pp. 261–273). Advances in Intelligent Systems and Computing: Vol. 288. Springer International Publishing.Details
Publication type
Research article (book contribution)
Peer reviewed
Yes
Publication status
Published
Year
2014
Book title
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V
Editor
Tantar A, Tantar E, Sun J, Zhang W, Ding Q, Schütze O, Emmerich M, Legrand P, Del Moral P, Coello Coello CA
Start page
261
End page
273
Volume
288
Title of series
Advances in Intelligent Systems and Computing
Publisher
Springer International Publishing
Language
English
ISBN
978-3-319-07493-1
DOI
Full text