TY - GEN
T1 - Using comparative preference statements in hypervolume-based interactive multiobjective optimization
AU - Brockhoff, Dimo
AU - Hamadi, Youssef
AU - Kaci, Souhila
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the " interesting" regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm.
AB - The objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the " interesting" regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm.
KW - Evolutionary multiobjective optimization
KW - Interactive decision making
KW - Multiobjective optimization
KW - Preferences
U2 - 10.1007/978-3-319-09584-4_13
DO - 10.1007/978-3-319-09584-4_13
M3 - Conference contribution
AN - SCOPUS:84905867041
SN - 9783319095837
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 136
BT - Learning and Intelligent Optimization - 8th International Conference, Lion 8, Revised Selected Papers
PB - Springer Verlag
T2 - 8th International Conference on Learning and Intelligent OptimizatioN, LION 2014
Y2 - 16 February 2014 through 21 February 2014
ER -