Interactive Multiobjective Optimisation: Preference Changes and Algorithm Responsiveness

Taylor, K and Li, X 2018, 'Interactive Multiobjective Optimisation: Preference Changes and Algorithm Responsiveness', in Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan, 15 - 19 July 2018, pp. 761-768.


Document type: Conference Paper
Collection: Conference Papers

Title Interactive Multiobjective Optimisation: Preference Changes and Algorithm Responsiveness
Author(s) Taylor, K
Li, X
Year 2018
Conference name GECCO 2018
Conference location Kyoto, Japan
Conference dates 15 - 19 July 2018
Proceedings title Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO 2018)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 761
End page 768
Total pages 8
Abstract For optimisation problems with multiple objectives and large search spaces, it may not be feasible to find all optimal solutions. Even if possible, a decision maker (DM) is only interested in a small number of these solutions. Incorporating a DM's solution preferences into the process reduces the problem's search space by focusing only on regions of interest. Allowing a DM to interact and alter their preferences during a single optimisation run facilitates learning and mistake correction, and improves the search for desired solutions. In this paper, we apply an interactive framework to four leading multi-objective evolutionary algorithms (MOEAs), which use reference points to model preferences. Furthermore, we propose a new performance metric for algorithm responsiveness to preference changes, and evaluate these algorithms using this metric. Interactive algorithms must respond to changes in DM preferences and we show how our new metric is able to differentiate between the four algorithms when run on the ZDT suite of test problems. Finally, we identify characteristics of these methods that determine their level of response to change.
Subjects Operations Research
Optimisation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Interactive optimization
Preference modeling
Multiobjective optimization
Evolutionary Optimization
DOI - identifier 10.1145/3205455.3205624
Copyright notice © 2018 Association for Computing Machinery
ISBN 9781450356183
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