Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms

• Published in: European journal of operational research Vol. 289; no. 3; pp. 1209 - 1222
• Main Authors: Diaz, Juan Esteban, López-Ibáñez, Manuel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16.03.2021
• Subjects: Automatic algorithm design and configuration; Decision maker’s preferences; Metaheuristics; Multi-objective optimisation; Automatic algorithm design and configuration; Decision maker’s preferences; Metaheuristics; Multi-objective optimisation
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2020.07.059

•A visual way of eliciting decision-maker's (DM's) preferences is described.•Empirical attainment differences are mapped into a weighted hypervolume indicator.•This indicator incorporates DM’s preferences into automatic algorithm configuration.•We demonstrate the effectiveness of our approach on a complex real-world problem. Automatic configuration (AC) methods are increasingly used to tune and design optimisation algorithms for problems with multiple objectives. Most AC methods use unary quality indicators, which assign a single scalar value to an approximation to the Pareto front, to compare the performance of different optimisers. These quality indicators, however, imply preferences beyond Pareto-optimality that may differ from those of the decision maker (DM). Although it is possible to incorporate DM’s preferences into quality indicators, e.g., by means of the weighted hypervolume indicator (HVw), expressing preferences in terms of weight function is not always intuitive nor an easy task for a DM, in particular, when comparing the stochastic outcomes of several algorithm configurations. A more visual approach to compare such outcomes is the visualisation of their empirical attainment functions (EAFs) differences. This paper proposes using such visualisations as a way of eliciting information about regions of the objective space that are preferred by the DM. We present a method to convert the information about EAF differences into a HVw that will assign higher quality values to approximation fronts that result in EAF differences preferred by the DM. We show that the resulting HVw may be used by an AC method to guide the configuration of multi-objective optimisers according to the preferences of the DM. We evaluate the proposed approach on a well-known benchmark problem. Finally, we apply our approach to re-configuring, according to different DM’s preferences, a multi-objective optimiser tackling a real-world production planning problem arising in the manufacturing industry.