Negotiating decision makers' reference points for group preference-based Evolutionary Multi-objective Optimization

Recent studies on Evolutionary Multi-objective Optimization (EMO) aim at focusing the search only on those portions of the front which satisfy the preferences of the Decision Maker (DM), i.e., the Regions Of Interest (ROIs), rather than approximating the whole Pareto front. Most studies assume the uniqueness of the DM which is not the case for several decision making situations. In this study, we address this problematic by providing the DMs with an agent-based negotiation support system to aggregate their conflicting preferences before the beginning of the evolutionary process. This negotiation system helps the DMs to confront and adjust their preferences through a number of negotiation rounds. The system output is a set of social preferences which will be injected subsequently in a preference-based EMO Algorithm (EMOA) in order to guide the search towards a satisfying social ROI. The usefulness of the proposed system is demonstrated through a case study.