A Simulated Annealing Algorithm to Learn an RMP Preference Model

Multiple Criteria Decision Aiding (MCDA) provides preference models and algorithms to assist decision-makers (DMs) in their decision-making tasks. The preference models are characterized by preference parameters which can be learned through preference learning algorithms from holistic judgments given by the DM. Here, we use Simulated Annealing (SA) to learn the parameters of the Ranking based on Multiple Reference Profiles (RMP) model and its simpler variant SRMP. Extensive experiments demonstrate that our proposal outperforms existing methods in terms of both calculation time and accuracy.