Hyperheuristics for indirect elicitation of outranking model’s parameters in Project Portfolio Optimization

Abstract

One of the main problems that face Multi-Objective Evolutionary Algorithms (MOEAs) when approximating the best compromise solutions is a proper a priori incorporation of the Decision Maker’s (DM) preferences. Particularly, when these methods rely on outranking approaches, they need eliciting several parameters. Given that his task is of great cognitive effort for a DM, it is performed indirectly through a battery of examples that (s)he provides previously and that reflex the desired preferences. So far, only metaheuristics have been used to transform such examples into parameters’ values of specific preference models. The present research propose an architecture for a hyperheuristic that integrates characterization and performance analysis into the elicitation process. It is expected that a good combination the metaheuristic could improve the quality of parameters estimated.