Integrating the decision maker’s preferences into Multiobjective Ant Colony Optimization

Multiobjective Ant Colony Optimization (MO-ACO) metaheuris-tics have shown to be very successful in addressing hard multiobjec-tive combinatorial optimization problems. As most other multiobjec-tive heuristics, MO-ACOs are however usually providing the decision maker with the best possible approximation of the Pareto-optimal fron-tier, leaving him or her with the delicate task of making a choice in an often very large set of non dominated solutions. This paper presents an approach of tackling multiobjective combinatorial optimization prob-lems by integrating a decision maker’s a priori preferences.