Abstract
The multiple criteria aggregation methods allow us to construct a recommendation from a set of alternatives based on the preferences of a decision maker. In some approaches, the recommendation is immediately deduced from the preferences aggregation process. When the aggregation model of preferences is based on the outranking approach, a special treatment is required, but some non-rational violations of the explicit global model of preferences could happen. In this case, the exploitation phase could then be treated as a multiobjective optimization problem. In this paper a new multiobjective evolutionary algorithm, which allows exploiting a known fuzzy outranking relation, is introduced with the purpose of constructing a recommendation for ranking problems. The performance of our algorithm is evaluated on a set of test problems. Computational results show that the multiobjective genetic algorithm-based heuristic is capable of producing high-quality recommendations.
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