A Method for Selecting Pareto Optimal Solutions in Multiobjective Optimization
In many real-life multiobjective optimization problems and particularly in combinatorial ones, a common methodology is to determine a Pareto optimal set. These sets can be extremely large or may contain an infinite number of solutions. It is then difficult to choose among these solutions, one that corresponds to the best compromise according to the decision-maker's preferences. In this paper, we propose a model to select a restricted set of solutions. These sets should be structurally different and the most representative of the Pareto-optimal solutions. Our model is implemented using hierarchical algorithms and variable neighborhood search metaheuristics.
eISSN 0975-5748; pISSN 0974-875X