In this paper, an original algorithm to solve multiobjective optimization problems, which makes use of the Tabu Search meta-heuristic, is presented. Scalarization of the vector problem is performed by introducing a fitness function that takes under control both the Pa re to optimality of the solutions, and the uniformity in the Pareto front sampling. The performance of the proposed algorithm is compared with that of a scalar Tabu Search method, coupled with the 8-constraint strategy. The results on analytical problems, whose Pareto front is available from the literature, demonstrate the effectiveness of the method.
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