Uma nova abordagem baseada em algoritmos evolutivos multiobjetivo aplicado ao problema do caixeiro viajante biobjetivo

Abstract

This work presents a new approach to the multiobjective evolutionary algorithm, called MOEA/NSM (Multiobjective Evolutionary Algorithm integrating NSGA-II, SPEA2 and MOEA/D features). The algorithm preserves, in general, the characteristics of an evolutionary algorithm, concentrating qualities of other approaches of success in the literature in a single approach, so that they work together, through subpopulations. The objective of the study was to combine the main characteristics of the NSGA-II, SPEA2 and MOEA/D algorithms, and also to include a local search technique to improve the objective space search. The MOEA/NSM algorithm was compared to the other classical approaches using 9 datasets for the biobjective traveling salesman problem. In addition, experiments were carried out also applying the local search in the classical approaches, resulting in a considerable improvement in the results for these algorithms. From the Pareto frontiers resulting from experiments, we applied the evaluation metrics by Hypervolume, Epsilon (ε), R2, EAF, in addition to the Shapiro-Wilk statistical hypothesis test. The results showed a better performance of the MOEA/NSM in relation to the others, even applying the local search in the others approaches. In this sense, the MOEA/NSM can be considered an algorithm that is able to find solutions not dominated of quality, as much as the classic algorithms of the literature.