A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem

Abstract

This paper presents a new approach called MOEA/NSM (multi-objective evolutionary algorithm integrating NSGA-II, SPEA2 and MOEA/D features). This paper combines the main characteristics of the NSGA-II, SPEA2 and MOEA/D algorithms, and also including 2-opt 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 bi-objective traveling salesman problem. In addition, experiments were carried out 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 the experiments, we applied the evaluation metrics by hypervolume, Epsilon ( $$\epsilon $$ ), EAF and Shapiro–Wilk statistical hypothesis test. The results showed a better performance of the MOEA/NSM when compared with NSGA-II, SPEA2 and MOEA/D.