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

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 objecti...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2019-08, Vol.23 (15), p.6157-6168
Main Authors: Moraes, Deyvid Heric, Sanches, Danilo Sipoli, da Silva Rocha, Josimar, Garbelini, Jader Maikol Caldonazzo, Castoldi, Marcelo Favoretto
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Decomposition
Elitism
Engineering
Evolutionary algorithms
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Objectives
Optimization
Robotics
Searching
Traveling salesman problem
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Summary: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 ( ϵ ), 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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3269-8