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Soft Computing
Erschienen in: Soft Computing 15/2019

12.06.2018 | Methodologies and Application

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

verfasst von: Deyvid Heric Moraes, Danilo Sipoli Sanches, Josimar da Silva Rocha, Jader Maikol Caldonazzo Garbelini, Marcelo Favoretto Castoldi

Erschienen in: Soft Computing | Ausgabe 15/2019

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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.
Vorheriger Artikel Outranking method for intuitionistic 2-tuple fuzzy linguistic information model in group decision making
Nächster Artikel Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines

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Metadaten
Titel
A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem
verfasst von
Deyvid Heric Moraes
Danilo Sipoli Sanches
Josimar da Silva Rocha
Jader Maikol Caldonazzo Garbelini
Marcelo Favoretto Castoldi
Publikationsdatum
12.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 15/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3269-8

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