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

Moraes, Deyvid Heric; Sanches, Danilo Sipoli; Rocha, Josimar da Silva; Garbelini, Jader M. Caldonazzo; Castoldi, Marcelo Favoretto

doi:10.1007/s00500-018-3269-8

Cited by 18 publications

(17 citation statements)

References 27 publications

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“…NTGA [5] is selected, because approaches described in this paper are based on it. Finally, MOEA/NSM [12] is used as the current best state-of-the-art method.…”

Section: Reference Methodsmentioning

confidence: 99%

“…Genetic Algorithms are commonly used (e.g. [11], [12]). Authors of [14] and [15] have used an ant colony optimization methods.…”

Section: Related Workmentioning

confidence: 99%

“…Recently created Multi-Objective Evolutionary Algorithm integrating NSGA-II, SPEA2, and MEA/D (MOEA/NSM) [12] has been successfully applied to mTSP. It uses the crowding distance, decomposition and Pareto strength.…”

Section: Related Workmentioning

confidence: 99%

“…In Multi-objective Travelling Salesman Problem (mTSP) multiple aspects of the route are evaluated [12]. It could be cost, time, length or risk of travel.…”

Section: Problemmentioning

confidence: 99%

“…A set of experiments is designed to verify the quality of PF approximations generated by all methods. The Multi-Objective Evolutionary Algorithm integrating NSGA-II, SPEA2, and MOEA/D (MOEA/NSM) [12] is currently the best-known method for mTSP. Hence, it is used to compare the results.…”

Section: Introductionmentioning

confidence: 99%

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Non-dominated Sorting Tournament Genetic Algorithm for Multi-Objective Travelling Salesman Problem

Myszkowski

Laszczyk

Dziadek

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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A Travelling Salesman Problem (TSP) is an NPhard combinatorial problem that is very important for many real-world applications. In this paper, it is shown, that proposed approach solves multi-objective TSP (mTSP) more effectively than other investigated methods, i.e. Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed methods use rank and crowding distance (well-known from NSGA-II), combining those mechanisms in a novel, unique way: competing and coevolving in the evolution process. The proposed modifications are investigated and verified by the benchmark mTSP instances, and results are compared to other methods.

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“…NTGA [5] is selected, because approaches described in this paper are based on it. Finally, MOEA/NSM [12] is used as the current best state-of-the-art method.…”

Section: Reference Methodsmentioning

confidence: 99%

“…Genetic Algorithms are commonly used (e.g. [11], [12]). Authors of [14] and [15] have used an ant colony optimization methods.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In Multi-objective Travelling Salesman Problem (mTSP) multiple aspects of the route are evaluated [12]. It could be cost, time, length or risk of travel.…”

Section: Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations