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A hybrid particle swarm optimization – variable neighborhood search algorithm for constrained shortest path problems

Marinakis Ioannis, Migdalas, Athanasios, Sifaleras, Angelo

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URIhttp://purl.tuc.gr/dl/dias/6017B6C5-4B7D-42BE-8321-C9D19E8A25CB-
Identifierhttps://www.sciencedirect.com/science/article/pii/S0377221717302357?via%3Dihub-
Identifierhttps://doi.org/10.1016/j.ejor.2017.03.031-
Languageen-
Extent16 pagesen
TitleA hybrid particle swarm optimization – variable neighborhood search algorithm for constrained shortest path problemsen
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
CreatorMigdalas, Athanasiosen
CreatorSifaleras, Angeloen
PublisherElsevieren
Content SummaryIn this paper, a well known NP-hard problem, the Constrained Shortest Path problem, is studied. As efficient metaheuristic approaches are required for its solution, a new hybridized version of Particle Swarm Optimization algorithm with Variable Neighborhood Search is proposed for solving this significant combinatorial optimization problem. Particle Swarm Optimization (PSO) is a population-based swarm intelligence algorithm that simulates the social behavior of social organisms by using the physical movements of the particles in the swarm. A Variable Neighborhood Search (VNS) algorithm is applied in order to optimize the particles’ position. In the proposed algorithm, the Particle Swarm Optimization with combined Local and Global Expanding Neighborhood Topology (PSOLGENT), a different equation for the velocities of particles is given and a novel expanding neighborhood topology is used. Another issue in the application of the VNS algorithm in the Constrained Shortest Path problem is which local search algorithms are suitable from this problem. In this paper, a number of continuous local search algorithms are used. The algorithm is tested in a number of modified instances from the TSPLIB and comparisons with classic versions of PSO and with other versions of the proposed method are performed. Also, the results of the algorithm are compared with the results of a number of metaheuristic and evolutionary algorithms. The results obtained are very satisfactory and strengthen the efficiency of the algorithm. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-03-26-
Date of Publication2017-
SubjectConstrained shortest path problemen
SubjectExpanding neighborhood topologyen
SubjectParticle swarm optimizationen
SubjectVariable neighborhood searchen
Bibliographic CitationY. Marinakis, A. Migdalas and A. Sifaleras, "A hybrid particle swarm optimization – variable neighborhood search algorithm for constrained shortest path problems," Eur. J. Oper. Res., vol. 261, no. 3, pp. 819-834, Sept. 2017. doi: 10.1016/j.ejor.2017.03.031en

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