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A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems.

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Title: A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems.
Authors: Marinakis, Yannis1 marinakis@ergasya.tuc.gr, Migdalas, Athanasios2,3 athmig@ltu.se, Sifaleras, Angelo4 sifalera@uom.gr
Source: European Journal of Operational Research. Sep2017, Vol. 261 Issue 3, p819-834. 16p.
Subjects: Particle swarm optimization, Swarm intelligence, Search algorithms, Metaheuristic algorithms, Evolutionary algorithms
Abstract: In 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. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems.
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  Data: <searchLink fieldCode="AR" term="%22Marinakis%2C+Yannis%22">Marinakis, Yannis</searchLink><relatesTo>1</relatesTo><i> marinakis@ergasya.tuc.gr</i><br /><searchLink fieldCode="AR" term="%22Migdalas%2C+Athanasios%22">Migdalas, Athanasios</searchLink><relatesTo>2,3</relatesTo><i> athmig@ltu.se</i><br /><searchLink fieldCode="AR" term="%22Sifaleras%2C+Angelo%22">Sifaleras, Angelo</searchLink><relatesTo>4</relatesTo><i> sifalera@uom.gr</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Sep2017, Vol. 261 Issue 3, p819-834. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink>
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  Label: Abstract
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  Data: In 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ejor.2017.03.031
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        Text: English
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        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Search algorithms
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      – SubjectFull: Metaheuristic algorithms
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              Text: Sep2017
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