A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems
•The Constrained Shortest Path problem is solved using a hybridized version of PSO.•A different equation for the particles’ velocities is used.•A novel expanding neighborhood topology is applied.•A VNS algorithm is applied in order to optimize the particles’ position.•The algorithm is tested in a nu...
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| Published in: | European journal of operational research Vol. 261; no. 3; pp. 819 - 834 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
16.09.2017
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| ISSN: | 0377-2217, 1872-6860, 1872-6860 |
| Online Access: | Get full text |
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| Abstract | •The Constrained Shortest Path problem is solved using a hybridized version of PSO.•A different equation for the particles’ velocities is used.•A novel expanding neighborhood topology is applied.•A VNS algorithm is applied in order to optimize the particles’ position.•The algorithm is tested in a number of modified instances from the TSPLIB.
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. |
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| AbstractList | •The Constrained Shortest Path problem is solved using a hybridized version of PSO.•A different equation for the particles’ velocities is used.•A novel expanding neighborhood topology is applied.•A VNS algorithm is applied in order to optimize the particles’ position.•The algorithm is tested in a number of modified instances from the TSPLIB.
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. 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. |
| Author | Migdalas, Athanasios Marinakis, Yannis Sifaleras, Angelo |
| Author_xml | – sequence: 1 givenname: Yannis surname: Marinakis fullname: Marinakis, Yannis email: marinakis@ergasya.tuc.gr organization: School of Production Engineering and Management, Decision Support Systems Laboratory, Technical University of Crete, University Campus, Chania 73100, Greece – sequence: 2 givenname: Athanasios surname: Migdalas fullname: Migdalas, Athanasios email: samig@civil.auth.gr, athmig@ltu.se organization: Department of Civil Engineering, Aristotle University of Thessalonike, 54124 Thessaloniki, Greece – sequence: 3 givenname: Angelo surname: Sifaleras fullname: Sifaleras, Angelo email: sifalera@uom.gr organization: School of Information Sciences, Department of Applied Informatics, University of Macedonia, 156 Egnatias Str., Thessaloniki 54006, Greece |
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| Keywords | Expanding neighborhood topology Variable Neighborhood Search Particle Swarm Optimization Constrained Shortest Path problem |
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| Snippet | •The Constrained Shortest Path problem is solved using a hybridized version of PSO.•A different equation for the particles’ velocities is used.•A novel... In this paper, a well known NP-hard problem, the constrained shortest path problem, is studied. As efficient metaheuristic approaches are required for its... |
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| SubjectTerms | Constrained Shortest Path problem Expanding neighborhood topology Industrial Logistics Industriell logistik Particle Swarm Optimization Variable Neighborhood Search |
| Title | A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems |
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