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Title: A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems
Authors: Marinakis, Yannis
Migdalas, Athanasios
Sifaleras, Angelo
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Particle Swarm Optimization
Variable Neighborhood Search
Expanding Neighborhood Topology
Constrained Shortest Path Problem
Issue Date: 2017
Publisher: Elsevier
Source: European Journal of Operational Research
Volume: 261
Issue: 3
First Page: 819
Last Page: 834
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. The results obtained are very satisfactory and strengthen the efficiency of the algorithm.
ISSN: 03772217
Other Identifiers: 10.1016/j.ejor.2017.03.031
Appears in Collections:Department of Applied Informatics

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