Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics

Mohemmed, Ammar W.; Sahoo, Nirod Chandra

doi:https://doi.org/10.1155/2007/27383

Discrete Dynamics in Nature and Society

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Research Article | Open Access

Volume 2007 | Article ID 027383 | https://doi.org/10.1155/2007/27383

Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics

Ammar W. Mohemmed¹and Nirod Chandra Sahoo¹

Received13 Mar 2007

Accepted04 Apr 2007

Published21 Jun 2007

Abstract

This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising metaheuristics for solving the single-source shortest-path problem (SPP) commonly encountered in graph theory. This hybrid search process combines PSO for iteratively finding a population of better solutions and noising method for diversifying the search scheme to solve this problem. A new encoding/decoding scheme based on heuristics has been devised for representing the SPP parameters as a particle in PSO. Noising-method-based metaheuristics (noisy local search) have been incorporated in order to enhance the overall search efficiency. In particular, an iteration of the proposed hybrid algorithm consists of a standard PSO iteration and few trials of noising scheme applied to each better/improved particle for local search, where the neighborhood of each such particle is noisily explored with an elementary transformation of the particle so as to escape possible local minima and to diversify the search. Simulation results on several networks with random topologies are used to illustrate the efficiency of the proposed hybrid algorithm for shortest-path computation. The proposed algorithm can be used as a platform for solving other NP-hard SPPs.

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Copyright

Copyright © 2007 Ammar W. Mohemmed and Nirod Chandra Sahoo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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