Notice of Retraction A fuzzy-particle swarm optimization based algorithm for solving shortest path problem

In this paper, an efficient particle swarm optimization (PSO) algorithm based on fuzzy logic for solving the single source shortest path problem (SPP) is proposed. A particle encoding/decoding scheme has been devised for particle-representation of the SPP parameters, which is free of the previously randomized path construction methods in computational problems like the SPP .The search capability of PSO is diversified by hybridizing the PSO with fuzzy logic. The local optimums will not be the point of convergence for the particles and the global optimum will be found in a shorter period of time if the PSO is correctly modified using fuzzy logic rules. Numerical computation results on several networks with random weights illustrate the efficiency of the proposed method for computation of the shortest paths in networks.

[1]  Chung G. Kang,et al.  Shortest path routing algorithm using Hopfield neural network , 2001 .

[2]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  M. H. Noroozi Beyrami Improving Particle Swarm Optimization using Fuzzy Logic , 2008 .

[4]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[5]  BERNARD M. WAXMAN,et al.  Routing of multipoint connections , 1988, IEEE J. Sel. Areas Commun..

[6]  Faouzi Kamoun,et al.  Neural networks for shortest path computation and routing in computer networks , 1993, IEEE Trans. Neural Networks.

[7]  J. Moy,et al.  Open Shortest Path First version 2 , 1998 .

[8]  Ajith Abraham,et al.  Fuzzy adaptive turbulent particle swarm optimization , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[9]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[10]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[11]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[12]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[13]  Ammar W. Mohemmed,et al.  Solving shortest path problem using particle swarm optimization , 2008, Appl. Soft Comput..

[14]  Chunguang Zhou,et al.  Particle swarm optimization for traveling salesman problem , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[15]  菅野 道夫,et al.  Industrial applications of fuzzy control , 1985 .

[16]  F. Benjamin Zhan,et al.  Shortest Path Algorithms: An Evaluation Using Real Road Networks , 1998, Transp. Sci..

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[18]  Dong-Chul Park,et al.  A neural network based multi-destination routing algorithm for communication network , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[19]  Giorgio Gallo,et al.  Shortest path algorithms , 1988, Handbook of Optimization in Telecommunications.

[20]  Narsingh Deo,et al.  Shortest-path algorithms: Taxonomy and annotation , 1984, Networks.

[21]  Guy Desaulniers,et al.  An efficient algorithm to find a shortest path for a car-like robot , 1993, IEEE Trans. Robotics Autom..

[22]  Dingwei Wang,et al.  Genetic algorithms for solving shortest path problems , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).