A novel hybrid PSO-ACO approach with its application to SPP

State-of-the-art in the area of shortest path problem (SPP) is considered to propose a novel hybrid approach, realized based on the well-known particle swarm optimization in association with ant colony optimization. The subject addressed in the present research is worthy of investigation due to the fact that efficient outcomes may be useful, in so many academic and industrial environments, which are encountered optimized path problems. Based on the matter presented, a number of applications of the SPP are in vehicle routing in the transportation systems, traffic routing in the communication networks and path planning in the VLSI design. Regarding the approach considered here, it should be noted that the main aim is to find the SPP between specified point and corresponding destination one, as long as some static obstacles are assumed in terrain. The properties of the present model enable us to organize a meta-heuristic in line with ant colony algorithms to solve the shortest path design. Subsequently, in order to evaluate the proposed approach, the investigated results are compared with other meta-heuristic algorithms. Computational results illustrate that the efficiency of the proposed approach is desirable with respect to other related techniques.

[1]  Abdel Lisser,et al.  Maximum probability shortest path problem , 2015, Discret. Appl. Math..

[2]  Benjamin Doerr,et al.  Tight Analysis of the (1+1)-EA for the Single Source Shortest Path Problem , 2011, Evolutionary Computation.

[3]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[4]  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).

[5]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Robert Schaefer.  Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, Kraków, Poland, September 11-15, 2010. Proceedings, Part II , 2010, PPSN.

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

[8]  S. Pallottino,et al.  Shortest Path Algorithms in Transportation models: classical and innovative aspects , 1997 .

[9]  L. B. Fu,et al.  Expected Shortest Paths in Dynamic and Stochastic Traf c Networks , 1998 .

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

[11]  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).

[12]  Bernardete Ribeiro,et al.  A neural network for shortest path computation , 2001, IEEE Trans. Neural Networks.

[13]  Patrice Marcotte,et al.  Equilibrium and Advanced Transportation Modelling , 2013 .

[14]  Masaharu Munetomo,et al.  A migration scheme for the genetic adaptive routing algorithm , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[15]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[16]  J. Y. Yen.  Finding the K Shortest Loopless Paths in a Network , 1971 .

[17]  Emmanuel Dotaro,et al.  Routing foreseeable lightpath demands using a tabu search meta-heuristic , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[18]  Russell C. Eberhart,et al.  Swarm intelligence for permutation optimization: a case study of n-queens problem , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[19]  Nikola Kasabov,et al.  Evolving personalized modeling system for integrated feature, neighborhood and parameter optimization utilizing gravitational search algorithm , 2015, Evol. Syst..

[20]  Leticia C. Cagnina,et al.  Particle swarm optimization for sequencing problems: a case study , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[21]  J. Deneubourg,et al.  The self-organizing exploratory pattern of the argentine ant , 1990, Journal of Insect Behavior.

[22]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[23]  Fei Long,et al.  Immune optimization approach solving multi-objective chance-constrained programming , 2015, Evol. Syst..

[24]  Manuela M. Veloso,et al.  Depth-based short-sighted stochastic shortest path problems , 2014, Artif. Intell..

[25]  Lawrence Mandow,et al.  Multiobjective shortest path problems with lexicographic goal-based preferences , 2014, Eur. J. Oper. Res..

[26]  Jens Vygen,et al.  A generalization of Dijkstra's shortest path algorithm with applications to VLSI routing , 2009, J. Discrete Algorithms.

[27]  Frank Neumann,et al.  More Effective Crossover Operators for the All-Pairs Shortest Path Problem , 2010, PPSN.

[28]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[29]  Tadao Takaoka.  Sharing information for the all pairs shortest path problem , 2014, Theor. Comput. Sci..