Abstract
This paper presents the Scatter Search Particle Filter (SSPF) algorithm and its application to the Dynamic Travelling Salesman Problem (DTSP). SSPF combines sequential estimation and combinatorial optimization methods to improve the execution time in dynamic optimization problems. It allows obtaining new high quality solutions in subsequent iterations using solutions found in previous time steps. The hybrid SSPF approach increases the performance of general Scatter Search (SS) metaheuristic in dynamic optimization problems. We have applied the SSPF algorithm to different DTSP instances. Experimental results have shown that SSPF performance is significantly better than classical DTSP approaches, where new solutions of derived problems are obtained without taking advantage of previous solutions corresponding to similar problems. Our proposal reduces execution time appreciably without affecting the quality of the estimated solution.
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References
Randall, M.: Constructive Meta-heuristics for Dynamic Optimization Problems. Technical Report. School of Information Technology. Bond University (2002)
Sadeh, N., Kott, A.: Models and Techniques for Dynamic Demand-Responsive Transportation Planning. Tech. Rept. TR-96-09, Carnegie Mellon University (1996)
Dror, M., Powell, W.: Stochastic and Dynamic Models in Transportation. Operations Research 41, 11–14 (1993)
Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: The displacement Problem and Dynamically Scheduling Aircraft Landings. Working paper (2002), Available online at http://graph.ms.ic.ac.uk/jeb/displace.pdf
Beasley, J., Sonander, J., Havelock, P.: Scheduling Aircraft Landings at London Heathrow using a Population Heuristic. Journal of the Operational Research Society 52, 483–493 (2001)
Glover, F., Kochenberger, G.A.: Handbook of metaheuristics. Kluwer, Dordrecht (2002)
Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation 1(1), 53–66 (1997)
Zhang-Can, H., Xiao-Lin, H., Si-Duo, C.: Dynamic traveling salesman problem based on evolutionary computation. In: Proc. of Evolutionary Computation Conf. 2, pp. 1283–1288 (2001)
Carpenter, J., Clifford, P., Fearnhead, P.: Building robust simulation based filters for evolving data sets. Tech. Rep., Dept. Statist., Univ. Oxford, Oxford, U.K (1999)
Arulampalam, M., et al.: A Tutorial on Particle Filter for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. On Signal Processing 50(2), 174–188 (2002)
Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys 35(3), 268–308 (2003)
Eyckelhof, C.J., Snoek, M.: Ant Systems for A Dynamic DSP: Ants Caught in a Traffic Jam. In: Proc. of ANTS 2002 Conference (2002)
Karp, R.M.: Reducibility among Combinatorial Problems. In: Miller, R., Thatcher, J. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)
Reinelt, G.: TSPLIB. University of Heidelberg (1996), Available online at http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/
Guntsh, M., Middendorf, M.: Applying Population based ACO to Dynamic Optimization Problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)
Guntsh, M., Middendorf, M., Schmeck, H.: An Ant Colony Optimization Approach to Dynamic TSP. In: Proc. GECCO 2001 Conference, pp. 860–867. Morgan Kaufmann Publishers, San Francisco (2000)
Guntsh, M., Middendorf, M.: Pheromone Modification Strategies for Ant Algorithms applied to Dynamic TSP. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)
Pantrigo, J.J., Sánchez, A., Gianikellis, K., Duarte, A.: Path Relinking Particle Filter for Human Body Pose Estimation. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 653–661. Springer, Heidelberg (2004)
Glover, F.: A Template for Scatter Search and Path Relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363, pp. 1–53. Springer, Heidelberg (1998)
Laguna, M., Marti, R.: Scatter Search methodology and implementations in C. Kluwer Academic Publisher, Dordrecht (2003)
Vizeacoumar, F.: TSP Implementation. Project report Combinatorial Optimization CMPUT – 670
Campos, V., Laguna, M., Martí, R.: Scatter Search for the Linear Ordering Problem. New Ideas in Optimization. McGraw-Hill, New York (1999)
Michalewitz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)
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Pantrigo, J.J., Duarte, A., Sánchez, Á., Cabido, R. (2005). Scatter Search Particle Filter to Solve the Dynamic Travelling Salesman Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_17
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DOI: https://doi.org/10.1007/978-3-540-31996-2_17
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