Abstract
This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the Vehicle Routing Problem with Time Windows (VRPTW). The proposed algorithm incorporates some principles from multi-objective optimization to allow particles to conduct a dynamic trade-off between objectives in order to reach feasibility. The main contribution of this paper is to demonstrate that without incorporating tailored heuristics or operators to tackle infeasibility, it is possible to evolve very poor infeasible route-plans to very good feasible ones using swarm intelligence.
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References
The VRP Web, http://neo.lcc.uma.es/radi-aeb/WebVRP/
Bräysy, O., Gendreau, M.: Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms. Transportation Science 39(1), 104–118 (2005)
Bräysy, O., Gendreau, M.: Vehicle Routing Problem with Time Windows, Part II: Metaheuristics. Transportation Science 39(1), 119–139 (2005)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 IEEE Congress on Evolutionary Computation, pp. 81–86 (2001)
Reyes-Sierra, M., Coello Coello, C.A.: International Journal of Computational Intelligence Research. Research India Publications, 287–308 (2006)
Moore, J., Chapman, R.: Application of Particle Swarm to Multiobjective Optimization, Department of Computer Science and Software Engineering. Auburn University (1999)
Coello-Coello, C.A., Salazar Lechuga, M.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE Congress on Computational Intelligence, pp. 12–17 (2002)
Coello Coello, C.A., Pulido, G.T., Salazar Lechuga, M.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)
Hu, X., Eberhart, R.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1677–1681 (2002)
Hu, X., Eberhart, R.C., Shi, Y.: Particle Swarm Optimization with extended memory for multiobjective optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003, pp. 193–197 (2003)
Fieldsend, J.E., Singh, S.: A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. In: UK Workshop on Computational Intelligence (UKCI 2002), pp. 37–44 (2002)
Parsopoulos, K.E., Vrahatis, M.N.: Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization. In: Natural Computing, pp. 235–306. Springer, Heidelberg (2002)
Parsopoulos, K.E., Vrahatis, M.N.: On the Computation of All Global Minimizers Through Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8, 211–224 (2004)
Santana-Quintero, L.V., Ramírez-Santiago, N., Coello Coello, C.A., Molina-Luque, J., Hernández-Díaz, A.G.: A new proposal for multiobjective optimization using particle swarm optimization and rough sets theory. LNCS, pp. 483–492. Springer, Heidelberg (2006)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm optimization algorithm. In: Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, pp. 4104–4109 (1997)
Chang, R.F., Lu, C.N.: Feeder reconfiguration for load facto improvement. In: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, pp. 980–984 (2002)
Mohan, C.K., Al-kazemi, B.: Discrete Particle Swarm Optimization. In: Proceeding of the Workshop on Particle Swarm Optimization (2001)
Yang, S., Wang, M., Jiao, L.: A quantum particle swarm optimization. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, vol. 1, pp. 320–324 (2004)
Al-kazemi, B., Mohan, C.K.: Multi-phase discrete particle swarm optimization. In: Proceedings of the Fourth International Workshop on Frontiers in Evolutionary Algorithms (2000)
Combining particle swarm optimisation with angle modulation to solve binary problems. In: IEEE Congress on Evolutionary Computing, vol. 1, pp. 89–96 (2005)
Javier Martinez Garcia, F., Moreno Perez, J.A.: Jumping Frogs Optimization: a new swarm method for discrete optimization. Documentos de Trabajo del DEIOC. N. 3/2008, Universidad de La Laguna (2008)
Martinez, F.J., Moreno, J.A.: Discrete Particle Swarm Optimization for the p-median problem. In: MIC 2007, Metaheuristics International Conference, Montreal, Canada (2007)
Consoli, S., Moreno-Perez, J.A., Darby-Dowman, K., Mladenovic, N.: Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol. 129, pp. 313–322 (2007)
Best Known Solutions Identified by Heuristics for Solomon’s, Benchmark Problems (1987), http://www.sintef.no/static/am/opti/projects/top/vrp/bknown.html
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Castro, J.P., Landa-Silva, D., Pérez, J.A.M. (2009). Exploring Feasible and Infeasible Regions in the Vehicle Routing Problem with Time Windows Using a Multi-objective Particle Swarm Optimization Approach. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_9
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DOI: https://doi.org/10.1007/978-3-642-03211-0_9
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