Abstract
Particle Swarm Optimization is an evolutionary method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The evolution is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of the inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.
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References
Al-kazemi B, Mohan CK (2002) Multi-phase Discrete Particle Swarm Optimization. In: Fourth International Workshop on Frontiers in Evolutionary Algorithms, Kinsale, Ireland
Cerulli R, Fink A, Gentili M, Voß S (2005) Metaheuristics comparison for the minimum labelling spanning tree problem. In: Golden BL, Raghavan S, Wasil EA (eds) The Next Wave on Computing, Optimization, and Decision Technologies, Springer-Verlag, New York, pp 93–106
Cerulli R, Fink A, Gentili M, Voß S (2006) Extensions of the minimum labelling spanning tree problem. Journal of Telecommunications and Information Technology 4:39–45
Chang RS, Leu SJ (1997) The minimum labelling spanning trees. Information Processing Letters 63(5):277–282
Correa ES, Freitas AA, Johnson CG (2006) A new discrete particle swarm algorithm applied to attribute selection in a bioinformatic data set. In: Proceedings of GECCO 2006, pp 35–42
Duin C, Voß S (1999) The Pilot Method: A strategy for heuristic repetition with applications to the Steiner problem in graphs. Wiley InterScience 34(3):181–191
Garey MR, Graham RL, Johnson DS (1977) The complexity of computing Steiner minimal trees. SIAM Journal on Applied Mathematics 32:835–859
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 4th IEEE International Conference on Neural Networks, Perth, Australia, pp 1942–1948
Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: IEEE Conference on Systems, Man, and Cybernetics, vol 5, pp 4104–4108
Kennedy J, Eberhart R (2001) Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, CA
Krumke SO, Wirth HC (1998) On the minimum label spanning tree problem. Information Processing Letters 66(2):81–85
Martí R (2003) Multi-Start Methods. In: Glover F, Kochenberger G (eds) Handbook in Metaheuristics, Kluwer Academic Publishers, pp 335–368
Moreno-Pérez JA, Castro-Gutiérrez JP, Martínez-García FJ, Melián B, Moreno-Vega JM, Ramos J (2007) Discrete Particle Swarm Optimization for the p-median problem. In: Proceedings of the 7th Metaheuristics International Conference, Montréal, Canada
Pugh J, Martinoli A (2006) Discrete multi-valued particle swarm optimization. In: Proceedings of IEEE Swarm Intelligence Symposium, vol 1, pp 103–110
Voß S (2000) Modern heuristic search methods for the Steiner tree problem in graphs. In: Du DZ, Smith JM, Rubinstein JH (eds) Advances in Steiner tree, Kluwer, Boston, pp 283–323
Xiong Y, Golden B, Wasil E (2006) Improved heuristics for the minimum labelling spanning tree problem. IEEE Transactions on Evolutionary Computation 10(6):700–703
Yang S, Wang M, Jiao L (2004) A Quantum Particle Swarm Optimization. In: Proceedings of CEC2004, the Congress on Evolutionary Computing, vol 1, pp 320–324
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Consoli, S., Pérez, J.A.M., Darby-Dowman, K., Mladenović, N. (2008). Discrete Particle Swarm Optimization for the Minimum Labelling Steiner Tree Problem. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_28
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DOI: https://doi.org/10.1007/978-3-540-78987-1_28
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