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Natural Computing
Erschienen in: Natural Computing 1/2010

01.03.2010

Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem

verfasst von: Sergio Consoli, José Andrés Moreno-Pérez, Kenneth Darby-Dowman, Nenad Mladenović

Erschienen in: Natural Computing | Ausgabe 1/2010

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Abstract

Particle Swarm Optimization is a population-based 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 improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of 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.
Vorheriger Artikel Honey Bees Mating Optimization algorithm for large scale vehicle routing problems
Nächster Artikel A memetic algorithm for the generalized traveling salesman problem

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Metadaten
Titel
Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem
verfasst von
Sergio Consoli
José Andrés Moreno-Pérez
Kenneth Darby-Dowman
Nenad Mladenović
Publikationsdatum
01.03.2010
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 1/2010
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-009-9137-9

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