Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem

Authors:
Sergio Consoli;José Andrés Moreno-Pérez;Kenneth Darby-Dowman;Nenad Mladenović
Affiliations:
CARISMA and NET-ACE, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK UB8 3PH;DEIOC, IUDR, Universidad de La Laguna, Santa Cruz de Tenerife, Spain 38271;CARISMA and NET-ACE, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK UB8 3PH;CARISMA and NET-ACE, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK UB8 3PH
Venue:
Natural Computing: an international journal
Year:
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.