Published: 2006 Dezember
Type: Technical Report
Institution: Institute AIFB University of Karlsruhe
In recent years, a number of authors have successfully extended particle swarm optimization to problem domains with multiple objectives. This paper addresses the issue of parallelizing multi-objective particle swarms. We propose and empirically compare two parallel versions which differ in the way they divide the swarm into subswarms that can be processed independently on different processors. One of the variants works asynchronously and is thus particularly suitable for heterogeneous computer clusters as occurring e.g. in modern grid computing platforms.
Evolutionäre Algorithmen, Parallele Algorithmen, Grid Computing, Multikriterielle Optimierung, Genetische Algorithmen, Naturanaloge Algorithmen, Optimierung