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Self-organized Invasive Parallel Optimization

Self-organized Invasive Parallel Optimization

Published: 2011 Juni

Buchtitel: Proceedings of the International Workshop on Bio-inspired Approaches for Distributed Computing (BADS)
Seiten: 49-56
Verlag: ACM
Erscheinungsort: Karlsruhe
Organisation: International Conference on Autonomic Computing (ICAC 2011)

Referierte Veröffentlichung


Self-organized Invasive Parallel Optimization (SIPO) is a new framework for solving optimization problems on parallel platforms. In contrast to existing approaches, the resources in SIPO are self-organized and represented as a unified resource to the user who specifies the optimization problem and its preferences to the system. SIPO starts working with one resource and automatically divides the optimization task stepwise into smaller tasks which are assigned to more resources. This job assignment is decided on demand by the resources. The novelty here is that there is no need to specify the number of parallel computing resources in the beginning of the optimization. This number is estimated during the optimization process by the resources. The proposed new framework of SIPO is described in this paper with respect to multi-objective optimization problems but it has a much larger scope. A comparative evaluation of using SIPO in multi-objective optimization problems shows that this adaptive approach can obtain equally good or sometimes even better solutions than other parallel and non-parallel methods which are not self-organized.

Download: Media:Bads04a-mostaghim.pdf


Effiziente Algorithmen


Organic Computing, Parallele Algorithmen, Multikriterielle Optimierung, Optimierung