Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

• Published in: Applied soft computing 2015-04, Vol.29, p.424-435
• Main Authors: García-Galán, S., Prado, R.P., Muñoz Expósito, J.E.
• Format: Article
• Language: English
• Subjects: Fuzzy classifier systems; Fuzzy computing; Grid computing; Particle swarm optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.11.064

•Proposal to improve scheduling in grid computing based on soft computing.•Fuzzy Classifier Systems are used as grid schedulers.•Critical aspect in these grid schedulers: knowledge acquisition.•New rules discovery strategy based on PSO is proposed, KARP.•Higher quality of knowledge allows a more efficient scheduling in the grid. [Display omitted] Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.