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The Kalman Swarm
A New Approach to Particle Motion in Swarm Optimization
Christopher K. Monson and Kevin D. Seppi
Brigham Young University, Provo UT 84602, USAc@cs.byu.edu
kseppi@cs.byu.edu
Abstract. Particle Swarm Optimization is gaining momentum as a simple and effective optimization technique. We present a new approach to PSO that significantly reduces the number of iterations required to reach good solutions. In contrast with much recent research, the focus of this work is on fundamental particle motion, making use of the Kalman Filter to update particle positions. This enhances exploration without hurting the ability to converge rapidly to good solutions.
LNCS 3102, p. 140 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004