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.
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Monson, C.K., Seppi, K.D. (2004). The Kalman Swarm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_13
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DOI: https://doi.org/10.1007/978-3-540-24854-5_13
Publisher Name: Springer, Berlin, Heidelberg
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