Abstract
Particle Swarm Optimization (PSO) is a nature inspired population-based approach successfully used as an optimization tool in many application. Estimation of distribution algorithms (EDAs), are evolutionary algorithms that try to estimate the probability distribution of the good individuals in the population. In this work, we present a new PSO algorithm that borrows ideas from EDAs. This algorithm is implemented and compared to previous PSO and EDAs hybridization approaches using a suite of well-known benchmark optimization functions.
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El-Abd, M., Kamel, M.S. (2009). PSO_Bounds: A New Hybridization Technique of PSO and EDAs. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_17
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DOI: https://doi.org/10.1007/978-3-642-01085-9_17
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