Abstract
We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic for parameter adaptation and integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid FPSO+FGA method is shown to be superior than the individual evolutionary methods on the set of benchmark functions.
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Valdez, F., Melin, P. (2010). A New Evolutionary Method with Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Systems to Dynamically Parameter Adaptation. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_14
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DOI: https://doi.org/10.1007/978-3-642-15111-8_14
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