Abstract
We describe in this paper an approach for mathematical function optimization using parallel computing combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) and Fuzzy Logic for parameter adaptation and integrate the results. The parallel evolutionary method combines the advantages of parallel computing, PSO and GA to give us an improved parallel FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The parallel hybrid FPSO+FGA method was developed using a computer with processor Intel Core 2 Quad of 64 bits that works to a frequency of clock of 2.5 GHz, 6 GB of RAM Memory and Ubuntu Linux Operating System.
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Valdez, F., Melin, P., Castillo, O. (2010). Fuzzy Control for Dynamical Parameter Adaptation in a Parallel Evolutionary Method Combining Particle Swarm Optimization and Genetic Algorithms. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_10
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DOI: https://doi.org/10.1007/978-3-642-15534-5_10
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