Abstract
According to the literature of particle swarm optimization (PSO), there are problems of local minima and premature convergence with this algorithm. A new algorithm is presented called the improved particle swarm optimization using the gradient descent method as operator of particle swarm incorporated into the Algorithm, as a function to test the improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The improved PSO algorithm IPSO is applied to Benchmark Functions. The results show that there is an improvement with respect to using the conventional PSO algorithm.
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Uriarte, A., Melin, P., Valdez, F. (2017). A New Hybrid PSO Method Applied to Benchmark Functions. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_28
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DOI: https://doi.org/10.1007/978-3-319-47054-2_28
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