Abstract:By resorting to the respective advantages of the artificial bee colony(ABC)and artificial fish swarm (AFS) algorithms, this work has proposed a hybrid optimization algorithm, referred as to ABC+AFS algorithm, used to optimize the parameters of least square support vector machine (LSSVM) in order to enhance its performance of predicting fluctuating wind velocity. Considering that the AFS algorithm has better ability of skipping over the local optimum, substituting the optimizing process of the employed bees in the ABC algorithm with that of the artificial fishes in the AFS algorithm is thus implemented to cope with the problems that the ABC algorithm easily traps into local optima. Concurrently, adopting the positive and negative feedback mechanism of the ABC algorithm comes over the problem of aimless optimization in the late period of the AFS algorithm. LSSVM for the prediction of fluctuating wind velocity has been established making use of the ABC+AFS algorithm to select its parameters. For a comparison, the results of fluctuating wind velocity forecasting are also taken into consideration using the ABC, AFS, and particle swarm optimization (PSO) algorithm based LSSVM. The numerical results show that the fluctuating wind velocity prediction method of the ABC+AFS algorithm based LSSVM has better prediction performance and a bright prospect of engineering application.
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