A support vector machine feature selection and parameter optimization algorithm based on fruit fly optimization is proposed. It imitates the foraging behavior of fruit flies, takes the taste concentration judgment value of food as a parameter, and encodes the feature set into binary code to obtain a feature subset for training the model. Then, a suitable fitness function is constructed to search for the optimal parameter value and feature subset. Experimental comparison with other algorithms shows that this method has the advantages of high classification accuracy and strong global search ability. It is applied to the fault diagnosis of rolling bearings, and the simulation results show that the model has good performance.
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