Support Vector Machine Optimization Based on Artificial Bee Colony Algorithm

A classification performance of support vector machine is largely dependent on the choice of its parameters.A parameter optimization method based on artificial bee colony algorithm is proposed to solve this problem and applied to intelligent motor bearing fault diagnosis.In this method,the inverse of classification error rate is used as fitness value,and the artificial bee colony algorithm is used to optimize the penalty factor and kernel parameter of support vector machine.Compared with genetic algorithm and other optimization algorithms on standard datasets,the proposed algorithm can overcome the local optimal solution problem and acquire higher classification precision,and it costs less running time on small classification number of classification problem.Then the proposed method is applied to the recognition of bearing fault signals.The wavelet transform is applied to the bearing fault signals and the normalized energy values of every frequency band are extracted to compose feature vectors.The proposed method is used as the classifier and high classification precision is acquired.