Abstract：In order to solve the problem that the selection of the kernel function parameters and penalty factor parameters in the support vector machine(SVM)algorithm is blindfold，we use the fruit fly optimization algorithm(FOA)to optimize the parameters in SVM．A fault diagnosis algorithm of SVM based on FOA is put forward，and then we use it to execute the pattern recognition of the turbine failure experimental data．This algorithm could optimize the SVM parameters automatically，and achieve ideal global optimal solution．Comparing with the SVM which optimized by the common used methods of the particle swarm optimization(PSO) and the Genetic Algorithm (GA) currently，the results demonstrate that FOA-SVM has the fastest recognition speed and the highest recognition rate．
石志标;苗莹. 基于FOA-SVM的汽轮机振动故障诊断[J]. , 2014, 33(22): 111-114.
Shi Zhi-biao;Miao-Ying. Vibration Fault Diagnosis for Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Machine. , 2014, 33(22): 111-114.
2014, Vol. 33 
