Server Fault Warning Method Based on IQPSO-GA Optimization of ANFIS Model
Aiming at the hardware failure of server bottom business on the stable operation of the system,a hybrid meta-heuristic optimization algorithm combining the improved quantum behavior particle swarm optimization(IQPSO)and genetic algorithm(GA)was proposed to train the parameters of adaptive neural fuzzy reasoning system(ANFIS),and obtain more accurate ANFIS rules for hardware fault warning.Firstly,by analyzing the mapping relationship between server business and hardware related parameters,the ANFIS model is trained to construct the prediction model through the collected data set.Secondly,by considering the problem that the ANFIS is prone to fall into the local optimal value in the gradient calculation process,an IQPSO algorithm is designed to globally search the ANFIS rule parameters by combining the crossover and mutation operators in the GA mixed meta-heuristic algorithm.Fi-nally,a set of post-processing sample data sets were used to test the effectiveness and stability of the proposed method.Experimental results show that the proposed method can effectively warn the hardware failures of the server.Based on the proposed hybrid element heuristic optimization algorithm,the ANFIS model has faster convergence speed and higher global search accuracy,and the generali-zation accuracy of the algorithm is over 47％higher than that of the traditional ANFIS model.
万方数据
维普
