An improved cuckoo search algorithm and its application in vibration fault diagnosis for a hydroelectric generating unit

Abstract

This article presents an improved cuckoo search (ICS) algorithm based on the parametric adaptation mechanism and non-uniform mutation. In the ICS algorithm, the biased random walk operator is modified to take advantage of the neighbourhood information of the current solution, and then the control parameters of step size, discovery probability and scaling factor are directly integrated into the optimized problems. Meanwhile, the non-uniform mutation operation is used to adaptively tune the search step of the current optimal solution. To evaluate the feasibility and efficiency of the presented algorithm, three groups of benchmark test functions are employed to perform the validation analysis. Subsequently, to diagnose the vibration fault of a hydroelectric generating unit, a combinational model is built, which combines ICS with a back-propagation neural network (ICSBP). The experimental results indicate that ICS is competitive on the optimization problems, and the ICSBP approach can effectively improve the accuracy of vibration fault diagnosis for a hydroelectric generating unit.