In order to solve complex problems, the gray wolf optimization algorithm has some shortcomings, such as relying on the initial population, low convergence accuracy and getting easily trapped into local optima. An improved grey wolf optimization algorithm （Quantum Gray Wolf Optimization Algorithm, QGWO） combining sinusoidal control factor and quantum local search is proposed. The control factors of Gray Wolf algorithm are changed according to the curve with sine change. The improved algorithm accelerates the convergence speed in the early stage of iteration to complete the global exploration quickly, and slows down the convergence speed in the late iteration to improve the accuracy of the algorithm. At the same time, quantum local search is introduced to reduce the probability of the algorithm falling into local optimum. Then, twelve standard test functions are selected to verify the performance of QGWO algorithm, and the single peak, multi peak and fixed dimension test functions are compared. The experimental results show that compared with GWO, WOA, SCA and CGWO, QGWO has higher accuracy and stability in solving test functions. Finally, an engineering example is used to optimize KELM for classification experiments. The results show that QGWO has better optimization performance.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2012-0080