In order to solve the problem that the basic state transition algorithm shows slow convergence speed and low convergence accuracy in some complex high dimensional functions, a hybird state transition algorithm is proposed, which can improve the local search ability of the algorithm and accelerate the convergence speed of the algorithm by adding a local search quasi-Newton operator. Besides, a strategy is proposed to call the quasi-Newton operator adaptively, which can judge the time when the algorithm converges to the vicinity of the global optimum, and then calls the quasi-Newton operator to give full play to its advantages of strong local search ability. The proposed method is successfully applied to the wireless network sensor location. Compared with other intelligent optimization algorithms, the hybird intelligence has the characteristics of faster convergence and higher accuracy.