In order to solve the problems of high computational complexity, strong parameter dependence and weak global optimization ability of traditional swarm intelligence optimization algorithm, a fruit fly optimization algorithm based on double drive with the theory of bacterial chemotaxis is proposed. Considering the distribution of the superior and the inferior fruit fly groups, the concepts of multiple repellents and multiple attractants are proposed, and the location of fruit fly is updated under the double drive, so as to avoid the invalid search of the traditional methods which only depend on the local best (worst) position during the updating process. Then, based on the fitness value information of the fruit flies, a weighted centroid vector calculation method of multiple repellents and multiple attractants is proposed to determine the searching radiuses of fruit flies adaptively and avoid the problem of strong parameter dependence faced by traditional methods. The experimental results on standard functions show that, the proposed method has lower parameter dependence, higher convergence accuracy and convergence speed than existing typical algorithms. Moreover, the PID controller optimized by the proposed method has high response speed and stability, showing the ability of the proposed algorithm on PID parameter optimization.