1.Tianjin University of Finance and Economics;2.Central University of Finance and Economics
The National Social Science Foundation of China ；the National Natural Science Foundation of China； the Ministry of education of Humanities and Social Science project
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 new fruit fly optimization algorithm based on double drive with the theory of bacterial chemotaxis was proposed. Considering the distribution of the superior and the inferior fruit fly groups, the concepts of multiple repellents and multiple attractants were proposed, and the location of fruit fly was updated under the double drive, so as to avoid the invalid search of the traditional methods which only depend on the local best (worst) fruit flies of the position 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 was 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.