关键词: 高斯变异, 花朵授粉算法（FPA）, Powell法, 最优值, 寻优能力

Abstract: Flower pollinate algorithm (FPA) is a novel swarm intelligence optimization algorithm which is proposed recently, and it has been widely researched and used because of its advantages of solving the balance problem of local search and global search, and having less parameters, being implemented easily and so on. However, there are less current researches on the parameter, and the speed of convergence in the later stage is slow, what is more, it is easy to fall into local optimizations, which incline to restrict the application of the FPA. In order to improve the overall performance of the FPA, firstly, this paper modifies the scaling factor of the control step size. Secondly, this paper proposes a hybrid algorithm GMPFPA (flower pollination algorithm combination with Gauss mutation and Powell search method). The Gauss mutation is utilized to perturb the global search of the GMPFPA, which enhances the diversity of population of the GMPFPA, and improves the global detection ability of the GMPFPA, and then the strong local search capability of the Powell search method is introduced to enhance the local development ability of the hybrid algorithm. Through the comparison experiment of 12 high dimensional classical test functions, the effectiveness and superiority of the improved algorithm are verified.

Key words: Gauss mutation, flower pollination algorithm (FPA), Powell method, optimum value, optimization ability