关键词: 关键词: 模糊, 高斯学习, 粒子群, 微分进化, 优化算法

Abstract: Due to the weak development ability of the Particle Swarm Optimization algorithms, it has the features of low precision, slow convergence rate and comparatively weak exploration ability of Differential Evolution algorithm, which might further lead to a trap in the local extremum. A Particle Swarm-Differential Evolution fusion algorithm based on fuzzy Gaussian learning strategy was proposed. On the basis of the standard particle swarm algorithm, the elite particle population was selected, and the fusion mechanism of elite particle swarm-evolution was constructed by using mutation, crossover and selection evolution operator to improve particle diversity and convergence. A fuzzy Gaussian learning strategy according with human thinking characteristics was introduced to improve particle optimization ability, and further generate an Elite Particle Swarm and Differential Evolution fusion algorithm based on fuzzy Gaussian learning strategy. Nine workbench functions were calculated and analyzed in this thesis. The results show that the mean values of the functions Schwefel.1.2, Sphere, Ackley, Griewank and Quadric Noise are respectively 1.5E-39, 8.5E-82, 9.2E-13, 5.2E-17, 1.2E-18, close to the minimum value of the algorithm. The convergence of Rosenbrock, Rastrigin, Schwefel and Salomon functions is 1~3 orders of magnitude higher than that of four contrast particle swarm optimization algorithms. At the same time, the convergence of the convergence algorithm is 5%~30% higher than that of the contrast algorithm. The algorithm has telling effects on improving the convergence speed and precision, and has significant capabilities in avoiding the local extremum and escaping global search.

Key words: Keywords: Fuzzy, Gaussian learning, particle swarm, differential evolution, optimization algorithm