Due to the weak development ability, Particle Swarm Optimization (PSO) algorithms have the shortages of low precision and slow convergence. Comparatively weak exploration ability of Differential Evolution (DE) algorithm, 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 operators 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 benchmark 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 values of the algorithm. The convergences of Rosenbrock, Rastrigin, Schwefel and Salomon functions are 1~3 orders of magnitude higher than those of four contrast particle swarm optimization algorithms. At the same time, the convergence of the proposed algorithm is 5%-30% higher than that of the contrast algorithms. The proposed algorithm has significant effects on improving convergence speed and precision, and has strong capabilities in escaping from the local extremum and global searching.

To address the problems of high global query sensitivity and low utility of mining results in the existing works, a Lattice-Trajectory Pattern Mining (LTPM) algorithm based on prefix sequence lattice and trajectory truncation was proposed for mining sequential patterns with differential privacy. An adaptive method was employed to obtain the optimal truncation length, and a dynamic programming strategy was used to truncate the original database. Based on the truncated database, the equivalent relation was used to construct the prefix sequence lattice for mining trajectory patterns. Theoretical analysis shows that LTPM satisfies ε-differential privacy. The experimental results show that the True Postive Rate (TPR) and Average Relative Error (ARE) of LTPM are better than those of N-gram and Prefix algorithms, which verifies that LTPM can effectively improve the utility of the mining results.