Focusing on the issue that the classical rough set can only deal with discrete attributes, a discretization algorithm based on Adaptive Hybrid Particle Swarm Optimization (AHPSO) was proposed. Firstly, the adaptive adjustment strategy was introduced, which could not only overcome the shortage that the particle swarm was easy to fall into local extremum but also improve the ability of seeking the global excellent result. Secondly, the Tabu Search (TS) method was introduced to deal with the global optimal particle of each generation and to get the best global optimal particle, which enhanced the local search ability of particle swarm. Finally, the attribute discretization points were initialized to the particle group when the classification ability of the decision table had been kept. The optimal discretization points were sought through the interaction between particles. By using the classification method of J48 decision tree based on WEKA (Waikato Environment for Knowledge Analysis) platform, compared with the discretization algorithms based on importance of attribute and information entropy, the classification accuracy of the proposed algorithm improved by about 10% to 20%.Compared with the discretization algorithms based on Niche Discrete PSO (NDPSO) and linearly decreasing weight PSO, the classification accuracy of the proposed algorithm improved by about 2% to 5%. The experimental results show that the proposed algorithm significantly enhances the accuracy of classification by J48 decision tree, and it has better validity for discretization of continuous attributes.

Since the fully connected topology of particle swarm algorithm has low convergence precision and easily falls into local extremum, an approach for automatically generating structural test data based on a hybrid particle swarm algorithm named HPSO (Hybrid Particle Swarm Optimization) was proposed. Firstly, under the premise of global convergence, the population which lacked of diversity used fixed-length ring topology to replace the fully connected one. Secondly, the roulette wheel method was introduced to select the candidate solutions and update the location information and velocity information. Lastly, for controlling and directing the particles to escape from local minimum, the conditions of tabu search algorithm were introduced too. The result of experiment shows that HPSO has a better performance than the Basic Particle Swarm Optimization (BPSO) in population diversity. And HPSO exhibited superiority in search success rate and path coverage in contract with combination method of Genetic Algorithm and Particle Swarm Optimization algorithm named GA-PSO in test data generation, while the average time-consuming is not much different from BPSO.