融合粒子群算法与蚁群算法对XML概率查询策略的改进

PSO(Particle Swarm Optimization) has the ability of doing a global searching quickly and stochastically, but it can’t make use of the feedback information; ACO (Ant Colony Optimization)converges on the best path through pheromone accumulation and renewal. It has the ability of parallel processing and global searching, but there is little pheromone on the path early and the speed to solution is slow. This paper combines PSO with ACO to improve XML probabilistic query, which adopts PSO to make pheromone distribution and make use of ASO to get a value accurately, as a result it develops enough advantages of the two algorithms, and the data query range is widen and convergence efficiency is increased. Through Simulation experiments, it shows a preferable result to XML query.