基于粒子群算法的Web服务组合研究

针对现有服务组合中QoS优化的不足，该文提出一种基于粒子群算法的解决QoS动态服务组合算法。通过对服务组合的业务逻辑与服务实例进行合理编码，重新定义粒子的位置、速度与“加”运算，利用粒子群算法的智能优化原理以及局部与全局优化信息加快粒子群的搜索速度，使其能够快速地得到一组满足约束条件的Pareto优化的服务组合。实验结果证明了算法的可行性和有效性。

Research on Web Services Composition Based on Particle Swarm Optimization

This paper presents an improved algorithm based on particle swarm, which is to resolve dynamic Web Services selection with QoS optimal in Web Services composition. The essence of the algorithm is that the problem of dynamic Web Service selection with QoS optimal is transformed into a multi-objective services composition optimization with QoS constraints. The theory of intelligent optimization of particle swarm optimization algorithm is utilized to produce a set of optimal Pareto services composition process with constraint principle by accelerating global and detail searching speed based on deciding PSO state. Experimental results indicate the feasibility and efficiency of this algorithm.