摘要： 基于服务质量的语义Web服务发现方法存在未成熟收敛以及Pareto集分布不均等问题。为此，在引入基于阈值的粒子比较准则、拥挤距离函数和合力变异算子的基础上，给出一种基于混合粒子群优化算法的语义Web服务发现方法。实验结果表明，该方法具有较高的匹配度和查准率。

关键词: Web服务, 服务质量, 粒子群优化算法, 拥挤距离, 变异算子

Abstract: Semantic Web service discovery based on Quality of Service(QoS) exists immature convergence and Pareto set uneven distribution. Aiming at this problem that this paper gives a semantic Web service discovery based on hybrid Particle Swarm Optimization(PSO) algorithm based on introduces threshold-based particle compare standards, crowded distance functions and backface mutation operator. Experimental results show that this method has higher matching degree and precision.

Key words: Web service, Quality of Service(QoS), Particle Swarm Optimization(PSO) algorithm, crowding distance, mutation operator

中图分类号: 

• TP301