基于改进蚁群优化算法的QoS区间数服务组合方法

QoS Interval Number Service Composition Method Based on Improved Ant Colony Optimization Algorithm

已有的Qo S服务组合方法由于无法准确量化区间型Qo S属性,且存在忽视Qo S属性中的数据分布特征和用户Qo S需求表达不准确的问题,导致其组合结果与用户理想结果存在较大偏差。为此,基于改进的蚁群优化算法,提出一种Qo S属性区间数的服务组合方法。从服务本身和用户体验两方面出发,应用区间数形式的用户满意度和Qo S效用函数构造服务组合的目标函数,并通过改进的蚁群信息素更新策略和参数选择策略加快蚁群收敛速度,在满足用户全局Qo S约束的基础上,找出用户满意度高、整体性能好的组合服务。实验结果表明,该方法能够有效提高服务组合的效率和成功率。

The existing Quality of Service （QoS） service composition methods fail to measure the interval QoS attributes, and overlooks the data distribution features in the QoS attributes and the description uncertainty for QoS demand, thus resulting in a big difference between the real composition results and the ideal ones. Therefore, a service composition method of QoS attribute interval number is proposed based on the improved Ant Colony Optimization （ACO） algorithm. User satisfaction and QoS utility function in interval numbers are used to construct the objective function of service composition. By using the improved pheromone updating and parameter selection strategies, the convergence speed is increased and the best service composition with high user satisfaction and service performance is found on the basis of satisfying the global QoS constraint. Experimental results show that the method can improve the efficiency and success rate of service compositon.