融合可信性评价的Web服务组合QoS优化

针对复杂网络环境下存在的恶意欺诈行为,提出一种将服务可信性评价和基于QoS感知的服务组合优化相结合的方法框架.首先,基于Web服务的历史行为分别采用贝叶斯学习理论和用户评价方法,从客、主观两方面对Web服务可信性进行评估;然后,利用可信性度量后的QoS属性构建多目标优化模型,并提出一种改进的多目标灰狼优化(IMOGWO)算法用于模型求解;最后,通过实验数据验证所提出方法框架在服务组合优化时的有效性.

QoS optimization of Web services composition incorporating with credibility evaluation

Aiming at malicious fraud in the complicated network environment, a method framework combining service credibility evaluation and QoS-aware service composition optimization is proposed. Firstly, based on the historical behavior of Web services, Bayesian learning theory and evaluation information of historical users are used to evaluate the credibility of Web services from both objective and subjective aspects. Then, by using the service QoS attributes measured by credibility, a multi-objective optimization model is constructed, and an improved multi-objective grey wolf optimization(IMOGWO) algorithm is proposed for model solution. Finally, the effectiveness of the method framework for service composition optimization is verified by experimental data.