QoS optimization of Web services composition incorporating with credibility evaluation

被引：0
|
作者：
Han M. [1 ]
Duan Y.-Z. [1 ]
机构：
[1] Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 08期
关键词：
Control parameter; Credibility evaluation; Multi-objective grey wolf optimizer; QoS; Services composition; Web services;
D O I：
10.13195/j.kzyjc.2019.0006
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1859 / 1865
页数：6
相关论文
共 22 条
  • [1] Ding Z J, Zhou Z X, Review of web service composition testing, Journal of Software, 2, pp. 299-319, (2018)
  • [2] Wu Y, Yan C, Ding Z, A multilevel index model to expedite web service discovery and composition in large-scale service repositories, IEEE Transactions on Services Computing, 9, 3, pp. 330-342, (2016)
  • [3] Rodriguez-Mier P, Pedrinaci C, Lama M, An integrated semantic web service discovery and composition framework, IEEE Transactions on Services Computing, 9, 4, pp. 537-550, (2016)
  • [4] Zeng L, Benatallah B, Ngu A H H, QoS-aware middleware for web services composition, IEEE Transactions on Software Engineering, 30, 5, pp. 311-327, (2004)
  • [5] Gao H C, Feng B Q, Zhu L, Intelligent optimization algorithm for solving TSP problem, Control and Decision, 21, 3, pp. 241-247, (2006)
  • [6] Chang H H, Feng Z R, Zhang Z J, An intelligent optimization algorithm for solving quality evaluation methods, Control and Decision, 28, 11, pp. 1735-1740, (2013)
  • [7] He Q, Wu Y L, Xu T W, Application of improved genetic simulated annealing algorithm in TSP optimization, Control and Decision, 33, 2, pp. 219-225, (2018)
  • [8] Han M, Zhang L J, Bi-group multi-objective particle swarm optimization algorithm based on diversity metric, Control and Decision, 32, 12, pp. 2268-2272, (2017)
  • [9] Ye H Z, Guan Y H, QoS-aware web service composition based on local selection and genetic algorithm, Journal of Chinese Computer Systems, 37, 7, pp. 1389-1392, (2016)
  • [10] Ma W L, Wang Z, Zhao Y W, Manufacturing cloud service combination optimization based on improved ant colony algorithm, Computer Integrated Manufacturing Systems, 22, 1, pp. 113-121, (2016)
123