Web Service Composition Optimization Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

被引：0
|
作者：
Song H. [1 ,2 ]
Wang Y.-L. [1 ]
Liu G.-Q. [1 ]
Zhang B. [2 ]
机构：
[1] School of Software, Northeastern University, Shenyang
[2] School of Computer Science and Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2019年 / 40卷 / 06期
关键词：
Artificial bee colony; Multi-objective optimization; Optimization of service composition; Web services;
D O I：
10.12068/j.issn.1005-3026.2019.06.004
中图分类号：
学科分类号：
摘要：
To solve the problem of combinatorial diversity and service quality in Web service composition optimization methods, an improvement in artificial bee colony algorithm was proposed. Several methods such as reverse learning operator, elite guidance strategy, and combination mutation strategy were led into the algorithm, by which targeted information could be provided to update individuals. Furthermore, the diversity of service portfolios was enhanced on the premise of ensuring the quality of service portfolios. The experimental results indicated that the refined algorithm has fast convergence speed and good uniformity. Meanwhile, a better optimistic effect was also received for the optimization of Web service composition, and the search accuracy, solution quality and convergence speed were improved as well. © 2019, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：777 / 782
页数：5
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