东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (6): 777-782.DOI: 10.12068/j.issn.1005-3026.2019.06.004

• 信息与控制 • 上一篇    下一篇

基于改进多目标蜂群算法的Web服务组合优化方法

宋航1,2， 王亚丽1， 刘国奇1， 张斌2   

  1. (1. 东北大学 软件学院， 辽宁 沈阳110169; 2. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169)
  • 收稿日期:2018-05-11 修回日期:2018-05-11 出版日期:2019-06-15 发布日期:2019-06-14
  • 通讯作者: 宋航
  • 作者简介:宋航(1977-)，男，辽宁沈阳人，东北大学讲师，博士研究生; 张斌(1964-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61402092，61603082).

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

SONG Hang1,2， WANG Ya-li1， LIU Guo-qi1， ZHANG Bin2   

  1. 1. School of Software， Northeastern University， Shenyang 110169， China; School of Computer Science and Engineering， Northeastern University， Shenyang 110169， China.
  • Received:2018-05-11 Revised:2018-05-11 Online:2019-06-15 Published:2019-06-14
  • Contact: ZHANG Bin
  • About author:-
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摘要： 为解决Web服务组合优化方法中的组合多样性和服务质量的问题，在人工蜂群算法上提出改进，通过在算法中引入反向学习算子、精英引导策略和组合变异策略等操作，使得种群个体有针对性地进行更新，在保证服务组合质量的前提下，提高了服务组合的多样性.结果表明，所提算法具有良好的算法收敛性和均匀性，同时在为Web服务组合优化方面，也取得了较好的优化效果，提高了寻优精度、解的质量和收敛速度.

关键词: Web服务, 服务组合优化, 人工蜂群, 多目标优化

Abstract: 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.

Key words: web services, optimization of service composition, artificial bee colony, multi-objective optimization

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