电信科学 ›› 2011, Vol. 27 ›› Issue (12): 67-71.doi: 10.3969/j.issn.1000-0801.2011.12.020

• 研究与开发 • 上一篇    下一篇

基于差异演化算法的QoS全局最优动态Web服务选择

康国胜,刘建勋,唐明董,徐宇   

  1. 湖南科技大学知识处理与网络化制造湖南省普通高校重点实验室 湘潭 411201
    Key Laboratory of Knowledge Processing and Networked Manufacture, Hunan University of Science and Technology, Xiangtan 411201, China
    湖南科技大学知识处理与网络化制造湖南省普通高校重点实验室 湘潭 411201
  • 出版日期:2011-12-15 发布日期:2011-12-15
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;教育部新世纪人才基金资助项目;湖南省杰出青年基金资助项目;湖南省教育厅基金资助项目;湖南省教育厅一般基金资助项目

Dynamic Web Service Selection Algorithm with Global Optimal QoS Based on Differential Evolution

Guosheng Kang,Jianxun Liu,Mingdong Tang,Yu Xu   

  1. Key Laboratory of Knowledge Processing and Networked Manufacture, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
    Key Laboratory of Knowledge Processing and Networked Manufacture, Hunan University of Science and Technology, Xiangtan 411201, China
  • Online:2011-12-15 Published:2011-12-15

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摘要：

QoS 全局最优动态Web 服务选择是服务组合中的一个难题。基于差异演化算法，设计一种用于解决该问题的 DE-GODSS 算法。算法的主要思想是将问题表示为一个带 QoS 约束的多目标服务组合优化问题，通过理想点的方法将多目标向单目标转化，然后利用差异演化算法的智能优化原理进行算法设计及求解，最终产生一组满足约束条件的优化服务组合流程集。理论分析证明DE-GODSS 算法的时间复杂度优于已有的多目标遗传算法，且实验结果表明该算法的收敛速度优于已有的多目标遗传算法。

关键词: 服务选择, QoS全局优化, 多目标优化, 差异演化

Abstract:

Dynamic Web service selection with global QoS optimization is a critical issue in Web service composition. In order to solve the problem, based on the algorithm of differential evolution（DE）, this paper proposes the DE-GODSS（global optimal of dynamic Web service selection based on DE）algorithm. The basic idea of the algorithm is to transform the original Web service selection problem into a multi-objective service composition optimization with global QoS constraints, which is further transformed into a single-object by using the method of ideal point. Then, the theory of intelligent optimization of DE is exploited to produce a set of optimal services composition process with QoS constraints. Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm, and the time complexity and convergence rate of our algorithm are much better than that of the multi-objective genetic algorithm used in prior work.

Key words: service selection, QoS global optimal, multi-objective optimization, defferential evolution

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