[1]张志鹏,周井泉.基于改良蜂群算法的 Web 服务组合优化方法[J].计算机技术与发展,2024,34(03):64-69.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 010]
　ZHANG Zhi-peng,ZHOU Jing-quan.Web Service Composition Optimization Method Based on Modified Artificial Bee Colony[J].,2024,34(03):64-69.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 010]

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基于改良蜂群算法的 Web 服务组合优化方法()

分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

34

期数:

2024年03期

页码:

64-69

栏目:

软件技术与工程

出版日期:

2024-03-10

文章信息/Info

Title:

Web Service Composition Optimization Method Based on Modified Artificial Bee Colony

文章编号:

1673-629X(2024)03-0064-06

作者:

张志鹏; 周井泉

南京邮电大学 电子与光学工程学院、柔性电子(未来技术)学院,江苏 南京 210003

Author(s):

ZHANG Zhi-peng; ZHOU Jing-quan

School of Electronics and Optical Engineering,School of Flexible Electronics ( Future Technology) ,Nanjing University of Posts and Telecommunications,Nanjing 210003,China

关键词:

云计算; Web 服务组合; 蜂群算法; QoS 属性; 混沌映射

Keywords:

cloud computing; Web service composition; bee colony algorithm; QoS attribute; chaos mapping

分类号:

TP301

DOI:

10. 3969 / j. issn. 1673-629X. 2024. 03. 010

摘要:

为提高 Web 组合优化算法的开发能力和运行结果的适应度、稳定性,以满足用户对 Web 服务的服务质量( Qualityof Service,QoS) 需求,提出了一种基于 QoS 模型和改良蜂群算

法( modified Artificial Bee Colony,mABC) 的 Web 服务组合优化方法。 构建应用于 Web 服务组合优化问题的 QoS 顺序数学模型,使用基于混沌的对立学习方法,在进程运行的初

始化阶段生成更好的初始群体,在蜂群算法的雇佣蜂阶段和围观蜂阶段使用新的相位搜索方程和围观搜索策略,有效地提高蜂群算法的 探 测 能 力 和 开 发 能 力。 将改良蜂群算法

与 人 工蜂群算法 ( Artificial Bee Colony, ABC )、 差 分 进 化 算 法(Differential Evolution,DE) 、改进灰狼优化算法( Modified Grey Wolf Optimizer,MGWO) 、最优导向人工蜂群算

法( Guided -best Artificial Bee Colony,GABC) 和改进人工蜂群算法(Improved Artificial Bee Colony, IABC) 进行了多次对比实验。 实验结果表明,改良蜂群算法尽管在执行时间方面比其余算法都要略微长一些,但它在更为重要的适应度、稳定性方面都优于其余几种对比算法。

Abstract:

In order to improve the development capability of Web Combinatorial optimization algorithm and the adaptability and stabilityof the running results, and meet the user ’ s demand for quality of service ( QoS ) of Web services, we propose a combinatorialoptimization method of Web services based on QoS model and modified Artificial Bee Colony ( mABC ) . The QoS sequentialmathematical model applied to the combinatorial optimization problem of Web services is constructed. The chaos based opposite learningmethod is used to generate a better initial population in the initialization phase of the process operation. New phase search equations andspectator search strategies are used in the hire bee phase and spectator bee phase of the bee colony algorithm to effectively improve the detection and development capabilities of the bee colony algorithm, modified Artificial Bee Colony, Artificial Bee Colony ( ABC ) ,Differential Evolution ( DE ) , Modified Grey Wolf Optimizer ( MGWO ) , Guided - best Artificial Bee Colony ( GABC ) , ImprovedArtificial Bee Colony

?( IABC) algorithms are compared and tested multiple times. The experiments show that although the modifiedABC has slightly longer execution time than that of other algorithms, it outperforms other comparative algorithms in more importantaspects of fitness and stability.

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