A two-phase method to optimize service composition in cloud manufacturing

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Computing Pub Date : 2024-04-15 DOI:10.1007/s00607-024-01286-x
Qiang Hu, Haoquan Qi, Yanzhe Jia, Lianen Qu
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Abstract

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability.

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优化云制造中服务组成的两阶段方法
服务组合广泛应用于云制造领域。由于存在大量类似的云制造服务,优化服务组合的搜索空间往往十分广阔。现有的优化模型主要关注 QoS(服务质量),而往往忽视 QoC(协作质量)。此外,服务组合优化的质量和稳定性仍有待提高。因此,本文提出了一种分两个阶段优化云制造中服务组成的方法。在第一阶段,我们引入了面向服务集群的服务响应框架,有效地生成候选响应服务集,以减少解决方案的搜索空间。在第二阶段,我们构建了一个整合了 QoS 和 QoC 的优化模型。随后,我们设计了一种结合多搜索策略岛模型的人工蜂群(ABC)算法,以优化云制造服务的组成。实验结果表明,服务集群的引入提高了搜索效率,所提出的方法在优化质量和稳定性方面优于 ABC 算法和其他蜂群智能算法。
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来源期刊
Computing
Computing 工程技术-计算机:理论方法
CiteScore
8.20
自引率
2.70%
发文量
107
审稿时长
3 months
期刊介绍: Computing publishes original papers, short communications and surveys on all fields of computing. The contributions should be written in English and may be of theoretical or applied nature, the essential criteria are computational relevance and systematic foundation of results.
期刊最新文献
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