{"title":"A two-phase method to optimize service composition in cloud manufacturing","authors":"Qiang Hu, Haoquan Qi, Yanzhe Jia, Lianen Qu","doi":"10.1007/s00607-024-01286-x","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":10718,"journal":{"name":"Computing","volume":"100 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00607-024-01286-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
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.
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.