| 基于自适应粒子群算法的制造云服务组合研究 |
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| 引用本文： | 刘卫宁,李一鸣,刘波.基于自适应粒子群算法的制造云服务组合研究[J].计算机应用,2012,32(10):2869-2874. |
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| 作者姓名： | 刘卫宁 李一鸣 刘波 |
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| 作者单位： | 1. 重庆大学 计算机学院,重庆 4000302. 重庆大学 信息物理社会可信服务计算教育部重点实验室,重庆 400030 |
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| 基金项目： | 重庆市科技攻关计划项目(2008AB2044);上海市科委资助项目(09DZ15024000) |
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| 摘 要： | 针对云制造系统中制造云服务组合的多目标规划问题,研究建立了问题模型并提出了求解方法。首先引入了网格制造模式的制造资源服务组合技术,探讨并描述了云制造模式中基于服务质量（QoS）的制造云服务组合过程；接着通过分析云制造模式下制造云服务的特征并基于制造领域知识,研究定义了制造云服务的八维QoS评估标准及计算表达式,推导出制造组合云服务的QoS表达,进而建立了制造云服务组合的多目标规划问题模型。最终设计了自适应粒子群算法来解决该多目标规划问题。仿真实验表明,该算法能有效并高效地解决该问题,且求解效率优于传统粒子群算法。
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| 关 键 词： | 云制造 多目标规划 服务组合 自适应粒子群算法 服务质量 |
| 收稿时间： | 2012-04-01 |
| 修稿时间： | 2012-05-22 |
Service composition in cloud manufacturing based on adaptive mutation particle swarm optimization |
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| LIU Wei-ning,LI Yi-ming,LIU Bo.Service composition in cloud manufacturing based on adaptive mutation particle swarm optimization[J].journal of Computer Applications,2012,32(10):2869-2874. |
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| Authors: | LIU Wei-ning LI Yi-ming LIU Bo |
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| Affiliation: | 1. College of Computer Science, Chongqing University, Chongqing 400030, China2. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education,Chongqing University,
Chongqing 400030, China3. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education,Chongqing University, Chongqing 400030, China |
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| Abstract: | To cope with Multi-objective Programming on Manufacturing Cloud Service Composition(MOP-MCSC) problem in cloud manufacturing(CMfg) system,a mathematical model and a solution algorithm were proposed and studied.Firstly,inspired by the resource service composition technology in manufacturing grid,a QoS-aware MOP-MCSC model in CMfg system had been explored and described.Secondly,by analyzing the characteristics of manufacturing cloud services according to the domain knowledge of manufacturing,an eight-dimensional QoS evaluation criterion with corresponding quantitative calculation formulas was defined.Then,the QoS expression of manufacturing cloud service was eventually formulated.Lastly,the MOP-MCSC model was built,and an Adaptive Mutation Particle Swarm Optimization(AMPSO) was designed to realize this model.The simulation experimental results suggest that the proposed algorithm could solve the MOP-MCSC problem efficiently and effectively with a better performance than conventional particle swarm optimization. |
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| Keywords: | cloud manufacturing multi-objective programming service composition Adaptive Mutation Particle Swarm Optimization(AMPSO) Quality of Service(QoS) |
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