云制造环境下的知识服务组合优化策略
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摘要
针对云制造环境下知识服务组合优化问题,通过分析服务组合优化过程,采用服务质量感知的服务组合策略建立了以时间、成本、可用性、准确性、创新性、可信性为优化目标的服务组合优化模型;采用聚类分析及关联规则挖掘策略对搜索空间进行预处理,减小了搜索空间,实现了知识服务资源的快速精准定位与匹配,提高了知识服务组合的效率和成功率;针对标准涡流搜索算法易陷入局部最小的问题,引入多涡流中心搜索及涡流中心自适应更新策略,提出一种改进的多中心涡流搜索算法对服务组合问题进行全局优化。仿真实验表明,聚类分析及关联规则挖掘策略与多中心涡流搜索算法结合,能极大地缩短寻优时间并获得更优解,从而更有效地解决知识服务组合优化问题。
Aiming at the optimization problem of knowledge service composition under cloud manufacturing,a service composition and optimization model with time,cost,availability,accuracy,innovation and credibility as the optimization objective was established with the the strategy of QoS-aware service composition by analyzing the process of service composition and optimization.The search space was pre-processed by using clustering analyse and association rule mining strategy,which contributed to reducing the search space and achieving identifying and matching of knowledge service resources quickly and accurately,and improving the efficiency and success rate of knowledge service composition.Aiming at the problem that standard vortex search algorithm is easy to fall into local minimum,an improved mutiple centre vortex search algorithm was proposed for global optimization of service composition problem.Simulation results showed that the combination of clustering analyse and association rule mining strategy with multi center vortex search algorithm could greatly shorten the search time and get better solution,and can solve the optimization problem of knowledge service composition more effectively.
Aiming at the optimization problem of knowledge service composition under cloud manufacturing,a service composition and optimization model with time,cost,availability,accuracy,innovation and credibility as the optimization objective was established with the the strategy of QoS-aware service composition by analyzing the process of service composition and optimization.The search space was pre-processed by using clustering analyse and association rule mining strategy,which contributed to reducing the search space and achieving identifying and matching of knowledge service resources quickly and accurately,and improving the efficiency and success rate of knowledge service composition.Aiming at the problem that standard vortex search algorithm is easy to fall into local minimum,an improved mutiple centre vortex search algorithm was proposed for global optimization of service composition problem.Simulation results showed that the combination of clustering analyse and association rule mining strategy with multi center vortex search algorithm could greatly shorten the search time and get better solution,and can solve the optimization problem of knowledge service composition more effectively.
引文
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[19]YIN Chao,ZHANG Yun,ZHONG Ting.Optimization model of cloud manufacturing services resource combination for new product development[J].Computer Integrated Manufacturing Systems,2012,18(7):1368-1378(in Chinese).[尹超,张云,钟婷.面向新产品开发的云制造服务资源组合优选模型[J].计算机集成制造系统,2012,18(7):1368-1378.]
[20]ZHOU Jiajun,YAO Xifan.A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition[J].International Journal of Advanced Manufacturing Technology,2017,88(9/10/11/12):3371-3387.
[21]ZHANG Zhiyong,ZHANG Xinhui,LIU Jie.Association rule mining based on apriori algorithm in distribution center storage planning[J].Logistics Engineering and Management,2012,34(4):56-59(in Chinese).[张智勇,张新辉,刘杰.基于Apriori算法的关联规则挖掘在配送中心储位规划中的应用[J].物流工程与管理,2012,34(4):56-59.]
[22]LMEZ B,DOAN T.A new metaheuristic for numerical function optimization:vortex search algorithm[J].Information Sciences,2015,293:125-145.
[23]LI Xuegui,XU Shaohua,LI Na,et al.Classification model of support vector machine based on vortex search algorithm[J].Control and Instruments in Chemical Industry,2016,43(12):1291-1295(in Chinese).[李学贵,许少华,李娜,等.基于涡流搜索算法的支持向量机分类模型[J].化工自动化及仪表,2016,43(12):1291-1295.]
[24]MA Wenlong,WANG Zheng,ZHAO Yanwei.Optimizing services composition in cloud manufacturing based on improved ant colony algorithm[J].Computer Integrated Manufacturing Systems,2016,22(1):113-121(in Chinese).[马文龙,王铮,赵燕伟.基于改进蚁群算法的制造云服务组合优化[J].计算机集成制造系统,2016,22(1):113-121.]
[2]TU Jianwei,LI Yan,LI Wenqiang,et al.Knowledge retrieval model and implementation for product innovative design[J].Computer Integrated Manufacturing Systems,2013,19(2):300-308(in Chinese).[涂建伟,李彦,李文强,等.一种面向产品创新设计的知识检索模型与实现[J].计算机集成制造系统,2013,19(2):300-308.]
[3]GUO Xin,ZHAO Wu,WANG Jie.A study of knowledge modeling and retrieval methods oriented towards innovative design of manufacturing planning[J].Journal of Mechanical Engineering,2017,53(15):66-72(in Chinese).[郭鑫,赵武,王杰.面向创新设计的工艺设计知识模型及检索方法研究[J].机械工程学报,2017,53(15):66-72.]
[4]LI Yingxin,JING Shikai,LI Xiangqian,et al.Personalized knowledge service approach for cloud manufacturing based on user behaviors[J].Computer Integrated Manufacturing Systems,2015,21(3):848-858(in Chinese).[李颖新,敬石开,李向前,等.云制造环境下基于用户行为感知的个性化知识服务技术[J].计算机集成制造系统,2015,21(3):848-858.]
[5]ZHAO Nan,NIU Zhanwen,GUO Wei.Quantum harmony search method for design knowledge resource serialization combination in cloud manufacturing environment[J].Computer Integrated Manufacturing Systems,2012,18(7):1435-1443(in Chinese).[赵楠,牛占文,郭伟.云制造环境中面向设计知识资源序列化组合的量子和声搜索算法[J].计算机集成制造系统,2012,18(7):1435-1443.]
[6]CHAFLE G B,CHANDRA S,MANN V,et al.Decentralized orchestration of composite web services[C]//Proceedings of the 13th International World Wide Web Conference On Alternate Track Papers&Posters.New York,N.Y.,USA:ACM,2004:134-143.
[7]DENG Shuiguang,WU Zhaohui,KUANG Li,et al.Management of serviceflow in a flexible way[C]//Proceedings of the5th International Conference on Web Information Systems Engineering.Berlin,Germany:Springer-Verlag,2004:428-438.
[8]MAO Z M,KATZ R H,BREWER A,et al.Fault-tolerant,scalable,wide-area Internet service composition[R].Berkeley,Cal.,USA:University of California at Berkeley,2001.
[9]ZHANG Ruoyan,ARPINAR I B,ALEMAN M B.Automatic composition of semantic web services[C]//Proceedings of IEEE International Conference on Web Services.Washington,D.C.,USA:IEEE,2007:150-158.
[10]ZENG Liangzhao,BENATALLAH B,NGU A H H,et al.QoS-aware middleware for web services composition[J].IEEE Transactions on Software Engineering,2004,30(5):311-327.
[11]LIU Huan,ZHONG Farong,OUYANG Bang,et al.An approach for QOS-aware web service composition based on improved genetic algorithm[C]//Proceedings of the 2010International Conference on Web Information Systems and MiningVolume 01.Washington,D.C.,USA:IEEE,2010:123-128.
[12]MAAMAR Z,MOSTFAOUI S K,YAHYAOUI H.Toward an agent-based and context-oriented approach for Web services composition[J].IEEE Transactions on Knowledge&Data Engineering,2005,17(5):686-697.
[13]GUO Hua,TAO Fei,ZHANG Lin,et al.Correlation-aware web services composition and QoS computation model in virtual enterprise[J].International Journal of Advanced Manufacturing Technology,2010,51(5/6/7/8):817-828.
[14]XIANG Feng,HU Yefa,YU Yingrong,et al.QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system[J].Central European Journal of Operations Research,2014,22(4):663-685.
[15]XU Xiaofei.Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved artificial bee colony optimisation algorithm[J].International Journal of Production Research,2015,53(14):4380-4404.
[16]LIU Bo,ZHANG Zili.QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups[J].International Journal of Advanced Manufacturing Technology,2017,88(9/10/11/12):2757-2771.
[17]TAO Fei,ZHAO Dongming,HU Yefa,et al.Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system[J].IEEETransactions on Industrial Informatics,2008,4(4):315-327.
[18]MA Wenlong,ZHU Linan,WANG Wanliang.Cloud service selection model based on QoS-aware in cloud manufacturing environment[J].Computer Integrated Manufacturing Systems,2014,20(5):1246-1254(in Chinese).[马文龙,朱李楠,王万良.云制造环境下基于QoS感知的云服务选择模型[J].计算机集成制造系统,2014,20(5):1246-1254.]
[19]YIN Chao,ZHANG Yun,ZHONG Ting.Optimization model of cloud manufacturing services resource combination for new product development[J].Computer Integrated Manufacturing Systems,2012,18(7):1368-1378(in Chinese).[尹超,张云,钟婷.面向新产品开发的云制造服务资源组合优选模型[J].计算机集成制造系统,2012,18(7):1368-1378.]
[20]ZHOU Jiajun,YAO Xifan.A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition[J].International Journal of Advanced Manufacturing Technology,2017,88(9/10/11/12):3371-3387.
[21]ZHANG Zhiyong,ZHANG Xinhui,LIU Jie.Association rule mining based on apriori algorithm in distribution center storage planning[J].Logistics Engineering and Management,2012,34(4):56-59(in Chinese).[张智勇,张新辉,刘杰.基于Apriori算法的关联规则挖掘在配送中心储位规划中的应用[J].物流工程与管理,2012,34(4):56-59.]
[22]LMEZ B,DOAN T.A new metaheuristic for numerical function optimization:vortex search algorithm[J].Information Sciences,2015,293:125-145.
[23]LI Xuegui,XU Shaohua,LI Na,et al.Classification model of support vector machine based on vortex search algorithm[J].Control and Instruments in Chemical Industry,2016,43(12):1291-1295(in Chinese).[李学贵,许少华,李娜,等.基于涡流搜索算法的支持向量机分类模型[J].化工自动化及仪表,2016,43(12):1291-1295.]
[24]MA Wenlong,WANG Zheng,ZHAO Yanwei.Optimizing services composition in cloud manufacturing based on improved ant colony algorithm[J].Computer Integrated Manufacturing Systems,2016,22(1):113-121(in Chinese).[马文龙,王铮,赵燕伟.基于改进蚁群算法的制造云服务组合优化[J].计算机集成制造系统,2016,22(1):113-121.]