A many-objective memetic algorithm for correlation-aware service composition in cloud manufacturing

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of servic...

[1]  Zili Zhang,et al.  QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups , 2017 .

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[4]  J. Leon Zhao,et al.  Service Selection for Composition with QoS Correlations , 2016, IEEE Transactions on Services Computing.

[5]  Mihai Alexandru Suciu,et al.  Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition , 2016, Appl. Soft Comput..

[6]  Hamed Bouzary,et al.  Service optimal selection and composition in cloud manufacturing: a comprehensive survey , 2018 .

[7]  Lei Ren,et al.  Real-Time Scheduling of Cloud Manufacturing Services Based on Dynamic Data-Driven Simulation , 2019, IEEE Transactions on Industrial Informatics.

[8]  Wang Yan,et al.  Extraction of volatile oil from Ocimum basilicum L. by supercritical CO2 and GC-MS analysis of the extract. , 2011 .

[9]  Qingsheng Zhu,et al.  A correlation-driven optimal service selection approach for virtual enterprise establishment , 2014, J. Intell. Manuf..

[10]  Carlos A. Coello Coello,et al.  HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[11]  Yong Tao,et al.  Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition , 2018, Knowl. Based Syst..

[12]  Hisao Ishibuchi,et al.  Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Space , 2010, PPSN.

[13]  Shuai Zhang,et al.  Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm , 2019, Appl. Soft Comput..

[14]  Shuai Zhang,et al.  A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm , 2016 .

[15]  Wei Tan,et al.  NCSR: Negative-Connection-Aware Service Recommendation for Large Sparse Service Network , 2016, IEEE Transactions on Automation Science and Engineering.

[16]  Yushun Fan,et al.  Business correlation-aware modelling and services selection in business service ecosystem , 2013, Int. J. Comput. Integr. Manuf..

[17]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[18]  Feng Li,et al.  A clustering network-based approach to service composition in cloud manufacturing , 2017, Int. J. Comput. Integr. Manuf..

[19]  Wei Liu,et al.  A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility , 2018, Int. J. Prod. Res..

[20]  Peng Qiu,et al.  Multi-objective service composition model based on cost-effective optimization , 2018, Applied Intelligence.

[21]  Lin Zhang,et al.  Agent-based simulation platform for cloud manufacturing , 2017, Int. J. Model. Simul. Sci. Comput..

[22]  D. Williamson,et al.  The box plot: a simple visual method to interpret data. , 1989, Annals of internal medicine.

[23]  Amit P. Sheth,et al.  Modeling Quality of Service for Workflows and Web Service Processes , 2002 .

[24]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[25]  Dandan Wu,et al.  Green Energy Management of the Energy Internet Based on Service Composition Quality , 2018, IEEE Access.

[26]  Fei Tao,et al.  Correlation-aware resource service composition and optimal-selection in manufacturing grid , 2010, Eur. J. Oper. Res..

[27]  Jianfeng Ma,et al.  Trust-based service composition in multi-domain environments under time constraint , 2014, Science China Information Sciences.

[28]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[29]  Xu Ji,et al.  Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing , 2018, The International Journal of Advanced Manufacturing Technology.

[30]  Xifan Yao,et al.  Correlation-aware QoS modeling and manufacturing cloud service composition , 2017, J. Intell. Manuf..

[31]  Carlos A. Brizuela,et al.  An overview on evolutionary algorithms for many‐objective optimization problems , 2018, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[32]  Yaghoub Farjami,et al.  An ensemble optimisation approach to service composition in cloud manufacturing , 2019, Int. J. Comput. Integr. Manuf..

[33]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[34]  D. Baird,et al.  Aspects of Sesarma catenata (Grapsidae, Crustacea) burrows and its implications in the event of an oil spill , 1988 .

[35]  Yang Yang,et al.  A genetic-based approach to web service composition in geo-distributed cloud environment , 2015, Comput. Electr. Eng..

[36]  Fei Tao,et al.  Correlation-aware web services composition and QoS computation model in virtual enterprise , 2010 .

[37]  Yang Cheng,et al.  Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach , 2020, Int. J. Prod. Res..

[38]  Xifan Yao,et al.  A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition , 2017, Int. J. Prod. Res..

[39]  Xifan Yao,et al.  An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing , 2018, Inf. Sci..

[40]  Tao Zhang,et al.  Evolutionary Many-Objective Optimization: A Comparative Study of the State-of-the-Art , 2018, IEEE Access.

[41]  Shengxiang Yang,et al.  A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[42]  Fei Tao,et al.  A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system , 2014, Enterp. Inf. Syst..

[43]  Minjie Zhang,et al.  Multi-Objective Service Composition in Uncertain Environments , 2015 .

[44]  Shengxiang Yang,et al.  A Grid-Based Evolutionary Algorithm for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[45]  Ananthram Swami,et al.  Trust-Based Service Composition and Binding with Multiple Objective Optimization in Service-Oriented Mobile Ad Hoc Networks , 2017, IEEE Transactions on Services Computing.

[46]  Fei Tao,et al.  Manufacturing grid resource and resource service digital description , 2009 .

[47]  Liu Jian,et al.  An approach for service composition optimisation considering service correlation via a parallel max–min ant system based on the case library , 2018, Int. J. Comput. Integr. Manuf..

[48]  Lei Ren,et al.  Cloud manufacturing: a new manufacturing paradigm , 2014, Enterp. Inf. Syst..

[49]  Xifan Yao,et al.  Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing , 2017 .

[50]  Shuai Zhang,et al.  Correlation-aware manufacturing service composition model using an extended flower pollination algorithm , 2018, Int. J. Prod. Res..