Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing

Process of manufacturing service composition.Display Omitted A novel hybrid differential artificial bee colony algorithm for service composition in cloud manufacturing is proposed.Multiple subpopulations with distinct hybrid evolutionary operators are adopted during the evolution process.The size of each subpopulation is adaptively adjusted based on the information derived from its search process.The control parameters of each evolution operator are adapted independently.The proposed algorithm outperforms the state-of-the-art approaches known in the literature. As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.

[1]  Lin Lv,et al.  Green partner selection in virtual enterprise based on Pareto genetic algorithms , 2012, The International Journal of Advanced Manufacturing Technology.

[2]  Weiming Shen,et al.  Multi-granularity resource virtualization and sharing strategies in cloud manufacturing , 2014, J. Netw. Comput. Appl..

[3]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

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

[5]  Fuyuki Ishikawa,et al.  A graph-based approach enhancing correctness and speed of web services composition through explicit specification of functional semantics , 2014, Int. J. Web Grid Serv..

[6]  Fei Tao,et al.  A Ranking Chaos Algorithm for dual scheduling of cloud service and computing resource in private cloud , 2013, Comput. Ind..

[7]  Giuseppe M. L. Sarnè,et al.  Cloning mechanisms to improve agent performances , 2013, J. Netw. Comput. Appl..

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

[9]  Zakaria Maamar,et al.  Toward an agent-based and context-oriented approach for Web services composition , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Xifan Yao,et al.  A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition , 2017 .

[11]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[12]  Amin Jula,et al.  Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition , 2015, Expert Syst. Appl..

[13]  Fei Tao,et al.  IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing , 2014, IEEE Transactions on Industrial Informatics.

[14]  Amin Jula,et al.  Cloud computing service composition: A systematic literature review , 2014, Expert Syst. Appl..

[15]  Octavian Morariu,et al.  Shop-floor resource virtualization layer with private cloud support , 2016, J. Intell. Manuf..

[16]  Fei Tao,et al.  FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System , 2013, IEEE Transactions on Industrial Informatics.

[17]  P. N. Suganthan,et al.  Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization , 2015, Appl. Soft Comput..

[18]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[19]  Dazhong Wu,et al.  Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation , 2015, Comput. Aided Des..

[20]  Fei Tao,et al.  A trust evaluation model towards cloud manufacturing , 2016 .

[21]  Lin Zhang,et al.  Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems , 2017 .

[22]  Fateh Seghir,et al.  A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition , 2018, J. Intell. Manuf..

[23]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[24]  Wolfgang Nejdl,et al.  A hybrid approach for efficient Web service composition with end-to-end QoS constraints , 2012, TWEB.

[25]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

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

[27]  George Q. Huang,et al.  IoT-based real-time production logistics synchronization system under smart cloud manufacturing , 2016 .

[28]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[29]  Chi-Guhn Lee,et al.  Manufacturing task semantic modeling and description in cloud manufacturing system , 2014 .

[30]  Fei Tao,et al.  Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .

[31]  Xiaorong Huang,et al.  Service requirement conflict resolution based on ant colony optimization in group-enterprises-oriented cloud manufacturing , 2016 .

[32]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[33]  Athanasios V. Vasilakos,et al.  Web services composition: A decade's overview , 2014, Inf. Sci..

[34]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[35]  Fei Tao,et al.  SDMSim: A manufacturing service supply–demand matching simulator under cloud environment , 2017 .

[36]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[37]  Xiao Xue,et al.  Manufacturing service composition method based on networked collaboration mode , 2016, J. Netw. Comput. Appl..

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

[39]  Quan-Ke Pan,et al.  Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems , 2011 .

[40]  Ying Feng,et al.  CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling , 2014, Appl. Soft Comput..

[41]  Yuan Cheng,et al.  Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S , 2015, The International Journal of Advanced Manufacturing Technology.

[42]  M. Shamim Hossain,et al.  Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[43]  Ali Mansourian,et al.  Automatic composition of WSMO based geospatial semantic web services using artificial intelligence planning , 2013 .

[44]  Robert X. Gao,et al.  Cloud-enabled prognosis for manufacturing , 2015 .

[45]  Erich Schikuta,et al.  A Parallel Branch and Bound Algorithm for Workflow QoS Optimization , 2009, 2009 International Conference on Parallel Processing.

[46]  Shi-Ming Huang,et al.  Enhancing conflict detecting mechanism for Web Services composition: A business process flow model transformation approach , 2008, Inf. Softw. Technol..

[47]  Xun Xu,et al.  Development of a Hybrid Manufacturing Cloud , 2014 .

[48]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[49]  Yu Xue,et al.  Discrete gbest-guided artificial bee colony algorithm for cloud service composition , 2014, Applied Intelligence.

[50]  Ahmed K. Elmagarmid,et al.  Composing Web services on the Semantic Web , 2003, The VLDB Journal.

[51]  Kwang Mong Sim,et al.  Agent-based Cloud service composition , 2012, Applied Intelligence.

[52]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[53]  Andrew Y. C. Nee,et al.  Advanced manufacturing systems: socialization characteristics and trends , 2015, Journal of Intelligent Manufacturing.

[54]  S. Karthikeyan,et al.  A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints , 2014, The International Journal of Advanced Manufacturing Technology.

[55]  Fei Tao,et al.  A study of optimal allocation of computing resources in cloud manufacturing systems , 2012, The International Journal of Advanced Manufacturing Technology.

[56]  Lida Xu,et al.  Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system , 2013 .

[57]  Daniela Zaharie,et al.  Influence of crossover on the behavior of Differential Evolution Algorithms , 2009, Appl. Soft Comput..

[58]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[59]  Xifan Yao,et al.  Emerging manufacturing paradigm shifts for the incoming industrial revolution , 2016 .

[60]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[61]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[62]  Xiao Xue,et al.  Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition , 2016, Inf. Sci..

[63]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[64]  Dimitris Mourtzis,et al.  Cloud-based cyber-physical systems and quality of services , 2016 .