A collaborative service group-based fuzzy QoS-aware manufacturing service composition using an extended flower pollination algorithm

Quality of service (QoS)-aware manufacturing service composition has attracted growing attention from experts and scholars. However, most of them ignore the fuzziness and complexity of QoS values and describe QoS values using precise numbers. In addition, a one-to-one mapping-based service composition method has been widely used making it difficult to obtain an optimal solution with higher QoS values. In this study, we construct a new collaborative service group-based fuzzy QoS-aware (CSGFQ) manufacturing service composition model, which not only expands the traditional one-to-one mapping-based relationship between services and subtasks, but also objectively describes QoS values using fuzzy numbers. An extended flower pollination algorithm (FPA) that embeds four improvements is presented to solve the corresponding model. Four groups of experiments are performed to compare our proposed method with other baseline algorithms to prove the practicality, effectiveness, efficiency, and other performance of the extended FPA in solving the CSGFQ service composition problem.

[1]  Yu-Jie Wang,et al.  A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation , 2015, Appl. Soft Comput..

[2]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[3]  Ronald E. Giachetti,et al.  A parametric representation of fuzzy numbers and their arithmetic operators , 1997, Fuzzy Sets Syst..

[4]  Hak-Keung Lam,et al.  Asynchronous Piecewise Output-Feedback Control for Large-Scale Fuzzy Systems via Distributed Event-Triggering Schemes , 2018, IEEE Transactions on Fuzzy Systems.

[5]  Kai Chen,et al.  A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization , 2018, Neurocomputing.

[6]  Wenbin Wang,et al.  An improved Particle Swarm Optimization Algorithm for QoS-aware Web Service Selection in Service Oriented Communication , 2010, Int. J. Comput. Intell. Syst..

[7]  Leandro dos Santos Coelho,et al.  Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization , 2008, Expert Syst. Appl..

[8]  Jeng-Shyang Pan,et al.  Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization , 2016, ACIIDS.

[9]  Shiji Song,et al.  hybrid differential evolution algorithm for job shop scheduling problems with xpected total tardiness criterion , 2013 .

[10]  Wei Xing Zheng,et al.  Synchronization and State Estimation of a Class of Hierarchical Hybrid Neural Networks With Time-Varying Delays , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Bilal Alatas,et al.  Chaotic bee colony algorithms for global numerical optimization , 2010, Expert Syst. Appl..

[12]  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..

[13]  Guiwu Wei,et al.  Grey relational analysis model for dynamic hybrid multiple attribute decision making , 2011, Knowl. Based Syst..

[14]  Ka Chi Lam,et al.  A material supplier selection model for property developers using Fuzzy Principal Component Analysis , 2010 .

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

[16]  Rosanna Fornasiero,et al.  Supply chain configurations: a model to evaluate performance in customised productions , 2017, Int. J. Prod. Res..

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

[18]  E. S. Ali,et al.  Combined economic and emission dispatch solution using Flower Pollination Algorithm , 2016 .

[19]  Fei Tao,et al.  Resource Service Composition and Its Optimal-Selection Based on Particle Swarm Optimization in Manufacturing Grid System , 2008, IEEE Transactions on Industrial Informatics.

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

[21]  Masatoshi Sakawa,et al.  Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms , 2000, Eur. J. Oper. Res..

[22]  Yong Chen,et al.  Concurrent optimal allocation of distributed manufacturing resources using extended Teaching-Learning-Based Optimization , 2017 .

[23]  G. Croes.  A Method for Solving Traveling-Salesman Problems , 1958 .

[24]  Shuai Zhang,et al.  A new manufacturing resource allocation method for supply chain optimization using extended genetic algorithm , 2011 .

[25]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[26]  Gang Ma,et al.  Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems , 2013, Int. J. Comput. Integr. Manuf..

[27]  Wei Xing Zheng,et al.  State Estimation of Discrete-Time Switched Neural Networks With Multiple Communication Channels , 2017, IEEE Transactions on Cybernetics.

[28]  Bijay Ketan Panigrahi,et al.  Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch , 2015 .

[29]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[30]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[31]  Lakshman S. Thakur,et al.  Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain , 2012, Expert Syst. Appl..

[32]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[33]  M. Sakawa,et al.  An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate , 1999 .

[34]  Yefa Hu,et al.  QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system , 2014, Central Eur. J. Oper. Res..

[35]  Loo Hay Lee,et al.  Fourth party logistics routing problem model with fuzzy duration time and cost discount , 2013, Knowl. Based Syst..

[36]  Mohamed Abdel-Baset,et al.  A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems , 2014 .

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

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

[39]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.