Computer Science ›› 2019, Vol. 46 ›› Issue (2): 187-195.doi: 10.11896/j.issn.1002-137X.2019.02.029

• Artificial Intelligence • Previous Articles     Next Articles

Multi-attribute Decision Making and Adaptive Genetic Algorithm for Solving QoS Optimization of Web Service Composition

LU Cheng-hua1,2, KOU Ji-song1   

  1. College of Management and Economics,Tianjin University,Tianjin 300072,China1
    College of Pearl River,Tianjin University of Finance and Economics,Tianjin 301811,China2
  • Received:2018-03-30 Online:2019-02-25 Published:2019-02-25

Abstract: With the increasing of service-oriented computing,the research on Web service composition based on quality of service (QoS) becomes an inevitable trend.With respect of the multi-dimensional nature and mutual contradiction,this paper transformed the optimization of Web service composition based on QoS into the problem of multi-attribute decision making to resolve it.The distances of each solution to the positive ideal solution (PIS) and the negative ideal solution (NIS) were summed up by means of a compromise coefficient.Finally,a set of ranked Web services were provided to users for a flexible choice.The traditional multi-attribute decision making method can not effectively solve the large-scale search space of Web service composition.Therefore,in order to solve the NP-hard problem of Web service composition optimization better,this paper developed an approach combining the multi-attribute decision making and adaptive genetic algorithm (MADMAGA).The experiments were conducted on a real and comprehensive QoS dataset.The experimental results indicate that the method can find the globally optimal solution in a short period of time.The ranking result of solutions is close to the true sort.Moreover,the proposed method has better scalability for solving the large-scale problem of Web service composition optimization.

Key words: Genetic algorithm, Multi-attribute decision making, Quality of service, Web service composition

CLC Number: 

  • TP301
[1]XU L,LI Y H,CHEN L,et al.A Testing Method for Web Servi- ces Focusing on User Requirements [J].Chinese Journal of Computers,2014,37(3):512-521.(in Chinese)
[2]WU Y P,BAO W D,ZHANG W M,et al.Web Service Composition Systems Survey [J].Computer Science,2011,38(9):1-4.(in Chinese)
[3]WANG P W,DING Z J,JIANG C J,et al.Constraint-Aware Approach to Web Service Composition [J].IEEE Transactions on Systems Man & Cybernetics Systems,2017,44(6):770-784.
[4]ROUACHED M,SALLAY H.A semantic QoS-aware web servi- ces composition framework [J].International Journal of Business Information Systems,2017,17(1):94.
[5]JATOTH C,GANGADHARAN G R,BUYYA R.Computa- tional Intelligence based QoS-aware Web Service Composition:A Systematic Literature Review [J].IEEE Transactions on Ser-vices Computing,2017,PP(99):1.
[6]BENSLIMANE S M,HUCHARD M,et al.QoS-aware optimal and automated semantic web service composition with user’s constraints[J].Service Oriented Computing & Applications,2017,11(2):1-19.
[7]LI J,ZHAO Y,LIU M,et al.An adaptive heuristic approach for distributed QoS-based service composition[C]∥ISCC’10 Proceedings of the IEEE Symposium on Computer and Communications.2010:687-694.
[8]ZHANG K,GAO H H,ZHU Y H,et al.QoS Dynamic Web Services Composition Method Based on Improved Simulated Annealing Algorithm[J].Journal of Applied Sciences,2017,35(5):570-584.(in Chinese)
[9]WANG L,ZHAO S S.Research on the Two-stage Heuristic Algorithm Based Web Service Composition Optimization [J].Electronic Technology,2012(10):19-24.(in Chinese)
[10]LI J,QIAO R,LIU Z Z.Solution of Web Service Composition Scheduling Problem Combining with Game Theory and Multi-objective MILP [J].Computer Engineering,2016,42(1):11-17.(in Chinese)
[11]WANG P,CHAO K M,LO C C.On optimal decision for QoS-aware composite service selection[J].Expert Systems with Applications,2010,9(6):440-449.
[12]LUO Y S,YANG K,TANG Q,et al.A multi-criteria network-aware service composition algorithm in wireless environments [J].Computer Communications,2012,35(15):1882-1892.
[13]MARDUKHI F,NEMATBAKHSH N,ZAMANIFAR K,et al. QoS decomposition for service composition using genetic algorithm[J].Applied Soft Computing,2013,13(7):3409-3421.
[14]ANGARITA R,RUKOZ M,CARDINALE Y.Modeling dyna- mic recovery strategy for composite web services execution [J].World Wide Web-internet & Web Information Systems,2016,19(1):1-21.
[15]GAO H,YAN J,MU Y.Trust-oriented QoS-aware composite service selection based on genetic algorithms[J].Concurrency & Computation Practice & Experience,2014,26(2):500-515.
[16]WU Q L,ZHOU T H.Research on Quality of Service-based Dynamic Web Service Composition Method [J].Computer Application and Software,2016,33(5):20-23.(in Chinese)
[17]ZHANG Y P,JING Z H,ZHANG Y W,et al.Dynamic Web Service Composition Based on Discrete Particle Swarm Optimization[J].Computer Science,2015,42(6):71-75.(in Chinese)
[18]WANG L,SHEN J,LUO J.Facilitating an ant colony algorithm for multi-objective data-intensive service provision[J].Journal of Computer & System Sciences,2015,81(4):734-746.
[19]TRAN V X,TSUJI H,MASUDA R.A new QoS ontology and its QoS-based ranking algorithm for Web services[J].Simulation Modelling Practice & Theory,2009,17(8):1378-1398.
[20]FANG X R.Study on Filter Algorithm of QoS-Based Fuzzy Multi-Attribute Web Service Composition [J].Applied Mecha-nics & Materials,2012,182-183:2131-2135.
[21]YANG J,LI D F,LAI L B.Composite Service Multi-attribute Selection Method Based on Message Negotiation Under the Web Service Environment [J].Operations Research and Management Science,2015(3):134-141.(in Chinese)
杨洁,李登峰,赖礼邦.Web 服务环境下基于信息协商的组合服务多属性选择方法[J].运筹与管理,2015(3):134-141.
[22]WANG L,SHEN J,LUO J.Facilitating an ant colony algorithm for multi-objective data-intensive service provision [J].Journal of Computer & System Sciences,2015,81(4):734-746.
[23]LIAO J,LIU Y,WANG J,et al.Lightweight approach for multi-objective web service composition [J].IET Software,2016,10(4):116-124.
[24]SILVA A S D,MEI Y,MA H,et al.Fragment-based genetic programming for fully automated multi-objective web service composition[C]∥The Genetic and Evolutionary Computation Conference.2017:353-360.
[25]SUN S X.A decomposition-based approach for service composition with global QoS guarantees[J].Information Sciences,2012,199(15):138-153.
[26]WANG T C,LEE H D.Developing a fuzzy TOPSIS approach based on subjective weights and objective weights [J].Expert Systems with Applications,2009,36(5):8980-8985.
[27]LAUMANNS M,THIELE L,DEB K,et al.Combining convergence and diversity in evolutionary multiobjective optimization [J].Evolutionary Computation,2014,10(3):263-282.
[28]AL-MASRI E,MAHMOUD Q H.Investigating Web Services on the World Wide Web[C]∥ International Conference on World Wide Web,WWW 2008,Beijing,China,April.DBLP,2008:795-804.
[29]WANG H.Robust Control of the Output Probability Density Functions for Multivariable Stochastic Systems [J].IEEE Transactions on Automatic Control,1999,44(11):2103-2107.
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