Skip to main content
SpringerLink
Log in

A New Hybrid Approach Using Genetic Algorithm and Q-learning for QoS-aware Web Service Composition

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

Abstract

Web Service composition (WSC) is a technology for building an application in Service Oriented Architecture (SOA). In WSC the sets of atomic Web services combine together to satisfy users’ requirements. Due to the increase in number of Web services with the same functionality and variety of Quality of Services (QoS), it became difficult to find a suitable Web service that satisfies the functional requirements, as well as optimizing the QoS. This has led to the emergence of QoS-aware WSC. However, to find an optimal solution in QoS-aware WSC is an NP-hard problem. In this paper, we propose a new approach that combines the use of Genetic Algorithm (GA) and Q-learning to find the optimal WSC. The performance of GAs depends on the initial population, so the Q-learning is utilized to generate the initial population to enhance the effectiveness of GA. We implemented our approach over the .NET Framework platform 4.7 using C# programming language. The experiment results show the effectiveness of our proposed approach compared to Q-learning algorithm and GA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hsieh, F.-S., Lin, J.-B.: A Self-adaptation Scheme for Workflow Management in Multi-agent Systems. J. Intell. Manuf. 27(1), 131–148 (2016)

    Article  Google Scholar 

  2. Ide, N., Pustejovsky, J.: What does interoperability mean, anyway? Toward an operational definition of interoperability for language technology. In: Proceedings of the 2nd International Conference on Global Interoperability for Language Resources (ICGL), Hong Kong (2010)

    Google Scholar 

  3. Rohallah, B., Ramdane, M., Zaid, S.: Agents and owl-s based semantic web service discovery with user preference support. Int. J. Web Semant. Technol. (IJWesT) 4(2), 57–75 (2013)

    Article  Google Scholar 

  4. Li, W.: Towards a resilient service oriented computing based on ad-hoc web service compositions in Dynamic Environments (Doctoral Dissertation). Institut d’Optique Graduate School (2014)

    Google Scholar 

  5. Eck, P., Wieringa, R.: Requirements engineering for service-oriented computing: a position paper. In: Proceedings of the 1st International Workshop on e-Services at ICEC, USA, pp. 23–28 (2003)

    Google Scholar 

  6. Wang, L., Shen, J.: A systematic review of bio-inspired service concretization. IEEE Trans. Serv. Comput. PP(99), 3 (2014)

    Google Scholar 

  7. Chen, Y., Huang, J., Lin, C.: Partial selection: an efficient approach for QoS-aware web service composition. In: Proceedings of the 2014 IEEE International Conference on Web Services, USA, p. 1 (2014)

    Google Scholar 

  8. Kamio, S., Iba, H.: adaptation technique for integrating genetic programming and reinforcement learning for real robots. IEEE Trans. Evol. Comput. 9(3), 318–333 (2005)

    Article  Google Scholar 

  9. dos Santos, J.P.Q., de Lima Jr., F.C., Magalhães, R.M., de Melo, J.D., Neto, A.D.D.: A parallel hybrid implementation using genetic algorithms, GRASP and reinforcement learning for the salesman traveling problem. In: Tenne, Y., Goh, C.K. (eds.) Computational Intelligence in Expensive Optimization Problems, vol. 2, pp. 345–369. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Canfora, G., Penta, M., Esposito, R., Villan, M.: An Approach for QoS-aware Service Composition based on Genetic Algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, USA, pp. 1069–1075 (2005)

    Google Scholar 

  11. Ma, H., Wang, A., Zhang, M.: A hybrid approach using genetic programming and greedy search for QoS-aware web service composition. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII, pp. 180–205 (2005)

    Google Scholar 

  12. Liu, H., Zhong, F., Ouyang, B., Wu, J.: An approach for QoS-aware web service composition based on improved genetic algorithm. In: Proceedings of the 2010 International Conference on Web Information Systems and Mining, IEEE, China, pp. 123–128 (2010)

    Google Scholar 

  13. Ai, L., Tang, M.: A penalty-based genetic algorithm for QoS-aware web service composition with inter-service dependencies and conflicts. In: Proceedings of the 4th IEEE International Conference on eScience, USA, p. 494 (2008)

    Google Scholar 

  14. Ai, L., Tang, M.: QoS-based web service composition accommodating inter-service dependencies using minimal-conflict hill-climbing repair genetic algorithm. In: Proceedings of the 4th IEEE International Conference on eScience, USA, p. 121 (2008)

    Google Scholar 

  15. Pop, C. B., Chifu, V., Salomie, I., Dinsoreanu, M., David, T., Acretoaie, V.: Ant-inspired technique for automatic web service composition and selection. In: Proceedings of the 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, p. 455 (2012)

    Google Scholar 

  16. Qiqing, F., Xiaoming, P., Qinghua, L., Yahui, H.: A global QoS optimizing web services selection algorithm based on MOACO for dynamic web service composition. In: Proceedings of the 2009 International Forum on Information Technology and Applications, Chengdu, China, p. 38 (2009)

    Google Scholar 

  17. Shanshan, Z., Lei, w., Lin, M., Zepeng, W.: An improved ant colony optimization algorithm for QoS-Aware dynamic web service composition. In: Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, China, p. 1998 (2012)

    Google Scholar 

  18. Wang, D., Huang, H., Xie, C.: A Novel adaptive web service selection algorithm based on ant colony optimization for dynamic web service composition. In: Proceedings of the 14th International Conference, ICA3PP, Dalian, China, pp. 391–399 (2014)

    Google Scholar 

  19. Li, W., Yan-xiang, H.: Web service composition based on QoS with Chaos Particle Swarm Optimization. In: Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), China, pp. 1–4 (2010)

    Google Scholar 

  20. Moustafa, A., Zhang, M.: Towards proactive web service adaptation. In: Proceedings of the 24th International Conference Advanced Information Systems Engineering (CAiSE), Poland, pp. 473–485 (2012)

    Google Scholar 

  21. Elsayed, D.H., Nasr, E.S., El Ghazali, A.E.D.M., Gheith, M.H.: Appraisal and analysis of various self-adaptive web service composition approaches. In: Ramachandran, M., Mahmood, Z. (eds.) Requirements Engineering for Service and Cloud Computing, pp. 229–246. Springer, Cham (2017). doi:10.1007/978-3-319-51310-2_10

    Chapter  Google Scholar 

  22. Wang, H., Wang, X., Hu, X., Zhang, X., Gu, M.: A multi-agent reinforcement learning approach to dynamic service composition. J. Inform. Sci. 363, 96–119 (2016)

    Article  Google Scholar 

  23. Shehu, U., Epiphaniou, G., Safdar, G.A.: A survey of QoS-aware web service composition techniques. Int. J. Comput. Appl. 89(12), 11 (2014)

    Google Scholar 

  24. Jatoth, C., Gangadharan, G.R., Buyya, R.: Computational intelligence based QoS-aware web service composition: a systematic literature review. IEEE Trans. Serv. Comput. PP(99), 1–88 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems Technology, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt

    Doaa H. Elsayed

  2. Independent Researcher, Cairo, Egypt

    Eman S. Nasr

  3. Department of Computer and Information Systems, Sadat Academy for Management Sciences, Cairo, Egypt

    Alaa El Din M. El Ghazali

  4. Department of Computer Science, Cairo University, Cairo, Egypt

    Mervat H. Gheith

Authors
  1. Doaa H. Elsayed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eman S. Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alaa El Din M. El Ghazali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mervat H. Gheith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doaa H. Elsayed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Elsayed, D.H., Nasr, E.S., El Ghazali, A.E.D.M., Gheith, M.H. (2018). A New Hybrid Approach Using Genetic Algorithm and Q-learning for QoS-aware Web Service Composition. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64861-3_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation