Abstract
Service-oriented computing is a computing paradigm that creates reusable modules over the Internet, often known as Web services. Web service composition aims to accomplish more complex functions by loosely coupling web services. Researchers have been proposing evolutionary computation (EC) techniques for efficiently building up composite services with optimized non-functional quality (i.e., QoS). Some of these techniques employ multi-objective EC algorithms to handle conflict qualities in QoS for fully automated service composition. One recent state-of-art work hybridizes NSGA-II and MOEA/D, which allows the multi-objective service composition problem to be decomposed into many scalar optimization subproblems, where a simple form of local search can be easily applied. However, their local search is considered to be less effective and efficient because it is randomly applied to a predefined large number of subproblems without focusing on the most suitable candidate solutions. In this paper, we propose a memetic NSGA-II with probabilistic model-based local search based on Estimation of Distribution Algorithm (EDA). In particular, a clustering technique is employed to select suitable Pareto solutions for local search. Each selected solution and its belonged cluster members are used to learn a distribution model that samples new solutions for local improvements. Besides that, a more challenging service composition problem that optimizes both functional and non-functional quality is considered. Experiments have shown that our method can effectively and efficiently produce better Pareto optimal solutions compared to other state-of-art methods in the literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Masri, E., Mahmoud, Q.H.: Qos-based discovery and ranking of web services. In: Proceedings of 16th International Conference on Computer Communications and Networks, ICCCN 2007, pp. 529–534. IEEE (2007)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Bansal, A., Blake, M.B., Kona, S., Bleul, S., Weise, T., Jaeger, M.C.: WSC-08: continuing the web services challenge. In: 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services, pp. 351–354. IEEE (2008)
Chen, Y., Huang, J., Lin, C.: Partial selection: an efficient approach for QoS-aware web service composition. In: IEEE ICWS, pp. 1–8. IEEE (2014)
Curbera, F., Nagy, W., Weerawarana, S.: Web services: why and how. In: Workshop on Object-Oriented Web Services-OOPSLA (2001)
Da Silva, A.S., Ma, H., Mei, Y., Zhang, M.: A hybrid memetic approach for fully automated multi-objective web service composition. In: 2018 IEEE International Conference on Web Services, pp. 26–33. IEEE (2018)
Da Silva, A.S., Mei, Y., Ma, H., Zhang, M.: Fragment-based genetic programming for fully automated multi-objective web service composition. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 353–360. ACM (2017)
Da Silva, A.S., Mei, Y., Ma, H., Zhang, M.: Evolutionary computation for automatic web service composition: an indirect representation approach. J. Heuristics 24(3), 425–456 (2018)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Fogel, D.B.: What is evolutionary computation? IEEE Spectr. 37(2), 26–32 (2000)
Gabrel, V., Manouvrier, M., Murat, C.: Web services composition: complexity and models. Discrete Appl. Math. 196, 100–114 (2015)
Jiang, S., Ong, Y.S., Zhang, J., Feng, L.: Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Trans. Cybern. 44(12), 2391–2404 (2014)
Kona, S., Bansal, A., Blake, M.B., Bleul, S., Weise, T.: WSC-2009: a quality of service-oriented web services challenge. In: 2009 IEEE Conference on Commerce and Enterprise Computing, pp. 487–490. IEEE (2009)
Lacomme, P., Prins, C., Ramdane-Cherif, W.: Competitive memetic algorithms for arc routing problems. Ann. Oper. Res. 131(1–4), 159–185 (2004)
Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30581-1_5
Rodriguez-Mier, P., Mucientes, M., Lama, M., Couto, M.I.: Composition of web services through genetic programming. Evol. Intel. 3(3–4), 171–186 (2010)
Tsutsui, S.: A comparative study of sampling methods in node histogram models with probabilistic model-building genetic algorithms. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 4, pp. 3132–3137. IEEE (2006)
Wang, C., Ma, H., Chen, A., Hartmann, S.: Comprehensive quality-aware automated semantic web service composition. In: Peng, W., Alahakoon, D., Li, X. (eds.) AI 2017. LNCS (LNAI), vol. 10400, pp. 195–207. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63004-5_16
Wang, C., Ma, H., Chen, A., Hartmann, S.: GP-based approach to comprehensive quality-aware automated semantic web service composition. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 170–183. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_15
Wang, C., Ma, H., Chen, G., Hartmann, S.: Towards fully automated semantic web service composition based on estimation of distribution algorithm. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 458–471. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03991-2_42
Wang, C., Ma, H., Chen, A., Hartmann, S.: A memetic NSGA-II with EDA-based local search for fully automated multiobjective web service composition. In: Genetic and Evolutionary Computation Conference Companion. ACM (2019), (To appear)
Wang, C., Ma, H., Chen, G.: EDA-based approach to comprehensive quality-aware automated semantic web service composition. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 147–148. ACM (2018)
Wang, C., Ma, H., Chen, A., Hartmann, S.: Knowledge-driven automated web service composition—an EDA-based approach. In: Hacid, H., Cellary, W., Wang, H., Paik, H.-Y., Zhou, R. (eds.) WISE 2018. LNCS, vol. 11234, pp. 135–150. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02925-8_10
Yin, H., Zhang, C., Zhang, B., Guo, Y., Liu, T.: A hybrid multiobjective discrete particle swarm optimization algorithm for a SLA-aware service composition problem. Math. Probl. Eng. 2014, 14 (2014)
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1, 32–49 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, C., Ma, H., Chen, G. (2019). Using EDA-Based Local Search to Improve the Performance of NSGA-II for Multiobjective Semantic Web Service Composition. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-27618-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27617-1
Online ISBN: 978-3-030-27618-8
eBook Packages: Computer ScienceComputer Science (R0)