Abstract
Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.
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References
Ai LF, Tang ML, Fidge C (2011) Partitioning composite web services for decentralized execution using a genetic algorithm. Future Gener Comput Syst 27:157–172
Alonso G, Casati F, Kuno H, Machiraju V (2003) Web services. Springer, Berlin
Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33(6):369–384
Berbner R, Spahn M, Repp N et al (2006) Heuristics for QoS-aware web service composition. In: IEEE international conference on web services (ICWS06)
Blau B, Conte T (2011) Contracting co-opetitive in service value networks. In: 2011 IEEE conference on commerce and enterprise, computing, pp 173–178
Blau B, Krämer J, Conte T, van Dinther C (2009) Service value networks. In: 2009 IEEE conference on commerce and enterprise computing, pp 194–201
Chan WKV, Hsu C (2012) Service value networks: humans hypernetwork to cocreate value. IEEE Trans Syst Man Cybern A Syst Hum 42(4):802–913
Chen BL (2006) Optimization theory and algorithm, 2nd edn. Tsinghua University Press, Beijing
Cao XB, Qiao H, Xu YW (2007) Negative selection based immune optimization. Adv Eng Softw 38:649–656
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Dasgupta D, Yu S, Nino F (2011) Recent advances in artificial immune systems: models and applications. Appl Soft Comput 11:1574–1587
de Castro LN, Timmis JI (2002) Artificial immune systems: a new vomputational intelligence paradigm. Springer, Berlin
de Castro LN, von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251
De Jong KA (2007) Evolutionary computation: a unified approach. The MIT Press, Cambridge, MA
Di XF, Fan YS, Shen YM (2011) Local martingale difference approach for service selection with dynamic QoS. Comput Math Appl 61(9):2638–2646
Fan XQ, Fang XW, Jiang CJ (2011) Research on Web service selection based on cooperative evolution. Expert Syst Appl 38:9736–9743
Haak S, Blau B (2012) Efficient QoS aggregation in service value networks. In: 2012 45th Hawaii international conference on system sciences, pp 1512–1521
Hu CH, Chen XH, Liang XM (2009) Dynamic services selection algorithm in web services composition supporting cross-enterprises collaboration. J Cent South Univ Technol 2:269–274
Kouchakpour P, Zaknich A, Braunl T (2009) A survey and taxonomy of performance improvement of canonical genetic programming. Knowl Inf Syst 21(1):1–39
Laurentys CA, Ronacher G, Palhares RM, Caminhas WM (2010) Design of an artificial immune system for fault detection: a negative selection approach. Expert Syst Appl 37:5507–5513
Liang W-Y, Huang C-C (2009) The generic genetic algorithm incorporates with rough set theory—an application of the web services composition. Expert Syst Appl 36:5549–5556
Luo YS, Qi Y, Hou D et al (2011) A novel heuristic algorithm for QoS-aware end-to-end service composition. Comput Commun 34(9):1137–1144
Ma Y, Zhang CW (2008) Quick convergence of genetic algorithm for QoS-driven web service selection. Comput Netw 52(5):1093–1104
Menascé DA, Casalicchio E, Dubey V (2010) On optimal service selection in service oriented architectures. Perform Eval 67:659–675
Pop CB, Chifu VR, Salomie I, Dinsoreanu M (2009) Optimal web service composition method based on an enhanced planning graph and using an immune-inspired algorithm. In: IEEE 5th international conference on intelligent computer communication and processing, pp 291–298
Qi LY, Dou WC, Zhang XY, Chen JJ (2012) A QoS-aware composition method supporting cross-platform service invocation in cloud environment. J Comput Syst Sci 78:1316–1329
Salomie I, Vlad M, Chifu VR, Pop CB (2011) Hybrid immune-inspired method for selecting the optimal or a near-optimal service composition. In: Proceedings of the federated conference on computer science and, information systems, pp 997–1003
Skoutas D, Sacharidis D, Simitsis A, Sellis T (2010) Ranking and clustering web services using multicriteria dominance relationships. IEEE Trans Serv Comput 3(3):163–177
Song S, Lee S-W (2012) A goal-driven approach for adaptive service composition using planning. Math Comput Model. doi:10.1016/j.mcm.2012.08.007
Sun SX, Zhao J (2012) A decomposition-based approach for service composition with global QoS guarantees. Inf Sci 199:138–153
Surace C, Worden K (2010) Novelty detection in a changing environment: a negative selection approach. Mech Syst Signal Process 24:1114–1128
Tsesmetzis D, Roussaki I, Sykas E (2008) QoS-aware service evaluation and selection. Eur J Oper Res 191:1101–1112
Wang P, Chao K-M, Lo C-C (2010) On optimal decision for QoS-aware composite service selection. Expert Syst Appl 37:440–449
Wang WB, Sun QB, Yang FC, Zhao XC (2010) An improved particle swarm optimization algorithm for QoS-aware web service selection in service oriented communication. Int J Comput Intell Syst 4(s):18–30
Wang ZJ, Liu ZZ, Zhou XF, Lou YS (2011) An approach for composite web service selection based on DGQoS. Int J Adv Manuf Technol 56:1167–1179
Xiao J, Boutaba R (2005) QoS-aware service composition and adaptation in autonomic communication. IEEE J Sel Areas Commun 23(12):2344–2360
Xu JY, Reiff-Marganiec S (2008) Towards heuristic web services composition using immune algorithm. In: 2008 IEEE international conference on web services, pp 238–245
Yu T, Zhang Y, Lin K-J (2007) Efficient algorithms for web services selection with end-to-end QoS constraints. ACM Trans Web 1(1), Article 6, 26 pages
Zeng LZ, Benatallah B, Ngu Anne HH et al (2004) QoS-aware middleware for web services composition. IEEE Trans Softw Eng 30(5):311–327
Zhan ZH, Zhang J, Li Y et al (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847
Zhao XC, Huang PY, Liu TT, Li XM (2012) A hybrid clonal selection algorithm for quality of service-aware web service selection problem. Int J Innov Comput Inf Control 8(12):8527–8544
Zhao XC, Song BQ, Huang PY et al (2012) An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition. Appl Soft Comput 12(8):2208–2216
Acknowledgments
This research is supported by National Natural Science Foundation of China (61105127, 71171079). We will also awfully thank the reviewers’ helpful and constructive comments and suggestions.
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Zhao, X., Wen, Z. & Li, X. QoS-aware web service selection with negative selection algorithm. Knowl Inf Syst 40, 349–373 (2014). https://doi.org/10.1007/s10115-013-0642-x
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DOI: https://doi.org/10.1007/s10115-013-0642-x