Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 507517, 11 pages
http://dx.doi.org/10.1155/2014/507517
Research Article

An Optimization Algorithm for Multipath Parallel Allocation for Service Resource in the Simulation Task Workflow

1PLA University of Science & Technology, Nanjing 210007, China
2Nanjing Artillery Academy, Nanjing 210110, China

Received 9 October 2013; Accepted 25 November 2013; Published 12 May 2014

Academic Editors: W. Sun, G. Zhang, and J. Zhou

Copyright © 2014 Zhiteng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. König, N. Lohmann, S. Moser, C. Stahl, and K. Wolf, “Extending the compatibility notion for abstract WS-BPEL processes,” in Proceedings of the 17th International Conference on World Wide Web (WWW '08), pp. 785–794, Beijing, China, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Salatge and J.-C. Fabre, “Fault tolerance connectors for unreliable Web Services,” in Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN '07), pp. 51–60, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Harney and P. Doshi, “Speeding up adaptation of web service compositions using expiration times,” in Proceedings of the 16th International World Wide Web Conference (WWW '07), pp. 1023–1032, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Sirin, Combining Description Logic Reasoning with AI Planning for Composition of Web Services, University of Maryland, College Park, Md, USA, 2006.
  5. J. Rao and X. Su, “A survey of automated web service composition methods,” in Proceedings of the 1st International Workshop on Semantic Web Services and Web Process Composition (SWSWPC '05), vol. 3387, pp. 43–54, July 2005. View at Scopus
  6. S. Mcilraith and T. C. Son, “Adapting golog for composition of semantic web services,” in Proceedings of the 8th International Conference on Knowledge Representation and Reasoning, pp. 482–496, Morgan Kaufmann, Toulouse, France, 2002.
  7. S. Narayanan and S. A. McIlraith, “Simulation, verification and automated composition of web services,” in Proceedings of the 11th International Conference on World Wide Web (WWW '02), pp. 77–88, ACM, New York, NY, USA, May 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Yajuan, Research on the Approaches for Web Services Composition, Jilin University, Changchun, China, 2011.
  9. X. Fan, C. Jiang, X. Fang, and Z. Ding, “Dynamic web service selection based on discrete particle swarm optimization,” Journal of Computer Research and Development, vol. 47, no. 1, pp. 147–156, 2010. View at Google Scholar · View at Scopus
  10. H. Zheng, W. Zhao, J. Yang, and A. Bouguettaya, “QoS analysis for Web service composition,” in Proceedings of the IEEE International Conference on Service Computing (SCC '09), pp. 235–242, Bangalore, India, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Zeng, B. Benatallah, A. H. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, “QoS-aware middleware for Web services composition,” IEEE Transactions on Software Engineering, vol. 30, no. 5, pp. 311–327, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Xiaohao, H. Dan, L. Xueshan, and L. Junxian, “Parallel optimization method of service resource in military information system,” Systems Engineering Theory & Practice, vol. 32, no. 9, pp. 2078–2086, 2009. View at Google Scholar
  13. D. Deutsch, “Quantum theory, the Church-Turing principle and the universal quantum computer,” Proceedings of The Royal Society of London A: Mathematical and Physical Sciences, vol. 400, no. 1818, pp. 97–117, 1985. View at Google Scholar · View at Scopus
  14. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 1st edition, 2000.
  15. S.-Y. Li and P.-C. Li, “Quantum genetic algorithm based on real encoding and gradient information of object function,” Journal of Harbin Institute of Technology, vol. 38, no. 8, pp. 1216–1223, 2006. View at Google Scholar · View at Scopus
  16. P. C. Li and S. Y. Li, “Quantum-inspired evolutionary algorithm for continous spaces optimization based on Bloch coordinates of qubits,” Neurocomputing, vol. 72, pp. 581–591, 2008. View at Google Scholar
  17. X. Shaohua, X. Chen, H. Xing, W. Ying, and L. Panchi, “Improved quantum genetic algorithm with double chains and its application,” Application Research of Compute, vol. 26, no. 7, pp. 2090–2092, 2010. View at Google Scholar
  18. S. Li and P. Li, Quantum Computing and Quantum Optimization Computing Algorithm, Harbin Polytechnical University, 2009.
  19. K.-H. Han and J.-H. Kim, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 580–593, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. A. D. S. Nicolau, R. Schirru, and A. Alvarenga de Moura Meneses, “Quantum evolutionary algorithm applied to transient identification of a nuclear power plant,” Progress in Nuclear Energy, vol. 53, no. 1, pp. 86–91, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. J. X. V. Neto, D. L. de Andrade Bernert, and L. dos Santos Coelho, “Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones,” Energy Conversion and Management, vol. 52, no. 1, pp. 8–14, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Xiao, J. Xu, Z. Chen, K. Zhang, and L. Pan, “A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding,” Computers and Mathematics with Applications, vol. 57, no. 11-12, pp. 1949–1958, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Gu, M. Gu, C. Cao, and X. Gu, “A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem,” Computers and Operations Research, vol. 37, no. 5, pp. 927–937, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. W. Zhiteng, Z. Hongjun, Z. Rui, X. Ying, and H. Jian, “Quantum genetic algorithm based on multi-chain coding scheme,” Computers & Operations Research, vol. 29, no. 26, pp. 2061–2064, 2012. View at Google Scholar