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Journal of Optimization
Volume 2017, Article ID 4685923, 9 pages
https://doi.org/10.1155/2017/4685923
Research Article

A Novel Distributed Quantum-Behaved Particle Swarm Optimization

1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an, Shaanxi Province 710071, China
2School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China

Correspondence should be addressed to Yangyang Li; moc.361@197_yyl

Received 29 December 2016; Accepted 4 April 2017; Published 3 May 2017

Academic Editor: Gexiang Zhang

Copyright © 2017 Yangyang Li 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. Y.-J. Gong, W.-N. Chen, Z.-H. Zhan et al., “Distributed evolutionary algorithms and their models: a survey of the state-of-the-art,” Applied Soft Computing Journal, vol. 34, pp. 286–300, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Fu, K. Ren, J. Shu, X. Sun, and F. Huang, “Enabling personalized search over encrypted outsourced data with efficiency improvement,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 9, pp. 2546–2559, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Xia, X. Wang, X. Sun, and Q. Wang, “A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 340–352, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Fu, F. Huang, X. Sun, A. Vasilakos, and C.-N. Yang, “Enabling Semantic Search based on Conceptual Graphs over Encrypted Outsourced Data,” IEEE Transactions on Services Computing, no. 99, pp. 1–1, 2016. View at Publisher · View at Google Scholar
  5. Z. Fu, X. Sun, Q. Liu, L. Zhou, and J. Shu, “Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing,” IEICE Transactions on Communications, vol. E98B, no. 1, pp. 190–200, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Q. Liu, W. Cai, J. Shen, Z. Fu, X. Liu, and N. Linge, “A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment,” Security and Communication Networks, vol. 9, no. 17, pp. 4002–4012, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Kong, M. Zhang, and D. Ye, “A belief propagation-based method for task allocation in open and dynamic cloud environments,” Knowledge-Based Systems, vol. 115, pp. 123–132, 2017. View at Publisher · View at Google Scholar
  8. Z. Fu, X. Sun, S. Ji, and G. Xie, “Towards efficient content-aware search over encrypted outsourced data in cloud,” in Proceedings of the 35th Annual IEEE International Conference on Computer Communications, (IEEE INFOCOM '16), San Francisco, Calif, USA, April 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. F. M. Johar, F. A. Azmin, M. K. Suaidi et al., “A review of genetic algorithms and parallel genetic algorithms on graphics processing unit (GPU),” in Proceeding of the 2013 IEEE International Conference on Control System, Computing and Engineering, (ICCSCE '13), pp. 264–269, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. Q. Yu, C. Chen, and Z. Pan, “Parallel genetic algorithms on programmable graphics hardware,” in Proceeding of the International Conference on Advances in Natural Computation, vol. 3612, pp. 1051–1059, Springer-Verlag. View at Publisher · View at Google Scholar
  11. S. Tsutsui and N. Fujimoto, “Solving quadratic assignment problems by genetic algorithms with GPU computation,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '09), pp. 2523–2530, Montreal, Canada, July 2009. View at Publisher · View at Google Scholar
  12. P. Pospíchal, “GPU-based acceleration of the genetic algorithm,” Gecco Competition, 2009. View at Google Scholar
  13. J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Aridhi, P. Lacomme, L. Ren, and B. Vincent, “A MapReduce-based approach for shortest path problem in large-scale networks,” Engineering Applications of Artificial Intelligence, vol. 41, pp. 151–165, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. T. G. Addair, D. A. Dodge, W. R. Walter, and S. D. Ruppert, “Large-scale seismic signal analysis with Hadoop,” Computers and Geosciences, vol. 66, no. 2, pp. 145–154, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Li, J. Song, F. Zhang, X. Ouyang, and S. U. Khan, “MapReduce-based fast fuzzy c-means algorithm for large-scale underwater image segmentation,” Future Generation Computer Systems, vol. 65, pp. 90–101, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Wang, X. Meng, and Y. Zhang, “An adaptive user preferences elicitation scheme for location recommendation,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 943–949, 2016. View at Publisher · View at Google Scholar
  18. A. Verma, X. Llorà, D. E. Goldberg, and R. H. Campbell, “Scaling genetic algorithms using MapReduce,” in Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA '09), pp. 13–18, Pisa, Italy, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Jin, C. Vecchiola, and R. Buyya, “MRPGA: an extension of MapReduce for parallelizing genetic algorithms,” in Proceedings of the IEEE 4th International Conference on eScience (eScience ’08), pp. 214–221, Indianapolis, Ind, USA, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Llorà, A. Verma, R. H. Campbell, and D. E. Goldberg, “When huge is routine: scaling genetic algorithms and estimation of distribution algorithms via data-intensive computing,” Studies in Computational Intelligence, vol. 269, pp. 11–41, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Tagawa and T. Ishimizu, “Concurrent differential evolution based on MapReduce,” International Journal of Computers, vol. 4, no. 4, pp. 161–168, 2010. View at Google Scholar
  22. C. Zhou, “Fast parallelization of differential evolution algorithm using MapReduce,” in Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, (GECCO '10), pp. 1113–1114, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Wu, G. Wu, and M. Yang, “A MapReduce based ant colony optimization approach to combinatorial optimization problems,” in Proceedings of the 8th International Conference on Natural Computation (ICNC '12), pp. 728–732, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. A. W. McNabb, C. K. Monson, and K. D. Seppi, “Parallel PSO using MapReduce,” in Proceedings of the 2007 IEEE Congress on Evolutionary Computation, (CEC '07), pp. 7–14, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Wang, Y. Li, Z. Chen, and Y. Xue, “Cooperative particle swarm optimization using MapReduce,” Soft Computing, pp. 1–11, 2016. View at Publisher · View at Google Scholar
  26. Y. Li, Z. Chen, Y. Wang, and L. Jiao, “Quantum-behaved particle swarm optimization using mapreduce,” Bio-Inspired Computing—Theories and Applications, vol. 682, pp. 173–178, 2016. View at Publisher · View at Google Scholar
  27. J. Li, J. Dang, and Y. Wang, “Medical image segmentation algorithm based on quantum clonal evolution and two-dimensional tsallis entropy,” Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, vol. 26, no. 3, pp. 465–471, 2014. View at Google Scholar · View at Scopus
  28. O. P. Patel and A. Tiwari, “Novel quantum inspired binary neural network algorithm,” Sādhanā, vol. 41, no. 11, pp. 1299–1309, 2016. View at Publisher · View at Google Scholar
  29. L. Jiao, Y. Li, M. Gong, and X. Zhang, “Quantum-inspired immune clonal algorithm for global optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, no. 5, pp. 1234–1253, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Li, R. Xiang, L. Jiao, and R. Liu, “An improved cooperative quantum-behaved particle swarm optimization,” Soft Computing, vol. 16, no. 6, pp. 1061–1069, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, no. 3, pp. 303–351, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Sun, B. Feng, and W. Xu, “Particle swarm optimization with particles having quantum behavior,” in Proceedings of the IEEE 2004 Congress on Evolutionary Computation, pp. 325–331, 2004. View at Scopus
  33. J. Sun, W. Fang, X. Wu, V. Palade, and W. Xu, “Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection,” Evolutionary Computation, vol. 20, no. 3, pp. 349–393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the International Conference on Neural Networks (ICNN '95), pp. 1942–1948, Perth, Australia, 1995. View at Publisher · View at Google Scholar
  35. Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 69–73, Anchorage, Alaska, USA, May, 1998. View at Publisher · View at Google Scholar
  36. F. van den Bergh, An analysis of particle swarm optimizers [Ph.D. dissertation], Department of Computer Science at the University of Pretoria, Pretoria, South Africa, 2002.
  37. J. J. Liang, B. Y. Qu, P. N. Suganthan, and G. Alfredo, “Problem definitions and evaluation criteria for the cec 2013 special session on real-parameter optimization,” Tech. Rep. 201212, Computational Intelligence Laboratory, Singapore; Zhengzhou University, Zhengzhou, China; Nanyang Technological University, January 2013. View at Google Scholar
  38. C. Hipp and M. Plum, “Optimal investment for insurers,” Insurance: Mathematics and Economics, vol. 27, no. 2, pp. 215–228, 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Bemporad and M. Morari, “Control of systems integrating logic, dynamics, and constraints,” Automatica, vol. 35, no. 3, pp. 407–427, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  40. C. Patrascioiu and C. Marinoiu, “The applications of the non-linear equations systems algorithms for the heat transfer processes,” in Proceedings of the 12th WSEAS International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems (MAMECTIS '10), pp. 30–35, 2010. View at Scopus
  41. B. P. Mann and N. D. Sims, “Energy harvesting from the nonlinear oscillations of magnetic levitation,” Journal of Sound and Vibration, vol. 319, no. 1-2, pp. 515–530, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. N. Filipovic, Z. Teng, M. Radovic, I. Saveljic, D. Fotiadis, and O. Parodi, “Computer simulation of three-dimensional plaque formation and progression in the carotid artery,” Medical & Biological Engineering & Computing, vol. 51, no. 6, pp. 607–616, 2013. View at Publisher · View at Google Scholar
  43. C. L. Collins, “Forward kinematics of planar parallel manipulators in the Clifford algebra of P2,” Mechanism and Machine Theory, vol. 37, no. 8, pp. 799–813, 2002. View at Publisher · View at Google Scholar · View at Scopus
  44. W. Song, Y. Wang, H.-X. Li, and Z. Cai, “Locating multiple optimal solutions of nonlinear equation systems based on multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 3, pp. 414–431, 2015. View at Publisher · View at Google Scholar · View at Scopus