Open Access
MATEC Web of Conferences
Volume 28, 2015
2015 the 4th International Conference on Advances in Mechanics Engineering (ICAME 2015)
Article Number 06001
Number of page(s) 6
Section Computer theory and Application Technology
Published online 28 October 2015
  1. Swarnkar, N., Singh, A. P. A. K., & Shankar, R. (2013). A Survey of Load Balancing Techniques in Cloud Computing. International Journal of Engineering, 2(8), 800–804 [Google Scholar]
  2. Lin, C. T. (2013). Comparative Based Analysis of Scheduling Algorithms for Resource Management in Cloud Computing Environment. International Journal of Computer Science and Engineering, 1(1), 17–23 [Google Scholar]
  3. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimzation. In Proceedings if IEEE international conference on neural networks (Vol. 4, No. 2, pp. 1942–1948). [Google Scholar]
  4. Shi, Y., & Eberhart, R. (1998, May). A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on (pp. 69–73). IEEE. [Google Scholar]
  5. Chaturvedi, K. T., Pandit, M., & Srivastava, L. (2009). Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. International Journal of Electrical Power & Energy Systems, 31(6), 249–257. [CrossRef] [Google Scholar]
  6. Tripathi, P. K., Bandyopadhyay, S., & Pal, S. K. (2007). Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Information Sciences, 177(22), 5033–5049. [CrossRef] [MathSciNet] [Google Scholar]
  7. Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. H. (2009). Adaptive particle swarm optimization. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(6), 1362–1381. [CrossRef] [Google Scholar]
  8. Liu, H., Abraham, A., & Hassanien, A. E. (2010). Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Generation Computer Systems, 26(8), 1336–1343. [CrossRef] [Google Scholar]
  9. Xue, S. J., & Wu, W. (2012). Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10(7), 1560–1566. [CrossRef] [Google Scholar]
  10. Chen, R. M., & Wang, C. M. (2011, February). Project scheduling heuristics-based standard PSO for task-resource assignment in heterogeneous grid. In Abstract and Applied Analysis (Vol. 2011). Hindawi Publishing Corporation. [Google Scholar]
  11. Zhan, S., & Huo, H. (2012). Improved PSO-based Task Scheduling Algorithm in Cloud Computing. Journal of Information & Computational Science, 9(13), 3821–3829. [Google Scholar]
  12. Wu, Z., Ni, Z., Gu, L., & Liu, X. (2010, December). A revised discrete particle swarm optimization for cloud workflow scheduling. In Computational Intelligence and Security (CIS), 2010 International Conference on (pp. 184–188). IEEE. [Google Scholar]
  13. Liu, H., Abraham, A., & Hassanien, A. E. (2010). Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Generation Computer Systems, (26(8), 1336–1343. [CrossRef] [Google Scholar]
  14. Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010, April). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on (pp. 400–407). IEEE. [Google Scholar]
  15. Ratnaweera, A., Halgamuge, S., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. Evolutionary Computation, IEEE Transactions on, 8(3), 240–255. [Google Scholar]
  16. Xuanping, Z., Du Yuping, Q. G., & Zheng, Q. (2005). Adaptive Particle Swarm Algorithm with Dynamically Changing Inertia Weight. Journal of xi’an jiaotong university, (39, 10), 1039–1042. [Google Scholar]
  17. Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., & Ehsan, M. (2012). Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems. International Journal of Electrical Power & Energy Systems, 42(1), 508–516. [CrossRef] [Google Scholar]
  18. Masrom, S., Abidin, S. Z., Omar, N., & Nasir, K. (2013). Time-Varying mutation in particle swarm optimization. In Intelligent Information and Database Systems (pp. 31–40). Springer Berlin Heidelberg. [Google Scholar]
  19. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.