International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.12(2) (2021)

References:

  1. Azizi, R., Sedghi, H., Shoja, H., & Sepas-Moghaddam, A. (2015). A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm. arXiv e-prints, arXiv-1506.
  2. Chu, X. L. et al. (2010). Method of image segmentation based on Fuzzy C-Means Clustering Algorithm and Artificial Fish Swarm Algorithm. Proc. 2010 International Conference on Intelligent Computing and Integrated Systems, ICISS2010, pp. 254-257. DOI: 10.1109/ICISS.2010.5657199.
  3. Dong, H., Zhang, K., & Zhu, L. (2012). An algorithm of 3D directional sensor network coverage enhancing based on artificial fish-swarm optimization. In The 2012 International Workshop on Microwave and Millimeter Wave Circuits and System Technology (pp. 1-4). IEEE.
  4. Dressler, F. and Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks. 54(6), pp. 881-900. DOI: 10.1016/j.comnet.2009.10.024.
  5. Engmann, F. et al. (2018). Prolonging the Lifetime of Wireless Sensor Networks: A Review of Current Techniques. Wireless Communications and Mobile Computing. DOI: 10.1155/2018/8035065.
  6. Farzi, S. (2009). Efficient Job Scheduling in Grid Computing with Modified Artificial Fish Swarm Algorithm. International Journal of Computer Theory and Engineering, 1(1), pp. 13-18. DOI: 10.7763/ijcte.2009.v1.3.
  7. Fonseca, C. M. and Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 28(1), pp. 26-37. DOI: 10.1109/3468.650319.
  8. Gui, T. et al. (2016). A Novel Cluster-based Routing Protocol Wireless Sensor Networks using Spider Monkey Optimization. pp. 5657-5662.
  9. He, S., Moncton, D. and Moncton, D. (2009). Fuzzy Clustering with Improved Artificial Fish Swarm Algorithm. DOI: 10.1109/CSO.2009.367.
  10. Helmy, A. O., Ahmed, S. and Hassenian, A. E. (2015) Artificial Fish Swarm Algorithm for Energy-Efficient Routing Technique. pp. 509-519. DOI: 10.1007/978-3-319-11313-5.
  11. Iyengar, S. S., Wu, H. C., Balakrishnan, N., & Chang, S. Y. (2007). Biologically inspired cooperative routing for wireless mobile sensor networks. IEEE Systems Journal, 1(1), 29-37.
  12. Krishnanand, K. N. and Ghose, D. (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2), pp. 87-124. DOI: 10.1007/s11721-008-0021-5.
  13. Li, X. (2010). A clustering algorithm based on artificial fish school. ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings, 7(2), pp. 766-769. DOI: 10.1109/ICCET.2010.5485745.
  14. Liu, C. B. et al. (2009). QoS multicast routing problem based on artificial fish-swarm algorithm. Proceedings of the 1st International Workshop on Education Technology and Computer Science, ETCS 2009, 2, pp. 814-817. DOI: 10.1109/ETCS.2009.443.
  15. Mishra, B. S. P., Dehuri, S. and Cho, S. B. (2015). Multi-objective Swarm Intelligence. Studies in Computational Intelligence, 592, pp. 27-73. DOI: 10.1007/978-3-662-46309-3.
  16. Neshat, M. et al. (2012). A review of Artificial Fish Swarm Optimization methods and applications. International Journal on Smart Sensing and Intelligent Systems, 5(1), pp. 107-148. DOI: 10.21307/ijssis-2017-474.
  17. Neshat, M., Adeli, A. and Sargolzaei, M. (2017). A review of artificial fish swarm optimization. DOI: 10.21307/ijssis-2017-474.
  18. Ranganathan, P. et al. (2006). Discovering adaptive heuristics for ad-hoc sensor networks by mining evolved optimal configurations. 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 3064-3070.
  19. Rushdy, E., Attia, M. and Abdalla, M. I. (2018). EnergyAware Optimized Hierarchical Routing. pp. 614-623. DOI: 10.1007/978-3-319-74690-6.
  20. Selvakennedy, S., Sinnappan, S. and Shang, Y. (2007). A biologically-inspired clustering protocol for wireless sensor networks. Computer Communications, 30(14-15), pp. 2786-2801. DOI: 10.1016/j.comcom.2007.05.010.
  21. Shen, W., Guo, X., Wu, C., & Wu, D. (2011). Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Systems, 24(3), 378-385.
  22. Song, X., Wang, C., Wang, J., & Zhang, B. (2010). A hierarchical routing protocol based on AFSO algorithm for WSN. In 2010 International Conference On Computer Design and Applications. Vol. 2, pp. 635, IEEE.
  23. Tian, W. and Liu, J. (2009). An improved artificial fish swarm algorithm for multi-robot task scheduling. 5th International Conference on Natural Computation, ICNC 2009, 4(2), pp. 127-130. DOI: 10.1109/ICNC.2009.795.
  24. Tian, W. J. et al. (2009). A new optimization algorithm for fuzzy set design. 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009, 2, pp. 431-435. DOI: 10.1109/IHMSC.2009.230.
  25. Yang, T. and Yong, T. (2006). Short Life Artificial Fish Swarm Algorithm for Wireless Sensor Network. pp. 378-381.
  26. Zhang, Y. L. et al. (2011). A modified glowworm swarm optimization for multimodal functions. Proceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011, pp. 2070-2075. DOI: 10.1109/CCDC.2011.5968545.


Other issues:
Vol.12(2)(2021)
Vol.12(1)(2021)
Vol.11(16)(2020)
Vol.11(15)(2020)
Vol.11(14)(2020)
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