Open Access
MATEC Web Conf.
Volume 200, 2018
International Workshop on Transportation and Supply Chain Engineering (IWTSCE’18)
Article Number 00005
Number of page(s) 4
Published online 14 September 2018
  1. J. Kennedy, R. Eberhart, Particle Swarm Optimization, Proc. IEEE Int. Conf. Neural Networks, 1942–1948 (1995) [CrossRef] [Google Scholar]
  2. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equations of state calculations by fast computing machines, J. of Chemical Physics 21(6), 1087–1091 (1953) [CrossRef] [Google Scholar]
  3. S. Kirpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Sc. 220, 671–680 (1983) [Google Scholar]
  4. A.A. Kannan, G. Mao, B. Vucetic, Simulated annealing based localization in wireless sensor network, Proc. IEEE Conf. LCN’05, 513–514 (2005) [Google Scholar]
  5. J. Moré, Z. Wu, Global continuation for distance geometry problems, SIAM J. Opt. 7, 814–836 (1997) [CrossRef] [Google Scholar]
  6. G.F. Nan, M.Q. Li, J. Li, Estimation of node localization with a real-coded genetic algorithm in WSNs, Proc. Int. Conf. on Machine Learning and Cybernetics 2, 873–878 (2007) [CrossRef] [Google Scholar]
  7. R.C. Abreu, J.E.C. Arroyo, A Particle Swarm Optimization Algorithm for topology Control in Wireless Sensor Networks, Inter. Conf. Chilean (2011) [Google Scholar]
  8. J.J. Gnana Chandran, S.P. Victor, An Energy Efficient Localization Technique Using Particle Swarm Optimization in Mobile Wireless Sensor Networks, American J. of Sc. Res., 33–48 (2010) [Google Scholar]
  9. R.V. Kulkarni, G.K. Venayagamoorthy, A. Miller, C.H. Dagli, Network-centric Localization in MANETs based on Particle Swarm Optimization IEEE Swarm Intelligence Symp., 1–6(2008) [Google Scholar]
  10. J. Aspnes, W. Whiteley, Y.R. Yang, IEEE Trans. mobile computing, A theory of network localization, 5(12), 1663–1678 (2006) [Google Scholar]
  11. P. Biswas, Y. Ye, Proc. 3rd Int. Symp. on Information Processing in Sensor Networks, 46–54 (2004) [Google Scholar]
  12. T.-C. Liang, T.-C. Wang, Y. Ye, A gradient search method to round the semidefinite programming relaxation solution for ad hoc wireless sensor network localization, Technical report, Stanford University (2004) [Google Scholar]
  13. H. Lakhbab, S. El Bernoussi, Int. J. of Math. Analysis 6, (2012) [Google Scholar]
  14. F. Javidrad, M. Nazari, A new hybrid particle swarm and simulated annealing stochastic optimization method, Appl. Soft. Comp. 60, 634–654 (2017) [CrossRef] [Google Scholar]
  15. M. Basu, P. Deb, G. Garai, Hybrid of Particle Swarm Optimization and Simulated Annealing for Multidimensional Function Optimization, Inter. J. Information Technology 20(1), 34–45 (2014) [Google Scholar]
  16. M. Bahrepour, E. Mahdipour, R. Cheloi, M. Yaghoobi, Super-sapso: a new sa-based pso algorithm, Appl. of Soft Comp. 58, 423–430 (2009) [CrossRef] [Google Scholar]
  17. G. Yang, D. Chen, G. Zhou, A new hybrid algorithm of particle swarm optimization, Lecture Notes in Comp. Sc. 4115, 50–60 (2006) [CrossRef] [Google Scholar]
  18. N. Sadati, M. Zamani, H. Mahdavian, Hybrid particle swarm-based simulated annealing optimization techniques Proc. IEEE Industrial Electronics Conf. 644–648 (2006) [Google Scholar]

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