Page 286 - Big Data Analytics for Intelligent Healthcare Management
P. 286
280 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING
REFERENCES
[1] R. Hedayatzadeh, F.A. Salmassi, M. Keshtgari, R. Akbari, K. Ziarati, Termite colony optimization: a novel
approach for optimizing continuous problems, in: 2010 18th Iranian Conference on Electrical Engineering
(ICEE), IEEE, 2010, pp. 553–558.
[2] M.M. Eusuff, K.E. Lansey, Optimization of water distribution network design using the shuffle frog leaping
algorithm, J. Water Resour. Plan. Manag. 129 (3) (2003) 210–225 cited By (since 1996) 297.
[3] S.H. Jung, Queen-bee evolution for genetic algorithms, Electron. Lett. 39 (6) (2003) 575–576.
[4] T.C. Havens, C.J. Spain, N.G. Salmon, J.M. Keller, Roach infestation optimization, in: Swarm Intelligence
Symposium, 2008. SIS 2008, IEEE, 2008, pp. 1–7.
[5] U. Premaratne, J. Samarabandu, T. Sidhu, A new biologically inspired optimization algorithm, in: 2009 In-
ternational Conference on Industrial and Information Systems (ICIIS), IEEE, 2009, pp. 279–284.
[6] H.A. Abbass, Mbo: marriage in honey bees optimization-a haplometrosis polygynous swarming approach,
in: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, IEEE, 2001, pp. 207–214.
[7] A. Reza Mehrabian, C. Lucas, A novel numerical optimization algorithm inspired from weed colonization,
Ecol. Inform. 1 (4) (2006) 355–366.
[8] L.M. Zhang, C. Dahlmann, Y. Zhang, Human-inspired algorithms continuous function optimization, in: ICIS
2009. IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009, vol. 1, IEEE,
2009, pp. 318–321.
[9] S. He, W. QH, J.R. Saunders, Group search optimizer: an optimization algorithm inspired by animal search-
ing behavior, IEEE Trans. Evol. Comput. 13 (5) (2009) 973–990.
[10] A. Mozaffari, A. Fathi, S. Behzadipour, The great salmon run: a novel bio–inspired algorithm for artificial
system design and optimisation, Int. J. Bio-Insp. Comput. 4 (5) (2012) 286–301.
[11] Candida Ferreira, Gene expression programming: a new adaptive algorithm for solving problems, arXiv
(2001) preprint cs/0102027.
[12] X.-S. Yang, Flower pollination algorithm for global optimization, in: Unconventional Computation and Nat-
ural Computation, 2012, pp. 240–249.
[13] X.-S. Yang, M. Karamanoglu, X. He, Multiobjective flower algorithm for optimization, Procedia Comput.
Sci. 18 (2013) 861–868.
[14] C.J.A.B. Filho, F.B.d.L. Neto, A.J.C.C. Lins, A.I.S. Nascimento, M.P. Lima, Fish school search, in: Nature-
Inspired Algorithms for Optimisation, Springer, 2009, pp. 261–277.
[15] F.B. de Lima Neto, A.J.C.C. Lins, A.I.S. Nascimento, M.P. Lima, et al., A novel search algorithm based on
fish school behavior, in: SMC 2008. IEEE International Conference on Systems, Man and Cybernetics, 2008,
IEEE, 2008, pp. 2646–2651.
[16] C. Sur, S. Sharma, A. Shukla, Egyptian vulture optimization algorithm–a new nature inspired metaheuristics
for knapsack problem, in: The 9th International Conference on Computing and Information Technology
(IC2IT2013), Springer, 2013, pp. 227–237.
[17] R.S. Parpinelli, H.S. Lopes, An eco-inspired evolutionary algorithm applied to numerical optimization,
in: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), 2011, IEEE, 2011,
pp. 466–471.
[18] H. Hugo, C. Blum, Distributed graph coloring: an approach based on the calling behavior of japanese tree
frogs, Swarm Intell. 6 (2) (2012) 117–150.
[19] A. Kaveh, N. Farhoudi, A new optimization method: Dolphin echolocation, Adv. Eng. Softw. 59 (2013)
53–70.
[20] Y. Shi, An optimization algorithm based on brainstorming process, Int. J. Swarm Intell. Res. 2 (4) (2011)
35–62.
[21] D. Simon, Biogeography-based optimization, IEEE Trans. Evol. Comput. 12 (6) (2008) 702–713.