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                "abstract": {
                    "value": ": Wireless Sensor Networks (WSNs) are increasingly realizing applications in IoT, smart grids, healthcare, security, swarm robotics, etc. Swarm Intelligence is used to optimize performance parameters associated with WSNs including localization, coverage, network lifetime, energy efficiency to name a few. The scope of this paper is restricted to a survey on Stochastic Diffusion Search, Genetic Algorithm and Particle Swarm Algorithm with respect to their organization and capacity to optimize these network parameters and their current as well as potential applications in WSNs.",
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                    {
                        "key": "ref1",
                        "unstructured": "D. E. Goldberg, Genetic Algorithm in a Search Optimization and Machine Learning, Addison Wesley, 1989"
                    },
                    {
                        "key": "ref2",
                        "unstructured": "D. Goldberg, B. Karp, Y. Ke, S. Nath, and S. Seshan, Genetic algorithms in search, optimization, and machine learning. Ad disonWesley, 1989"
                    },
                    {
                        "key": "ref3",
                        "doi": "10.4236/wsn.2011.310038",
                        "unstructured": "A. Norouzi, F. S. Babamir, and A. H. Zaim, “A novel energy efficient routing protocol in wireless sensor networks,” Journal o f Wireless Sensor Network,vol3.,no.10,pp.1–10,2011"
                    },
                    {
                        "key": "ref4",
                        "doi": "10.1007/978-3-642-24094-2_2",
                        "unstructured": "al-Rifaie M.M., Bishop J.M., Blackwell T. (2011) Resource Allocation and Dispensation Impact of Stochastic Diffusion Search on Differential Evolution Algorithm. In: Pelta D.A., Krasnogor N., Dumitrescu D., Chira C., Lung R. (eds) Nature Inspired Cooper ative Strategies for Optimization (NICSO 2011). Studies in Com putational Intelligence, vol 387. Springer, Berlin, Heidelberg"
                    },
                    {
                        "key": "ref5",
                        "doi": "10.1049/el:20040096",
                        "unstructured": "Williams, H.; Bishop, M. Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC. Algorithms 2014, 7, 206-228. [6]D. R. Myatt, J. M. Bish op and S. J. Nasuto, \"Minimum stable convergence criteria for Stochastic Diffusion Search,\" in Electronics Letters, vol. 40, no. 2, pp. 112-113, 22 Jan. 2004.doi: 10.1049/el:20040096"
                    },
                    {
                        "key": "ref6",
                        "doi": "10.7232/iems.2012.11.3.215",
                        "unstructured": "Kennedy J. (2011) Particle Swarm Optimization. In: Sammut C., Webb G. I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. [8]Kachitvichyanukul, Voratas. (2012). Comparison of three evolutionary algorithms: GA, PSO, and DE. Industrial Engineering a nd Management Systems. 12. 215-223. 10.7232/iems.2012.11.3.215. [9]Eberhart, &Yuhui Shi. (n.d.). Particle swarm optimization: developments, applications and resources. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). doi:10.1109/cec.2001.934374 [10]Bishop, John & Torr, Philip. (1992). The Stochastic Search Network. 10.1007/978-94-011-2360-0_24"
                    },
                    {
                        "key": "ref7",
                        "doi": "10.11113/jt.v78.9743",
                        "unstructured": "Alhammadi, Abdulraqeb, Hashim, Fazirulhisyam, Fadlee, Mohd et al. (1 more author) (2016) An Adaptive Localization System Using Particle Swarm Optimization In A Circular Distribution Form. Jurnal Te knologi. pp. 105-110. [19]Ratnaweera, A., Halgamuge, S., and Watson, H. C. 2004. Self-organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation. 8(3): 240-255"
                    },
                    {
                        "key": "ref8",
                        "doi": "10.1007/s10462-009-9148-z",
                        "unstructured": "M. Al-Obaidy, A. Ayesh, and A. F. Sheta, “Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms,” Artificial Intelligence Review, vol. 29, no. 3, pp. 183–194, 2008"
                    },
                    {
                        "key": "ref9",
                        "doi": "10.1109/ccnc.2009.4784844",
                        "unstructured": "H.-S. Seo, S.-J. Oh, and C. -W. Lee, “Evolutionary genetic algorithm for efficient clustering of wireless sensor networks,” in Proceedings of the 6th IEEE Consumer Communications and Networking Conference, pp. 1–5, 2009"
                    },
                    {
                        "key": "ref10",
                        "unstructured": "] N. LingaRaj.K, Aradhana.D, “Multiple mobile agents in wireless sensor networks using genetic algorithms,” International Journal of Scientific and Engineering Research, vol. 3, no. 8, pp. 1–5, 2012"
                    },
                    {
                        "key": "ref11",
                        "doi": "10.1109/iraniancee.2012.6292445",
                        "unstructured": "M. Karimi, H. Naji, and S. Golestani, “Optimizing cluster-head selection in wireless sensor networks using genetic algorithm and har mony search algorithm,” in Proceedings of the 20th Iranian Conference on Electrical Engineering, pp. 706–710, 2012"
                    },
                    {
                        "key": "ref12",
                        "doi": "10.1145/508791.508902",
                        "unstructured": "al-Rifaie MM, Aber A, Raisys R (2011) Swarming robots and possible medical applications. In: International Society for the Elect ronic Arts (ISEA 2011), Istanbul, Turkey [25]Hurley, S., & Whitaker, R. M. (2002). An agent based approach to site selection for wireless networks. Proceedings of the 2002 ACM Symposium on Applied Computing-SAC ’02"
                    },
                    {
                        "key": "ref13",
                        "unstructured": "Abhinav Dhiman, Nishant (2016). “Genetic Al gorithm for localization in WSN”. 10.15680/IJIRCCE.2016. 0407013"
                    }
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