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Swarm Intelligence and Social Insects

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Mapping Biological Systems to Network Systems

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Abstract

Swarm intelligence is a behavior shown by a collection of social insects and animals which exhibit spatial arrangement and synchronized motion. These animals control and manage their position with the help of local interactions among companions of the same species. The work is performed in a systematic manner such that exchange of information occurs. The information which is gathered is shared with the other species. This ability benefits them in many aspects of social life, such as the need to protect themselves from predators and to perform well-organized locomotion and foraging (Nicole in Fish, networks, and synchronization 199:3518–3562, 2012).

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References

  • Abharian, A., & Shakeri, E. (2011). Bacteria foraging optimization Robust-RED for AQM/TCP network. International Journal of Modeling and Optimization, 1(1), 49.

    Article  Google Scholar 

  • Al-Hadi, I. A. A., Hashim, S. Z. M., & Shamsuddin, S. M. H. (2011). Bacterial foraging optimization algorithm for neural network learning enhancement. In 11th International conference on hybrid intelligent systems (HIS) (pp. 200–205).

    Google Scholar 

  • Baguda, Y. S., Fisal, N., Rashid, R. A., Yusof, S. K., Syed, S. H., & Shuaibu, D. S. (2012). Biologically-inspired optimal video streaming over unpredictable wireless channel. International Journal of Future Generation Communication and Networking.

    Google Scholar 

  • Beckers, R., Holland, O. E., & Deneubourg, J. L. (2000). From local actions to global tasks: Stigmergy and collective robotics. In Studies in cognitive systems (Vol. 26, pp. 1008–1022).

    Google Scholar 

  • Caro, G. D., & Dorigo, M. (1997). AntNet: A mobile agents approach to adaptive routing. Technical report 97–12, IRIDIA, Universite’ Libre de Bruxelles.

    Google Scholar 

  • Caro, G. D., Ducatelle, F., & Gambardella, L. M. (2005). AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, 16, 443–455.

    Article  Google Scholar 

  • Chen, Z., Li, S., Yue, W., Hu, L., & Sun, W. (2012, November). Bacterial foraging optimization algorithm based routing strategy for wireless sensor networks. International Review on Computers and Software, 7(6), 2826–2830.

    Google Scholar 

  • Ciimara, D., & Loureiro, A. A. F. (2001). A GPS/Ant-Like routing algorithm for ad hoc networks. Telecommunication Systems, 18(1–3), 85–100.

    Google Scholar 

  • Civicioglu, P., & Besdok, E. (2013). A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39(4), 315–346.

    Article  Google Scholar 

  • Das, S., Biswas, A., Dasgupta, S., & Abraham, A. (2009a). Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. Foundations of Computational Intelligence (Vol. 3, pp. 23–55). Berlin Heidelberg: Springer.

    Google Scholar 

  • Das, S., Dasgupta, S., Biswas, A., Abraham, A., & Konar, A. (2009b). On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(3), 670–679.

    Article  Google Scholar 

  • Dhivya, M., Sundarambal, M., & Anand, L. N. (2011). Energy efficient computation of data fusion in wireless sensor networks using cuckoo based particle approach (CBPA). International Journal of Communications, Network and System Sciences, 4(4), 249.

    Google Scholar 

  • Dhivya, M., & Sundarambal, M. (2011). Cuckoo search for data gathering in wireless sensor networks. International Journal of Mobile Communications, 9(6), 642–656.

    Article  Google Scholar 

  • Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.

    Article  Google Scholar 

  • Great Smoky Mountains. (2012). Retrieved January 2012, from http://www.nps.gov/grsm/naturescience/fireflies.htm.

  • Gunes, M., Sorges, U., & Bouazizi, I. (2002). ARA-the ant-colony based routing algorithm for MANETs. In Proceedings of the International Conference on Parallel Processing Workshops (pp. 79–85).

    Google Scholar 

  • Kim, D. H., Abraham, A., & Cho, J. H. (2007). A hybrid genetic algorithm and bacterial foraging approach for global optimization. Information Sciences, 177(18), 3918–3937.

    Article  Google Scholar 

  • Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(2), 262–267.

    Article  Google Scholar 

  • Layeb, A. (2011). A novel quantum inspired cuckoo search for knapsack problems. International Journal of Bio-Inspired Computation, 3(5), 297–305.

    Article  Google Scholar 

  • Lee, G., & Chong, N. Y. (2008). Flocking controls for swarms of mobile robots inspired by fish schools. In Recent Advances in Multi Robot Systems.

    Google Scholar 

  • Leibnitz, K., Wakamiya, N., & Murata, M. (2006). Biologically inspired self-adaptive multipath routing in overlay networks. Communications of the ACM, 49(3), 63–67.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  • Nicole, A. (2012). Fish, networks, and synchronization. Ph.D. (Vol. 199, pp. 3518–3562).

    Google Scholar 

  • Ouaarab, A., Ahiod, B., & Yang, X. S. (2014). Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications, 24(7–8), 1659–1669.

    Article  Google Scholar 

  • Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52–67.

    Article  MathSciNet  Google Scholar 

  • Payne, R. B., Sorenon, M. D., & Klitz, K. (2005). The cuckoos. Oxford: Oxford University Press.

    Google Scholar 

  • Perretto, M., & Lopes, H. S. (2005). Reconstruction of phylogenetic trees using the ant colony optimization paradigm. Genetics and Molecular Research, 4(3), 581–589.

    Google Scholar 

  • Shen, H., Zhu, Y., Zhou, X., Guo, H., & Chang, C. (2009). Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization. In Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (pp. 497–504). New York: ACM.

    Google Scholar 

  • Tatar, N., & Holban, S. (2012). A Bio Inspired Alternative to Huffman Coding. Suceava, Romania: Proceeding of Development and Application Systems.

    Google Scholar 

  • Tyrrell, A., Auer, G., & Bettstetter, C. (2006). Fireflies as role models for synchronization in ad hoc networks. In Proceedings of the International Conference on Bio Inspired Models of Network, Information and Computing Systems (pp. 4.

    Google Scholar 

  • Valian, E., Mohanna, S., & Tavakoli, S. (2011). Improved cuckoo search algorithm for feedforward neural network training. International Journal of Artificial Intelligence and Applications, 2(3), 36–43.

    Article  Google Scholar 

  • Valian, E., Tavakoli, S., Mohanna, S., & Haghi, A. (2013). Improved cuckoo search for reliability optimization problems. Computers and Industrial Engineering, 64(1), 459–468.

    Article  Google Scholar 

  • Wedde, H. F., Farooq, M., & Zhang, Y. (2004). Beehive: An efficient fault- tolerant routing algorithm inspired by honey bee behavior. Ant Colony, Optimization, and Swarm Intelligence, 3172, 8394.

    Google Scholar 

  • Wedde, H.F. et al. (2005). BeeAdHoc: An energy efficient routing algorithm for mobile ad-hoc networks inspired by bee behavior. In Proceedings of the GECCO (pp.153–160). New York: ACM.

    Google Scholar 

  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization. Studies in Computational Intelligence (pp. 65–74).

    Google Scholar 

  • Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In World Congress on Nature and Biologically Inspired Computing, NaBIC (pp. 210–214).

    Google Scholar 

  • Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330–343.

    Article  MATH  Google Scholar 

  • Yang, X. S., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing and Applications, 24(1), 169–174.

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology, Jodhpur, India

    Heena Rathore

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  1. Heena Rathore
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Correspondence to Heena Rathore .

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Rathore, H. (2016). Swarm Intelligence and Social Insects. In: Mapping Biological Systems to Network Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29782-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-29782-8_4

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  • Publisher Name: Springer, Cham

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