A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

References

• Anderson, T. R. (2006). Biology of the ubiquitous house sparrow: From genes to populations. Oxford: Oxford University Press.
   Google Scholar
• Barnard, C. J., & Sibly, R. M. (1981). Producers and scroungers: A general model and its application to captive flocks of house sparrows. Animal Behavior, 29, 543–550. doi:10.1016/S0003-3472(81)80117-0.
   Web of Science ®Google Scholar
• Barta, Z., & Giraldeau, L. A. (2000). Daily patterns of optimal producer and scrounger use under predation hazard: A state-dependent dynamic game analysis. American Naturalist, 155, 570–582. doi:10.1086/303342.
   PubMed Web of Science ®Google Scholar
• Beauchamp, G. (1998). The effect of group size on mean food intake rate in birds. Biological Reviews, 73, 449–472. doi:10.1017/S0006323198005246.
   Web of Science ®Google Scholar
• Beauchamp, G. (2003). Group-size effects on vigilance: A search for mechanisms. Behavioral Processes, 63, 111–121. doi:10.1016/S0376-6357(03)00002-0.
   PubMed Web of Science ®Google Scholar
• Bednekoff, B. A., & Lima, S. L. (1998). Randomness, chaos and confusion in the study of antipredator vigilance. Trends in Ecology and Evolution, 13, 284–287. doi:10.1016/S0169-5347(98)01327-5.
   PubMed Web of Science ®Google Scholar
• Coolen, I., Giraldeau, L. A., & Lavoie, M. (2001). Head position as an indicator of producer and scrounger tactics in a ground-feeding bird. Animal Behavior, 61, 895–903. doi:10.1006/anbe.2000.1678.
   Web of Science ®Google Scholar
• Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15, 4–31. doi:10.1109/TEVC.2010.2059031.
   Web of Science ®Google Scholar
• Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.
   Google Scholar
• Ekman, J. (1987). Exposure and time use in willow tit flocks: The cost of subordination. Animal Behavior, 35, 445–452. doi:10.1016/S0003-3472(87)80269-5.
   Web of Science ®Google Scholar
• Elgar, M. A., & Catterall, C. P. (1981). Flocking and predator surveillance in house sparrows: Test of an hypothesis. Animal Behavior, 29, 868–872. doi:10.1016/S0003-3472(81)80022-X.
   Web of Science ®Google Scholar
• Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science Numerical Simulation, 17, 4831–4845. doi:10.1016/j.cnsns.2012.05.010.
   Web of Science ®Google Scholar
• Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. L. (2010). A knowledge-based artificial fish-swarm algorithm. In 13th IEEE international conference on computational science and engineering (pp. 327–332). Hong Kong: IEEE Computer Society.
   Google Scholar
• Giraldeau, L. A., & Caraco, T. (2000). Social foraging theory. Princeton, NJ: Princeton University Press.
   Google Scholar
• Jin, X. D. (2001). Solving constrained optimization problems using cultural algorithm and regional schemata  (Doctorate dissertation). Detroit, MI: Wayne State University.
   Google Scholar
• Johnson, C. A., Giraldeau, L. A., & Grant, J. W. A. (2001). The effect of handling time on interference among house sparrows foraging at different seed densities. Behavior, 138, 597–614. doi:10.1163/156853901316924494.
   Web of Science ®Google Scholar
• Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471. doi:10.1007/s10898-007-9149-x.
   Web of Science ®Google Scholar
• Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: Charged system search. Acta Mechanica, 213, 267–289. doi:10.1007/s00707-009-0270-4.
   Web of Science ®Google Scholar
• Kennedy, J., Eberhart, R. C., & Shi, Y. H. (2001). Swarm intelligence. Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Koops, M. A., & Giraldeau, L. A. (1996). Producer–scrounger foraging games in starlings: A test of rate-maximizing and risk-sensitive models. Animal Behavior, 51, 773–783. doi:10.1006/anbe.1996.0082.
   Web of Science ®Google Scholar
• Krause, J., & Ruxton, G. D. (2002). Living in groups. Oxford: Oxford University Press.
   Google Scholar
• Krishnanand, K. N., & Ghose, D. (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3, 87–124. doi:10.1007/s11721-008-0021-5.
   Google Scholar
• Kuo, H. C., & Lin, C. H. (2013). A directed Genetic Algorithm for global optimization. Applied Mathematics and Computation, 219, 7348–7364. doi:10.1016/j.amc.2012.12.046.
   Web of Science ®Google Scholar
• Liker, A., & Barta, Z. (2002). The effects of dominance on social foraging tactic use in house sparrows. Behavior, 139, 1061–1076. doi:10.1163/15685390260337903.
   Web of Science ®Google Scholar
• Lima, S. L., & Dill, L. M. (1990). Behavioral decisions made under the risk of predation: A review and prospectus. Canadian Journal of Zoology, 68, 619–640. doi:10.1139/z90-092.
   Web of Science ®Google Scholar
• Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M. N., Salcedo-Sanz, S., & Geem, Z. W. (2013). A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26, 1818–1831. doi:10.1016/j.engappai.2013.05.008.
   Web of Science ®Google Scholar
• Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1, 355–366. doi:10.1016/j.ecoinf.2006.07.003.
   Web of Science ®Google Scholar
• Pulliam, H. R. (1973). On the advantages of flocking. Journal of Theoretical Biology, 38, 419–422. doi:10.1016/0022-5193(73)90184-7.
   PubMed Web of Science ®Google Scholar
• Pulliam, H. R., Pyke, G. H., Caraco, T., & Pulliam, H. R. (1982). The scanning behavior of juncos: A game-theoretical approach. Journal of Theoretical Biology, 95, 89–103. doi:10.1016/0022-5193(82)90289-2.
   Web of Science ®Google Scholar
• Rezaee Jordehi, A. R., & Jasni, J. (2013). Parameter selection in particle swarm optimisation: A survey. Journal of Experimental & Theoretical Artificial Intelligence, 25, 527–542. doi:10.1080/0952813X.2013.782348.
   Web of Science ®Google Scholar
• Roberts, G. (2003). The group-size effect in non-feeding animals. Behavioral Processes, 63, 127–128. doi:10.1016/S0376-6357(03)00005-6.
   PubMed Web of Science ®Google Scholar
• Shi, Y. H. (2011). Brain storm optimization algorithm. In Proceedings of 2th International Conference on Swarm Intelligence (pp. 303–309). Chongqing: IEEE Computational Intelligence Society.
   Google Scholar
• Sirot, E. (2006). Social information, antipredatory vigilance and flight in bird flocks. Animal behavior, 72, 373–382. doi:10.1016/j.anbehav.2005.10.028.
   Web of Science ®Google Scholar
• Smith, J. E. (2007). Coevolving Memetic Algorithms: A review and progress report. IEEE Transaction on System, Man, and Cybernetics – Part B: Cybernetics, 37, 6–17.
   PubMed Web of Science ®Google Scholar
• Sullivan, K. A. (1984). The advantages of social foraging in downy woodpeckers. Animal Behavior, 32, 16–22. doi:10.1016/S0003-3472(84)80319-X.
   Web of Science ®Google Scholar
• Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Studies in Computational Intelligence, 284, 65–74.
   Google Scholar
• Yang, X. S. (2014). Nature-inspired optimization algorithm. London: Elsevier.
   Google Scholar
• Yang, X. S., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing & Applications, 24, 169–174. doi:10.1007/s00521-013-1367-1.
   Web of Science ®Google Scholar
• Yang, X. S., Karamanoglu, M., & He, X. S. (2014). Flower pollination algorithm: A novel approach for multiobjective optimization. Engineering Optimization, 46, 1222–1237. doi:10.1080/0305215X.2013.832237.
   Web of Science ®Google Scholar