Fully informed artificial bee colony algorithm

References

• Akay, B., & Karaboga, D. (2012). A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 192, 120–142. doi:10.1016/j.ins.2010.07.015.
   Web of Science ®Google Scholar
• Banharnsakun, A., Achalakul, T., & Sirinaovakul, B. (2011). The best-so-far selection in artificial bee colony algorithm. Applied Soft Computing, 11, 2888–2901. doi:10.1016/j.asoc.2010.11.025.
   Web of Science ®Google Scholar
• Bansal, J. C., & Sharma, H. (2012). Cognitive learning in differential evolution and its application to model order reduction problem for single-input single-output systems. Memetic Computing, 4, 209–229.
   Web of Science ®Google Scholar
• Bansal, J. C., Sharma, H., Arya, K. V., Deep, K., & Pant, M. (2014). Self-adaptive artificial bee colony. Optimization, 63, 1513–1532. doi:10.1080/02331934.2014.917302.
   Web of Science ®Google Scholar
• Bansal, J. C., Sharma, H., Arya, K. V., & Nagar, A. (2013). Memetic search in artificial bee colony algorithm. Soft Computing, 17, 1911–1928. doi:10.1007/s00500-013-1032-8.
   Web of Science ®Google Scholar
• Bansal, J. C., Sharma, H., Nagar, A., & Arya, K. V. (2013). Balanced artificial bee colony algorithm. International Journal of Artificial Intelligence and Soft Computing, 3, 222–243. doi:10.1504/IJAISC.2013.053392.
   Google Scholar
• Deep, K., & Thakur, M. (2007). A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation, 188, 895–911. doi:10.1016/j.amc.2006.10.047.
   Web of Science ®Google Scholar
• Diwold, K., Aderhold, A., Scheidler, A., & Middendorf, M. (2011). Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Computing, 1(1), 1–14.
   Google Scholar
• Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proc. of CEC 99 – the Congress on Evolutionary Computation (Vol. 2). Washington, DC: IEEE.
   Google Scholar
• El-Abd, M. (2012). Performance assessment of foraging algorithms vs. evolutionary algorithms. Information Sciences, 182, 243–263. doi:10.1016/j.ins.2011.09.005.
   Web of Science ®Google Scholar
• Gao, W., & Liu, S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39, 687–697. doi:10.1016/j.cor.2011.06.007.
   Web of Science ®Google Scholar
• Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization  (Technical Report TR06). Erciyes: Erciyes Universtiy Press.
   Google Scholar
• Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214, 108–132. doi:10.1016/j.amc.2009.03.090.
   Web of Science ®Google Scholar
• Karaboga, D., & Akay, B. (2011). A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11, 3021–3031. doi:10.1016/j.asoc.2010.12.001.
   Web of Science ®Google Scholar
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proc. of IEEE International Conference on Neural Networks, Proceedings (Vol. 4, pp. 1942–1948). Perth, WA: IEEE.
   Google Scholar
• Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 18, 50–60. doi:10.1214/aoms/1177730491.
   Google Scholar
• Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation, 8, 204–210. doi:10.1109/TEVC.2004.826074.
   Web of Science ®Google Scholar
• Price, K. V., Storn, R. M., & Lampinen, J. A. (2005). Differential evolution: A practical approach to global optimization. Springer Verlag.
   Google Scholar
• Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, 12, 64–79. doi:10.1109/TEVC.2007.894200.
   Web of Science ®Google Scholar
• Sharma, H., Bansal, J., & Arya, K. (2012). Dynamic scaling factor based differential evolution algorithm. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), December 20–22, 2011 (pp. 73–85). Springer.
   Google Scholar
• Sharma, H., Bansal, J. C., & Arya, K. V. (2013). Opposition based lévy flight artificial bee colony. Memetic Computing, 5, 213–227. doi:10.1007/s12293-012-0104-0.
   Web of Science ®Google Scholar
• Sharma, H., Bansal, J. C., & Arya, K. V. (2014). Power law-based local search in artificial bee colony. International Journal of Artificial Intelligence and Soft Computing, 4, 164–194. doi:10.1504/IJAISC.2014.062814.
   Google Scholar
• Vesterstrom, J., & Thomsen, R. (2004). A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In Proc. of Congress on Evolutionary Computation (CEC) (Vol. Vol. 2, pp. 1980–1987). IEEE.
   Google Scholar
• Williamson, D. F., Parker, R. A., & Kendrick, J. S. (1989). The box plot: A simple visual method to interpret data. Annals of Internal Medicine, 110, 916. doi:10.7326/0003-4819-110-11-916.
   PubMed Web of Science ®Google Scholar
• Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217, 3166–3173. doi:10.1016/j.amc.2010.08.049.
   Web of Science ®Google Scholar