Abstract
BCSA has constantly attracted the interest of investigators from diverse disciplines worldwide since its introduction in 2009. This interest has led to various hybridizations for improving the performance of the basic BCSA. These hybridizations can improve MCSA and achieve favorable results.
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References
Abdel-Baset, M., & Hezam, I. M. (2016b). Solving linear least squares problems based on improved cuckoo search algorithm. Mathematical sciencese, 5(2), 199–202.
Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018b). Hybrid clustering analysis using improved krill herd algorithm. Applied Intelligence, pp. 1–25.
Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018d). A novel weighting scheme applied to improve the text document clustering techniques. In Innovative Computing, Optimization and Its Applications, pp. 305–320. Springer.
Abualigah, L. M., Sawaie, A. M., Khader, A. T., Rashaideh, H., Al-Betar, M. A., & Shehab, M. (2017b). \(\beta \)-hill climbing technique for the text document clustering. New Trends in Information Technology, 60.
Alajmi, B. N., Ahmed, K. H., Finney, S. J., & Williams, B. W. (2011). Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Transactions on Power Electronics, 26(4), 1022–1030.
Ardjani, F., Sadouni, K., & Benyettou, M. (2010). Optimization of svm multiclass by particle swarm (pso-svm). In 2010 2nd International Workshop on Database Technology and Applications, pp. 1–4. IEEE.
Burke, E. K., & Newall, J. P. (2002). Enhancing timetable solutions with local search methods. In International Conference on the Practice and Theory of Automated Timetabling, pp. 195–206. Springer.
Cho, K.-H., Yeh, C.-H., Tournier, J.-D., Chao, Y.-P., Chen, J.-H., & Lin, C.-P. (2008). Evaluation of the accuracy and angular resolution of q-ball imaging. Neuroimage, 42(1), 262–271.
Dejam, S., Sadeghzadeh, M., & Mirabedini, S. J. (2012). Combining cuckoo and tabu algorithms for solving quadratic assignment problems. Journal of Academic and Applied Studies, 2(12), 1–8.
Jamil, M., & Yang, X.-S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150–194.
Jiao, J., & Long, W. (2014). Hybrid cuckoo search algorithm based on powell search for constrained engineering design optimization13, 431–440.
Layeb, A. (2011). A novel quantum inspired cuckoo search for knapsack problems. International Journal of bio-inspired Computation, 3(5), 297–305.
Li, Z., Zhou, Y., Zhang, S., & Song, J. (2016). Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Mathematical Problems in Engineering.
Li, X., Wang, J., & Yin, M. (2014). Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Computing and Applications, 24(6), 1233–1247.
Liu, X., & Hui, F. (2014). Pso-based support vector machine with cuckoo search technique for clinical disease diagnoses. The Scientific World Journal.
Pavlyukevich, I. (2007). Lévy flights, non-local search and simulated annealing. Journal of Computational Physics, 226(2), 1830–1844.
Reyaz-Ahmed, A., Zhang, Y.-Q., & Harrison, R. W. (2009). Granular decision tree and evolutionary neural svm for protein secondary structure prediction. International Journal of Computational Intelligence Systems, 2(4), 343–352.
Rubio, A., & Gámez, J. A. (2011). Flexible learning of k-dependence bayesian network classifiers. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 1219–1226. ACM.
Schaerf, A., & Meisels, A. (1999). Solving employee timetabling problems by generalized local search. In Congress of the Italian Association for Artificial Intelligence, pp. 380–389. Springer.
Shehab, M., & Khader, A. T. (2018). Modified cuckoo search algorithm using a new selection scheme for unconstrained optimization problems, 14, 1.
Shehab, M., Daoud, M. Sh., AlMimi, H. M., Abualigah, L. M., & Khader, A. T. (2019a). Hybridizing cuckoo search algorithm for extracting the odf maxima in spherical harmonic representation. International Journal of Bio-Inspired Computation, (in press).
Shehab, M., Khader, A. T., & Al-Betar, M. A. (2016). New selection schemes for particle swarm optimization. IEEJ Transactions on Electronics, Information and Systems, 136(12), 1706–1711. https://doi.org/10.1541/ieejeiss.136.1706.
Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017a). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing.
Shehab, M., Khader, A. T., & Alia, M. A. (2019b). Enhancing cuckoo search algorithm by using reinforcement learning for constrained engineering optimization problems. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 812–816. IEEE.
Shehab, M., Khader, A. T., & Laouchedi, M. (2017c). Modified cuckoo search algorithm for solving global optimization problems. In International Conference of Reliable Information and Communication Technology, pp. 561–570. Springer.
Shehab, M., Khader, A. T., & Laouchedi, M. (2018a). A hybrid method based on cuckoo search algorithm for global optimization problems. Journal of ICT, 17(3), 469–491.
Shehab, M., Khader, A. T., Al-Betar, M. A., & Abualigah, L. M. (2017b). Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In Information Technology (ICIT), 2017 8th International Conference on, pp. 36–43. IEEE.
Shehab, M., Khader, A. T., Laouchedi, M., & Alomari, O. A. (2018b). Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. The Journal of Supercomputing, 1–28.
Sheikholeslami, R., Zecchin, A. C., Zheng, F., & Talatahari, S. (2016). A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems. Journal of Hydroinformatics, 18(3), 544–563.
Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713.
Walton, S., Hassan, O., Morgan, K., & Brown, M. R. (2011b). Modified cuckoo search: a new gradient free optimisation algorithm. Chaos, Solitons & Fractals, 44(9), 710–718.
Wang, G.-G., Gandomi, A. H., Zhao, X., & Chu, H. C. E. (2016b). Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Computing, 20(1), 273–285.
Yang, X.-S. (2010b). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer.
Yang, X.-S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616–1624.
Yang, X.-S., & He, X. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.
Yılmaz, S., & U Küçüksille, E. (2015). A new modification approach on bat algorithm for solving optimization problems. Applied Soft Computing, 28, 259–275.
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Shehab, M. (2020). Hybridization Cuckoo Search Algorithm for Extracting the ODF Maxima. In: Artificial Intelligence in Diffusion MRI. Studies in Computational Intelligence, vol 877. Springer, Cham. https://doi.org/10.1007/978-3-030-36083-2_7
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