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Hybridization Cuckoo Search Algorithm for Extracting the ODF Maxima

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Artificial Intelligence in Diffusion MRI

Part of the book series: Studies in Computational Intelligence ((SCI,volume 877))

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.

    Google Scholar 

  • Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018b). Hybrid clustering analysis using improved krill herd algorithm. Applied Intelligence, pp. 1–25.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jiao, J., & Long, W. (2014). Hybrid cuckoo search algorithm based on powell search for constrained engineering design optimization13, 431–440.

    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 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Liu, X., & Hui, F. (2014). Pso-based support vector machine with cuckoo search technique for clinical disease diagnoses. The Scientific World Journal.

    Google Scholar 

  • Pavlyukevich, I. (2007). Lévy flights, non-local search and simulated annealing. Journal of Computational Physics, 226(2), 1830–1844.

    Article  MathSciNet  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Shehab, M., & Khader, A. T. (2018). Modified cuckoo search algorithm using a new selection scheme for unconstrained optimization problems, 14, 1.

    Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017a). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Yang, X.-S. (2010b). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer.

    Google Scholar 

  • Yang, X.-S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616–1624.

    Article  MathSciNet  Google Scholar 

  • Yang, X.-S., & He, X. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.

    Article  Google Scholar 

  • Yılmaz, S., & U Küçüksille, E. (2015). A new modification approach on bat algorithm for solving optimization problems. Applied Soft Computing, 28, 259–275.

    Google Scholar 

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Correspondence to Mohammad Shehab .

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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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