A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection

References

• Alatas, B. 2010. Chaotic harmony search algorithms. Applied Mathematics and Computation 216:2687–99. doi:10.1016/j.amc.2010.03.114.
   Web of Science ®Google Scholar
• Alatas, B., E. Akin, and A. B. Ozer. 2009. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 40:1715–34. doi:10.1016/j.chaos.2007.09.063.
   Web of Science ®Google Scholar
• Altman, N. S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46:175–85.
   Web of Science ®Google Scholar
• Arora, S., and P. Anand. 2017. Chaos-enhanced flower pollination algorithms for global optimization. Journal of Intelligent & Fuzzy Systems 33:3853–69. doi:10.3233/JIFS-17708.
   Web of Science ®Google Scholar
• Arora, S., and P. Anand. 2018a. Chaotic grasshopper optimization algorithm for global optimization. Neural Computing and Applications, 31(8):4385–4405.
   Web of Science ®Google Scholar
• Arora, S., and P. Anand. 2018b. Learning automata based butterfly optimization algorithm for engineering design problems. International Journal of Computational Materials Science and Engineering, 116:147-160.
   Web of Science ®Google Scholar
• Arora, S., and P. Anand. 2019. Binary butterfly optimization approaches for feature selection. Expert Systems with Applications 116:147–60. doi:10.1016/j.eswa.2018.08.051.
   Web of Science ®Google Scholar
• Arora, S., and S. Singh 2014. Performance research on firefly optimization algorithm with mutation. In International Conference, Computing & Systems, Tamil Nadu, India.
   Google Scholar
• Arora, S., and S. Singh. 2017a. An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization. International Journal of Interactive Multimedia and Artificial Intelligence 4:14–21. doi:10.9781/ijimai.2017.442.
   Web of Science ®Google Scholar
• Arora, S., and S. Singh. 2017b. An improved butterfly optimization algorithm with chaos. Journal of Intelligent & Fuzzy Systems 32:1079–88. doi:10.3233/JIFS-16798.
   Web of Science ®Google Scholar
• Arora, S., and S. Singh. 2018. Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23:715–734.
   Google Scholar
• Balasaraswathi, V. R., M. Sugumaran, and Y. Hamid. 2017. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. Journal of Communications and Information Networks 2:107–19. doi:10.1007/s41650-017-0033-7.
   Google Scholar
• Bello, R., Y. Gomez, A. Nowe, and M. M. Garcia 2007. Two-step particle swarm optimization to solve the feature selection problem. In Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference, Rio de Janeiro, Brazil, 691–96. IEEE.
   Google Scholar
• Chen, H., W. Jiang, C. Li, and R. Li. 2013. A heuristic feature selection approach for text categorization by using chaos optimization and genetic algorithm. Mathematical Problems in Engineering (2013):1–6. doi:10.1155/2013/524017.
   Google Scholar
• Derrac, J., S. Garca, D. Molina, and F. Herrera. 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1:3–18. doi:10.1016/j.swevo.2011.02.002.
   Web of Science ®Google Scholar
• Eberhart, R., and J. Kennedy 1995a. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium,  Perth, Western Australia, 39–43. IEEE.
   Google Scholar
• Eberhart, R., and J. Kennedy 1995b. Proceedings of IEEE international conference on neural networks.
   Google Scholar
• Emary, E., H. M. Zawbaa, and A. E. Hassanien. 2016a. Binary ant lion approaches for feature selection. Neurocomputing 213:54–65. doi:10.1016/j.neucom.2016.03.101.
   Web of Science ®Google Scholar
• Emary, E., H. M. Zawbaa, and A. E. Hassanien. 2016b. Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–81. doi:10.1016/j.neucom.2015.06.083.
   Web of Science ®Google Scholar
• Franklin, J. 2005. The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer 27:83–85. doi:10.1007/BF02985802.
   Google Scholar
• Gandomi, A., X.-S. Yang, S. Talatahari, and A. Alavi. 2013. Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation 18:89–98. doi:10.1016/j.cnsns.2012.06.009.
   Web of Science ®Google Scholar
• Gandomi, A. H. 2014. Interior search algorithm (isa): A novel approach for global optimization. ISA Transactions 53:1168–83. doi:10.1016/j.isatra.2014.03.018.
   PubMed Web of Science ®Google Scholar
• Gandomi, A. H., and D. A. Roke 2014. Engineering optimization using interior search algorithm. In Swarm Intelligence (SIS), 2014 IEEE Symposium, Orlando, FL, USA, 1–7. IEEE.
   Google Scholar
• Goldberg, D. E., and J. H. Holland. 1988. Genetic algorithms and machine learning. Machine Learning 3:95–99. doi:10.1023/A:1022602019183.
   Google Scholar
• Gu, S., R. Cheng, and Y. Jin. 2018. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Computing 22:811–22. doi:10.1007/s00500-016-2385-6.
   Web of Science ®Google Scholar
• Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3:1157–82.
   Google Scholar
• Han, J., J. Pei, and M. Kamber. 2011. Data mining: Concepts and techniques. Waltham, MA, USA: Elsevier.
   Google Scholar
• He, D., C. He, L.-G. Jiang, H.-W. Zhu, and G.-R. Hu. 2001. Chaotic characteristics of a one-dimensional iterative map with infinite collapses. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions 48:900–06. doi:10.1109/81.933333.
   Google Scholar
• Hedar, A.-R., J. Wang, and M. Fukushima. 2008. Tabu search for attribute reduction in rough set theory. Soft Computing 12:909–18. doi:10.1007/s00500-007-0260-1.
   Web of Science ®Google Scholar
• Holland, J., and D. Goldberg. 1989. Genetic algorithms in search, optimization and machine learning. Massachusetts: Addison-Wesley.
   Google Scholar
• Jordehi, A. R. 2015. A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems. Neural Computing and Applications 26:827–33. doi:10.1007/s00521-014-1751-5.
   Web of Science ®Google Scholar
• Joshi, H., and S. Arora. 2017. Enhanced grey wolf optimization algorithm for global optimization. Fundamenta Informaticae 153:235–64. doi:10.3233/FI-2017-1539.
   Web of Science ®Google Scholar
• Kabir, M. M., M. Shahjahan, and K. Murase. 2011. A new local search based hybrid genetic algorithm for feature selection. Neurocomputing 74:2914–28. doi:10.1016/j.neucom.2011.03.034.
   Web of Science ®Google Scholar
• Kalra, S., and S. Arora 2016. Firefly algorithm hybridized with flower pollination algorithm for multimodal functions. In International Congress on Information and Communication Technology, Udaipur, India, 207–19. Springer.
   Google Scholar
• Karaboga, D., and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39:459–71. doi:10.1007/s10898-007-9149-x.
   Web of Science ®Google Scholar
• Kohavi, R., and G. H. John. 1997. Wrappers for feature subset selection. Artificial Intelligence 97:273–324. doi:10.1016/S0004-3702(97)00043-X.
   Web of Science ®Google Scholar
• Kohli, M., and S. Arora. 2017. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering 5(4):458-472.
   Google Scholar
• Kumar, M., T. K. Rawat, A. Jain, A. A. Singh, and A. Mittal. 2015. Design of digital differentiators using interior search algorithm. Procedia Computer Science 57:368–76. doi:10.1016/j.procs.2015.07.351.
   Google Scholar
• Li, Q., H. Chen, H. Huang, X. Zhao, Z. Cai, C. Tong, W. Liu, and X. Tian. 2017. An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Computational and Mathematical Methods in Medicine 1–15. doi:10.1155/2017/9512741.
   Web of Science ®Google Scholar
• Liu, H., and L. Yu. 2005. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17:491–502. doi:10.1109/TKDE.2005.66.
   Web of Science ®Google Scholar
• Lu, H., X. Wang, Z. Fei, and M. Qiu. 2014. The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms.Mathematical Problems in Engineering 2014:1-16, Article ID 924652.
   Web of Science ®Google Scholar
• Mafarja, M., and S. Abdullah. 2013. Record-to-record travel algorithm for attribute reduction in rough set theory. Journal of Theoretical and Applied Information Technology 49:507–13.
   Google Scholar
• Mafarja, M., and S. Abdullah. 2015. A fuzzy record-to-record travel algorithm for solving rough set attribute reduction. International Journal of Systems Science 46:503–12. doi:10.1080/00207721.2013.791000.
   Web of Science ®Google Scholar
• Mafarja, M., and S. Mirjalili. 2018. Whale optimization approaches for wrapper feature selection. Applied Soft Computing 62:441–53. doi:10.1016/j.asoc.2017.11.006.
   Web of Science ®Google Scholar
• Mafarja, M. M., and S. Mirjalili. 2017. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–12. doi:10.1016/j.neucom.2017.04.053.
   Web of Science ®Google Scholar
• Mirjalili, S. 2015. The ant lion optimizer. Advances in Engineering Software 83:80–98. doi:10.1016/j.advengsoft.2015.01.010.
   Web of Science ®Google Scholar
• Mirjalili, S. 2016a. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications 27:1053–73. doi:10.1007/s00521-015-1920-1.
   Web of Science ®Google Scholar
• Mirjalili, S. 2016b. Sca: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96:120–33. doi:10.1016/j.knosys.2015.12.022.
   Web of Science ®Google Scholar
• Mirjalili, S., A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili. 2017. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software 114:163–91. doi:10.1016/j.advengsoft.2017.07.002.
   Web of Science ®Google Scholar
• Mirjalili, S., and A. Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95:51–67. doi:10.1016/j.advengsoft.2016.01.008.
   Web of Science ®Google Scholar
• Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69:46–61. doi:10.1016/j.advengsoft.2013.12.007.
   Web of Science ®Google Scholar
• Moravej, M., and S.-M. Hosseini-Moghari. 2016. Large scale reservoirs system operation optimization: The interior search algorithm (ISA) approach. Water Resources Management 30:3389–407. doi:10.1007/s11269-016-1358-y.
   Web of Science ®Google Scholar
• Naanaa, A. 2015. Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization. Applied Mathematics and Computation 269:402–11. doi:10.1016/j.amc.2015.07.111.
   Web of Science ®Google Scholar
• Peng, H., F. Long, and C. Ding. 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27:1226–38. doi:10.1109/TPAMI.2005.159.
   PubMed Web of Science ®Google Scholar
• Saremi, S., S. Mirjalili, and A. Lewis. 2017. Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software 105:30–47. doi:10.1016/j.advengsoft.2017.01.004.
   Web of Science ®Google Scholar
• Sayed, G. I., A. Darwish, and A. E. Hassanien. 2018. A new chaotic whale optimization algorithm for features selection. Journal of Classification 35:300–344.
   PubMed Web of Science ®Google Scholar
• Sayed, G. I., A. E. Hassanien, and A. T. Azar. 2017. Feature selection via a novel chaotic crow search algorithm. Neural Computing and Applications 31(1):171–188.
   Web of Science ®Google Scholar
• Sayed, G. I., G. Khoriba, and M. H. Haggag. 2018. A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence 48,  10:3462-3481..
   Google Scholar
• Sayed, S. A.-F., E. Nabil, and A. Badr. 2016. A binary clonal flower pollination algorithm for feature selection. Pattern Recognition Letters 77:21–27. doi:10.1016/j.patrec.2016.03.014.
   Web of Science ®Google Scholar
• Tavazoei, M. S., and M. Haeri. 2007. Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Applied Mathematics and Computation 187:1076–85. doi:10.1016/j.amc.2006.09.087.
   Web of Science ®Google Scholar
• Wang, J., T. Li, and R. Ren 2010. A real time IDSs based on artificial bee colony-support vector machine algorithm. In Advanced computational intelligence (IWACI), 2010 third international workshop on, 91–96. IEEE, Suzhou, China.
   Google Scholar
• Wolpert, D. H., and W. G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1:67–82. doi:10.1109/4235.585893.
   Google Scholar
• Xue, B., M. Zhang, and W. N. Browne. 2014. Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing 18:261–76. doi:10.1016/j.asoc.2013.09.018.
   Web of Science ®Google Scholar
• Yang, X.-S. 2010a. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation 2:78–84. doi:10.1504/IJBIC.2010.032124.
   Web of Science ®Google Scholar
• Yang, X.-S. 2010b. Nature-inspired metaheuristic algorithms. Frome, UK: Luniver press.
   Google Scholar
• Yang, X. S., M. Karamanoglu, and X. He. 2014. Flower pollination algorithm: A novel approach for multiobjective optimization. Engineering Optimization 46:1222–37. doi:10.1080/0305215X.2013.832237.
   Web of Science ®Google Scholar
• Yang, Y., and J. O. Pedersen. 1997. A comparative study on feature selection in text categorization. Conference Paper published in proceedings of the Fourteenth International Conference on Machine Learning, Nashville, Tennessee, USA, vol. 97, 412–20.
   Google Scholar
• Yldz, B. S. 2017. Natural frequency optimization of vehicle components using the interior search algorithm. Materials Testing 59:456–58. doi:10.3139/120.111018.
   Web of Science ®Google Scholar
• Zawbaa, H. M., E. Emary, and C. Grosan. 2016. Feature selection via chaotic antlion optimization. PloS One 11:e0150652. doi:10.1371/journal.pone.0150652.
   PubMed Web of Science ®Google Scholar
• Zheng, W.-M. 1994. Kneading plane of the circle map. Chaos, Solitons & Fractals 4:1221–33. doi:10.1016/0960-0779(94)90033-7.
   Web of Science ®Google Scholar
• Zorarpac, E., and S. A. Özel. 2016. A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Systems with Applications 62:91–103. doi:10.1016/j.eswa.2016.06.004.
   Web of Science ®Google Scholar