Abstract
In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).
References
[1] I. Fister, Jr., M. Perc, S. M. Kamal and I. Fister, A review of chaos-based firefly algorithms: Perspectives and research challenges, Appl. Math. Comput. 252 (2015), 155–165. 10.1016/j.amc.2014.12.006Search in Google Scholar
[2] G. Kanagaraj, S. G. Ponnambalam and N. Jawahar, A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems, Comput. Ind. Eng. 66 (2013), no. 4, 1115–1124. 10.1016/j.cie.2013.08.003Search in Google Scholar
[3] V. Kelner, F. Capitanescu, O. Léonard and L. Wehenkel, A hybrid optimization technique coupling an evolutionary and a local search algorithm, J. Comput. Appl. Math. 215 (2008), no. 2, 448–456. 10.1016/j.cam.2006.03.048Search in Google Scholar
[4] M. D. R. Payne, The Cuckoos, Oxford University Press, Oxford, 2005. Search in Google Scholar
[5] G. Preet Singh and A. Singh, Comparative study of krill herd, firefly and cuckoo search algorithms for unimodal and multimodal optimization, Int. J. Intell. Syst. Appl. 2 (2014), no. 3, 35–49. 10.5815/ijisa.2014.03.04Search in Google Scholar
[6] A. Ouaarab, B. Ahiod and X. S. Yang, Discrete cuckoo search algorithm for the travelling salesman problem, Neural Comput. Appl. 24 (2014), no. 7–8, 1659–1669. 10.1007/s00521-013-1402-2Search in Google Scholar
[7] S. Salcedo-Sanz, Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures, Phys. Rep. 655 (2016), 1–70. 10.1016/j.physrep.2016.08.001Search in Google Scholar
[8] S. S. Sankalap Arora and S. Singh, A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search, 2013 International Conference on Control Computing Communication & Materials (Allahabad 2013), IEEE Press, Piscataway (2013), 1–4. Search in Google Scholar
[9] D. Shilane, J. Martikainen, S. Dudoit and S. J. Ovaska, A general framework for statistical performance comparison of evolutionary computation algorithms, Inform. Sci. 178 (2008), no. 14, 2870–2879. 10.1016/j.ins.2008.03.007Search in Google Scholar
[10] N. P. N. Suganthan, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Technical Report, Nanyang Technological University, Singapore, 2005. Search in Google Scholar
[11] D. A. Wood, Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic, chaos-enhanced, fat-tailed distribution sampling and metaheuristic profiling, J. Natural Gas Sci. Eng. 34 (2016), no. 1, 236–252. 10.1016/j.jngse.2016.06.060Search in Google Scholar
[12] X.-S. Yang, Firefly algorithm, Lévy flights and global optimization, Research and Development in Intelligent Systems XXVI, Springer London (2010), 209–218. 10.1007/978-1-84882-983-1_15Search in Google Scholar
[13] X.-S. Yang and S. Deb, Cuckoo search via Lévy flights, World Congress on Nature and Biologically Inspired Computing (Coimbatore 2009), IEEE Press, Piscataway (2009), 210–214. 10.1109/NABIC.2009.5393690Search in Google Scholar
[14] X.-S. Yang and M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: An Overview, Elsevier, Amsterdam, 2013. 10.1016/B978-0-12-405163-8.00001-6Search in Google Scholar
[15] Y. W. Zhang, L. Wang and Q. D. Wu, Dynamic adaptation cuckoo search algorithm, Control Decision 29 (2014), no. 4, 617–622. Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
