A modified cuckoo search using different search strategies

Year 2016, Volume: 4 Issue: Special Issue-1, 190 - 194, 26.12.2016

Hüseyin Haklı

Abstract

Cuckoo search (CS) is one of the recent population-based algorithms used
for solving continuous optimization problems. The most known problem for
optimization techniques is balancing between exploration and exploitation. CS
uses two search strategies to updating the nest: local and global search.  Although cuckoo search are adequate for the
exploration, it is not well enough the exploitation. Only one search equation
is used for local search, this equation remains incapable and causes some
deficiencies about the exploitation.  To
enhance the ability of exploitation and to balance between global search and
local search, different search strategies were implemented in CS algorithm. The
proposed method was compared with basic CS on well-known unimodal and multimodal
benchmark functions. Experimental results show that the proposed method is more
successful than the basic CS in terms of solution quality.

Keywords

Cuckoo search, Continuous optimization

References

• [1] J. Kennedy, R. Eberhart, “Particle swarm optimization”, in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43.
• [2] D. Karaboga, “An idea based on honey bee swarm for numerical optimization”,Technical Report-TR06, Erciyes University, Engineering Faculty, Comput. Eng.Dep., 2005.
• [3] M. Dorigo, G.D. Caro, “Ant colony optimization: a new meta-heuristic”, in: Proceedings of the 1999 Congress on Evolutionary Computation, 1999, pp. 1470–1477.
• [4] X.-S. Yang, “Firefly algorithms for multimodal optimization”, in: 5th International Symposium SAGA, 2009, pp. 169–178.
• [5] X.-S. Yang, “A new metaheuristic bat-inspired algorithm”, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Eds. Cruz C., Gonzalez J., Krasnogor N., and Terraza G.), Springer, SCI 284, 65-74, 2010.
• [6] X.-S. Yang and S. Deb, “Cuckoo search via Le´vy flights”, in Proceedings of world congress on nature and biologically inspired computing IEEE Publications, 2009, pp. 210–214.
• [7] C. Cobos, H. Muñoz-Collazos, R. Urbano-Muñoz, M. Mendoza, E. León and E. Herrera-Viedma, “Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion”, Information Sciences, vol. 281, pp. 248–264, 2014.
• [8] T. T. Nguyen, D. N. Vo and T. T. Dao, “Cuckoo Search Algorithm Using Different Distributions for Short-Term Hydrothermal Scheduling with Cascaded Hydropower Plants”, in Proc. TENCON’14 , 2014, pp. 1-6.
• [9] T. T. Nguyen, D. N. Vo and A. V. Truong, “Cuckoo search algorithm for short-term hydrothermal scheduling”, Applied Energy, vol. 132 pp. 276–287, 2014.
• [10] M. Basu and A. Chowdhury, “Cuckoo search algorithm for economic dispatch”, Energy , vol. 60, pp. 99-108, 2013.
• [11] S. Agrawal, R. Panda, S. Bhuyan and B.K. Panigrahi, “Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm”, Swarm and Evolutionary Computation , vol. 11, pp. 16–30, 2013.
• [12] X. Ding, Z. Xu , N.J. Cheung and X. Liu, “Parameter estimation of Takagi–Sugeno fuzzy system using heterogeneous cuckoo search algorithm”, Neurocomputing, vol. 151, pp. 1332–1342, 2015.
• [13] S. Berrazouane and K. Mohammedi, “Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system”, Energy Conversion and Management , vol. 78, pp. 652–660, 2014.
• [14] G. Kanagaraj, S.G. Ponnambalam and N. Jawahar, “A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems”, Computers & Industrial Engineering , vol. 66, pp. 1115–1124, 2013.
• [15] J. Wang, H. Jiang, Y. Wu and Y. Dong, “Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm”, Energy , vol. 81, pp. 627-644, 2015.
• [16] G. Li , P. Niu and X. Xiao , "Development and investigation of efficient artificial bee colony algorithm for numerical function optimization", Applied Soft Computing, Vol. 12, pp. 320–332, 2012.
• [17] X.-S. Yang and S. Deb, “Cuckoo search: recent advances and applications”, Neural Comput & Applic, vol. 24, pp. 169–174, 2014.
• [18] E. Valian, S. Tavakoli, S. Mohanna and A. Haghi, “Improved cuckoo search for reliability optimization problems”, Computers & Industrial Engineering , vol. 64, pp. 459–468, 2013.
• [19] S. Walton, O. Hassan, K. Morgan and M.R. Brown, “Modified cuckoo search: A new gradient free optimisation algorithm”, Chaos, Solitons & Fractals , vol. 44, pp. 710–718, 2011.
• [20] Z. Zhang and Y. Chen, “An Improved Cuckoo Search Algorithm with Adaptive Method”, in: Proceedings of 2014 Seventh International Joint Conference on Computational Sciences and Optimization, 2014, pp. 204-207.
• [21] P. Zhao and H. Li, “Opposition-Based Cuckoo Search Algorithm for Optimization Problems”, in: Proceedings of 2012 Fifth International Symposium on Computational Intelligence and Design, 2012, pp. 344-347.
• [22] X. Li and M. Yin, “Modified cuckoo search algorithm with self adaptive parameter method”, Information Sciences , vol. 298, pp. 80–97, 2015.
• [23] D. Binu , M. Selvi and A. Georgea, “MKF-Cuckoo: Hybridization of Cuckoo Search and Multiple Kernel-Based Fuzzy C-Means Algorithm”, AASRI Procedia , vol. 4, pp. 243 – 249, 2013.
• [24] M.K. Marichelvam, T. Prabaharan and X.S. Yang, “Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan”, Applied Soft Computing , vol. 19, pp. 93–101, 2014.
• [25] W. Gao, S. Liu, L. Huang, A novel artificial bee colony algorithm based on modified search equation and orthogonal learning, IEEE T. Syst. Man cy. B,, doi: 10.1109/TSMCB.2012.2222373, 2012.
• [26] W. Gao, S. Liu, L. Huang, A global best artificial bee colony algorithm for global optimization, J. Comput. Appl. Math. 236 (2012) 2741-2753.