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A hybrid algorithm based on cuckoo search and differential evolution for numerical optimization

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DOI: 10.23977/cpcs.2020.41001 | Downloads: 28 | Views: 2548

Author(s)

Jun Xi 1, Liming Zheng 1

Affiliation(s)

1 School of Information Science and Technology, Jinan University, Guangzhou, 510632, China

Corresponding Author

Liming Zheng

ABSTRACT

Cuckoo search (CS) is a new intelligent bionic algorithm, but it is easy to fall into the local optimum. In this paper, a hybrid algorithm based on cuckoo search and differential evolution (CSDE) is proposed. In the global optimization, a control factor is introduced to judge the optimization state of CS, further determine whether it is necessary to combine the differential strategy to improve the population diversity. Besides, different evolutionary strategies are used to enhance the local search. Numerical experiments, carried on 28 benchmark functions from CEC 2013, demonstrate that CSDE successfully achieves better optimization performance than other competitive optimization algorithms.

KEYWORDS

Cuckoo search; Differential evolution; Numerical optimization

CITE THIS PAPER

Jun Xi, Liming Zheng. A hybrid algorithm based on cuckoo search and differential evolution for numerical optimization, Performance and Communication Systems (2020) Vol. 4: 1-8. DOI: http://dx.doi.org/10.23977/cpcs.2020.41001.

REFERENCES

[1] Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 29(1), 17-35.
[2] Hussain, K., Salleh, M. N. M., Cheng, S., & Shi, Y. (2019). Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 52(4), 2191-2233.
[3] Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22(2),
387- 408.
[4] Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
[5]  Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution–an updated survey. Swarm and Evolutionary Computation, 27, 1-30.
[6] Wu, X., & Che, A. (2019). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling. Omega, 82, 155-165.
[7]  Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948).
[8] Kennedy, J. (2010). Particle swarm optimization. Encyclopedia of machine learning, 760-766.
[9] Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
[10] Yang, X. S. (2009, October). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.
[11] Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210-214). IEEE.
[12] Bhandari, A. K., & Maurya, S. (2019). Cuckoo search algorithm-based brightness preserving histogram scheme for low-contrast image enhancement. Soft Computing, 1-27.
[13] Boushaki, S. I., Kamel, N., & Bendjeghaba, O. (2018). A new quantum chaotic cuckoo search algorithm for data clustering. Expert Systems with Applications, 96, 358-372.
[14] Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 29(1), 17-35.
[15] Naik, M. K., & Panda, R. (2016). A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Applied Soft Computing, 38, 661-675.
[16] Ma, H. S., Li, S. X., Li, S. F., Lv, Z. N., & Wang, J. S. (2019). An improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations. Neural Computing and Applications, 31(5), 1375-1389.
[17] Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11(6), 4135-4151.
[18] Hassan, A., & Pillay, N. (2019). Hybrid metaheuristics: An automated approach. Expert Systems with Applications, 130, 132-144.
[19] Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232-249.
[20] Reddy, S. S. (2019). Optimal power flow using hybrid differential evolution and harmony search algorithm. International Journal of Machine Learning and Cybernetics, 10(5), 1077-1091.
[21] Wang, G. G., Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2016). A new hybrid method based on krill herd and cuckoo search for global optimisation tasks. International Journal of Bio-Inspired Computation, 8(5), 286-299.
[22] Chi, R., Su, Y. X., Zhang, D. H., Chi, X. X., & Zhang, H. J. (2019). A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 31(1), 653-670.
[23] Wei, J., & Yu, Y. (2017). An effective hybrid cuckoo search algorithm for unknown parameters and time delays estimation of chaotic systems. IEEE Access, 6, 6560-6571.
[24]  Liang, J. J., Qu, B. Y., Suganthan, P. N., & Hernández-Díaz, A. G. (2013). Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212(34), 281-295.
[25] Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, 32(200), 675-701.

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