Abstract
In order to improve convergence rate and optimization precision of the cuckoo search (CS) algorithm, an improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations (SC-DSCS, where ‘SC’ represents ‘Subpopulation Collaboration,’ ‘DS’ represents ‘dynamic self-adaption’) is proposed. In SC-DSCS, the population of cuckoos is divided into two subgroups. The nest locations of birds belonging to the first subgroup are updated according to the traditional CS algorithm so as to retain the global search ability, and the second subgroup produces the corresponding nest locations for next generation by flying from the better nest locations to enhance the local search ability of the CS algorithm. Through collaboration between two subgroups, the problem that the local search ability of CS algorithm is not strong can be effectively solved. In order to reduce the probability of the algorithm falling into local optimum and improve the optimization precision, the SC-DSCS algorithm creates a new bird’s nest under the comprehensive assessment of the first three best bird’s nests. The new nest is added to the optimal bird’s nest sequence. In order to improve the adaptability of the SC-DSCS, adaptive step length control is adopted in the bird’s nest position updating process. Finally, nine benchmark functions are adopted to carry out the simulation experiments. The proposed algorithm is compared with particle swarm optimization algorithm, artificial colony algorithm and differential evolution algorithm. Simulation results show that the proposed SC-DSCS algorithm has better convergence speed and optimization precision.
Similar content being viewed by others
References
Yang X, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009). IEEE Publications, Washington, pp 210–214
Wang J, Li S, Song J (2015) Cuckoo search algorithm based on repeat-cycle asymptotic self-learning and self-evolving disturbance for function optimization. Comput Intell Neurosci 2015, Article ID 374873, 12 pages. https://doi.org/10.1155/2015/374873
Yang X-S, Deb S (2013) Multi objective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624
Yang XS, Deb S (2018) Cuckoo search: state-of-the-art and opportunities. In: IEEE international conference on soft computing and machine intelligence. IEEE
Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059
Wang F, He X-S, Wang Y, Yang SM (2012) Markov model and convergence analysis based on cuckoo search algorithm. Comput Eng 38(11):180–185
Huang L, Ding S, Yu S et al (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Math Model 40(5–6):3860–3875
Deb S, Deb S, Gandomi AH et al (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362
Zheng H, Zhou Y (2012) A novel cuckoo search optimization algorithm based on Gauss distribution. J Comput Inf Syst 8(10):4193–4200
Mlakar Uroš (2016) Iztok Fister Jr, Iztok Fister. Hybrid self-adaptive cuckoo search for global optimization. Swarm Evol Comput 29:47–72
Li X, Yin M (2015) Modified cuckoo search algorithm with self adaptive parameter method. Inf Sci 298(C):80–97
Chen L, Long W (2013) The improved cuckoo search algorithm to solve the engineering structural optimization problem. Comput Appl Res 31:679–683
Nie H, Liu B et al (2014) Hybrid differential evolution and cuckoo search algorithm for resource-constrained project scheduling. J Guilin Univ Technol 34(2):315–321
Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1–4):55–61
Bhandari AK, Singh VK, Kumar A et al (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560
Ahmed J, Salam Z (2014) A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Appl Energy 119:118–130
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
Kaveh A, Bakhshpoori T (2013) Optimum design of steel frames using cuckoo search algorithm with lévy flights. Struct Des Tall Spec Build 22(13):1023–1036
Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, 1995. Proceedings, pp 1942–1948
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. J Appl Soft Comput 8(1):687–697
Palmer Katie et al (2014) Differential evolution of cognitive impairment in nondemented older persons: results from the Kungsholmen project. J Psychiatry 159(3):436–442
Civicioglu P, Besdok E (2014) Comparative analysis of the cuckoo search algorithm. In: Yang XS (ed) Cuckoo search and firefly algorithm. Studies in computational intelligence, vol 516. Springer, Cham, pp 85–113
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declared that they have no conflicts of interest to this work.
Rights and permissions
About this article
Cite this article
Ma, Hs., Li, Sx., Li, Sf. et al. An Improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations. Neural Comput & Applic 31, 1375–1389 (2019). https://doi.org/10.1007/s00521-018-3512-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3512-3