A novel cuckoo search algorithm under adaptive parameter control for global numerical optimization
- 76 Downloads
Cuckoo search (CS) is a well-known population-based stochastic search technique for solving global numerical optimization problems. At each iteration process, CS searches for new solutions by Lévy flights random walk together with a local random walk (LRW). For LRW, mutation proceeds with a uniformly distributed random number in the interval [0, 1] as its mutation factor, which plays an important role in controlling the population diversity and the explorative power of the algorithm. However, this mutation factor generally results in sensitivity to the given optimization problem and thus fails to balance well these two aspects. In view of this consideration, we introduce a simple adaptive parameter control mechanism to LRW, and propose a novel adaptive cuckoo search (CSAPC) algorithm in this paper to improve the optimization performance of CS. The adaptive parameter control mechanism dynamically updates the control parameters based on a Cauchy distribution and the Lehmer mean during the iteration. To verify the performance of CSAPC, simulations and comparisons are conducted on 48 benchmark functions from two well-known test suites. In order to further test its efficacy, CSAPC is applied to solve the problem of parameter estimation of two typical uncertain fractional-order chaotic systems. The numerical, statistical and graphical analysis demonstrates the great competency of CSAPC, and hence can be regarded as an efficient and promising tool for solving the real-world complex optimization problems besides the benchmark problems.
KeywordsCuckoo search algorithm Adaptive parameter control Global numerical optimization Fractional-order chaotic systems
This work is supported by the Fundamental Research Funds for the Central Universities (No. 2017YJS200), China Scholarship Council (No. 201807090092), the National Nature Science Foundation of China (No. 61772063) and Beijing Natural Science Foundation (Z180005).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Agrawal OP, Kumar P (2007) Comparison of five numerical schemes for fractional differential equations. In: Advances in fractional calculus, pp 43–60Google Scholar
- Boushaki SI, Kamel N, Bendjeghaba O (2015) Improved cuckoo search algorithm for document clustering. In: IFIP international conference on computer science and its applications. Springer, pp 217–228Google Scholar
- Caraffini F, Iacca G, Neri F, Picinali L, Mininno E (2013) A CMA-ES super-fit scheme for the re-sampled inheritance search. In: 2013 IEEE congress on evolutionary computation (CEC). IEEE, pp 1123–1130Google Scholar
- Cheung Ngaam J, Xue MD, Hong BS (2017) A nonhomogeneous cuckoo search algorithm based on quantum mechanism for real parameter optimization. IEEE Trans Cybern 47(2):391Google Scholar
- Elsayed SM, Sarker RA, Essam DL (2013) A genetic algorithm for solving the CEC’2013 competition problems on real-parameter optimization. In: Evolutionary Computation, pp 356–360Google Scholar
- Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, 1989. Reading: Addison-WesleyGoogle Scholar
- Guerrero M, Castillo O, García M (2015) Fuzzy dynamic parameters adaptation in the cuckoo search algorithm using fuzzy logic. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 441–448Google Scholar
- Huang H, Hu P (2016) A self-adaptive mutation cuckoo search algorithm. In: 2016 12th world congress on intelligent control and automation (WCICA). IEEE, pp 1064–1068Google Scholar
- James K (2011) Particle swarm optimization. In Encyclopedia of machine learning, pages 760–766. SpringerGoogle Scholar
- Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz Alfredo G (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on realparameter optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University. Singapore. Technical Report, 2013, vol 201212, issue 34, pp 281–295Google Scholar
- Mandal B, Si T (2015) Opposition based particle swarm optimization with exploration and exploitation through gbest. In: International conference on advances in computing, communications and informatics, pp 245–250Google Scholar
- Ouaarab A, Ahiod B, Yang X-S (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669Google Scholar
- Petr, I, Bedn, D (2009) Fractional-order chaotic systems. In: IEEE international conference on emerging technologies & factory automation, pp 1031–1038Google Scholar
- Shehab M, Ahamad TK, Laouchedi M (2018) A hybrid method based on cuckoo search algorithm for global optimization problems. J ICT 17(3):469–491Google Scholar
- Shehab M, Khader AT, Laouchedi M (2017) Modified cuckoo search algorithm for solving global optimization problems. In: International conference of reliable information and communication technology. Springer, pp 561–570Google Scholar
- Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC, special session on real-parameter optimization. KanGAL Rep 2005005:2005Google Scholar
- Wang F, He XS, Wang Y, Yang SM (2012) Markov model and convergence analysis based on cuckoo search algorithm. Comput Eng 38(11):180–185Google Scholar
- Wang F, Luo L, He XS, Wang Y (2011) Hybrid optimization algorithm of PSO and cuckoo search. In: 2011 2nd international conference on artificial intelligence, management science and electronic commerce (AIMSEC). IEEE, pp. 1172–1175Google Scholar
- Wang H, Wang W, Sun H, Li C, Rahnamayan S, Liu Y (2015) A modified cuckoo search algorithm for flow shop scheduling problem with blocking. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 456–463Google Scholar
- Wei Sun, Lin Anping Yu, Liang Qiaokang Hongshan, Guohua Wu (2017) All-dimension neighborhood based particle swarm optimization with randomly selected neighbors. Inf Sci Int J 405:141–156Google Scholar
- Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp. 210–214Google Scholar
- Yang X-S (2013) Cuckoo search and firefly algorithm: Theory and applications, vol 516. Springer, BerlinGoogle Scholar
- Zaharie D (2001) On the explorative power of differential evolution. In: 3rd International workshop on symbolic and numerical algorithms on scientific computing, SYNASC-2001, Timişoara, RomaniaGoogle Scholar
- Zhang Z, Chen Y (2014) An improved cuckoo search algorithm with adaptive method. In: 2014 seventh international joint conference on computational sciences and optimization (CSO). IEEE, pp. 204–207Google Scholar