Abstract
The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective.
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Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142
Burnwal S, Deb S (2013) Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol 64(5–8):951–959
Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46(229–247):2012
Civicioglu P (2013a) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(8121–8144):2013
Civicioglu P (2013b) Circular antenna array design by using evolutionary search algorithms. Progr Electromagn Res B 54:265–284
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
Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, India and Nanyang Technological University, Singapore, Technical ReportTechnical Report
Dey N, Samanta S, Yang XS et al (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio Inspir Comput 5(5):315–326
Durgun İ, Yildiz AR (2012) Structural design optimization of vehicle components using cuckoo search algorithm. Mater Test 54(3):185–188
Ehsan V, Saeed T (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–468
El-Abd M (2012) Generalized opposition-based artificial bee colony algorithm. IEEE Congr Evol Comput (CEC) 2012:1–4
Gandomi A, Yang X, Alavi A (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35
Garcia-Martinez C, Lozano M, Herrera F, Molina D, Sanchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185:1088–1113
Goghrehabadi A, Ghalambaz M, Vosough A (2011) A hybrid power series—cuckoo search optimization algorithm to electrostatic deflection of micro fixed-fixed actuators. Int J Multidiscip Sci Eng 2(4):22–26
Hansen N, Ostermeier A (2001) Completely derandomized self adaptation in evolution strategies. Evol Comput 9(2):159–195
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4(2):1942–1948
Layeb A (2011) A novel quantum inspired cuckoo search for knapsack problems. Int J Bio Inspir Comput 3:297–305
Li XT, Wang JN, Yin MH (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141
Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous space. J Glob Optim 11:341–359
Tuba M, Subotic M, Stanarevic N (2011) Modified cuckoo search algorithm for unconstrained optimization problems. In: Proceeding of the 5th European conference on European computing conference (ECC’11), pp 263–268
Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm Chaos. Solitons Fractals 44:710–718
Wang Y, Cai ZX, Zhang QF (2011a) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 18(1):153–177
Wang Y, Cai Z, Zhang Q (2011b) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications, SAGA 2009. Lecture Notes in Computer Sciences, vol 5792, pp 169–178
Yang XS (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Springer, Berlin, pp 240–249
Yang XS, Deb S (2009) Cuckoo search via Levy flights. World Congress on nature & biologically inspired computing (NaBIC 2009). IEEE Publication, USA, pp 210–214
Yang XS, Gandomi Amir H (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483
Yildiz AR, Saitou KN (2011) Topology synthesis of multicomponent structural assemblies in continuum domains. J Mech Des 133(1):011008
Yildiz AR, Solanki KN (2012) Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. Int J Adv Manuf Technol 59(1–4):367–376
Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88
Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912
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
Acknowledgments
This research is fully supported by Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University under Grant No. ZSDZZZZXK37 and the Fundamental Research Funds for the Central Universities Nos. 11CXPY010. Guangxi Natural Science Foundation (No. 2013GXNSFBA019263), Science and Technology Research Projects of Guangxi Higher Education (No.2013YB029), Scientific Research Foundation of Guangxi Normal University for Doctors.
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Li, X., Yin, M. A particle swarm inspired cuckoo search algorithm for real parameter optimization. Soft Comput 20, 1389–1413 (2016). https://doi.org/10.1007/s00500-015-1594-8
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DOI: https://doi.org/10.1007/s00500-015-1594-8