Soft Computing

, Volume 20, Issue 4, pp 1389–1413 | Cite as

A particle swarm inspired cuckoo search algorithm for real parameter optimization

  • Xiangtao Li
  • Minghao Yin
Methodologies and Application


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.


Cuckoo search algorithm Global numerical optimization  Particle swarm optimization Exploration Exploitation  Chaotic system 



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.


  1. Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30CrossRefGoogle Scholar
  2. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRefGoogle Scholar
  3. Burnwal S, Deb S (2013) Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol 64(5–8):951–959CrossRefGoogle Scholar
  4. Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46(229–247):2012Google Scholar
  5. Civicioglu P (2013a) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(8121–8144):2013Google Scholar
  6. Civicioglu P (2013b) Circular antenna array design by using evolutionary search algorithms. Progr Electromagn Res B 54:265–284Google Scholar
  7. 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):315–346CrossRefGoogle Scholar
  8. 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 ReportGoogle Scholar
  9. 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):315–326CrossRefGoogle Scholar
  10. Durgun İ, Yildiz AR (2012) Structural design optimization of vehicle components using cuckoo search algorithm. Mater Test 54(3):185–188CrossRefGoogle Scholar
  11. Ehsan V, Saeed T (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–468CrossRefGoogle Scholar
  12. El-Abd M (2012) Generalized opposition-based artificial bee colony algorithm. IEEE Congr Evol Comput (CEC) 2012:1–4Google Scholar
  13. Gandomi A, Yang X, Alavi A (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35Google Scholar
  14. 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 185:1088–1113Google Scholar
  15. 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):22–26Google Scholar
  16. Hansen N, Ostermeier A (2001) Completely derandomized self adaptation in evolution strategies. Evol Comput 9(2):159–195CrossRefGoogle Scholar
  17. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4(2):1942–1948CrossRefGoogle Scholar
  18. Layeb A (2011) A novel quantum inspired cuckoo search for knapsack problems. Int J Bio Inspir Comput 3:297–305CrossRefGoogle Scholar
  19. Li XT, Wang JN, Yin MH (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247CrossRefGoogle Scholar
  20. 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):281–295Google Scholar
  21. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141CrossRefGoogle Scholar
  22. Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669CrossRefGoogle Scholar
  23. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous space. J Glob Optim 11:341–359Google Scholar
  24. 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 263–268Google Scholar
  25. Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm Chaos. Solitons Fractals 44:710–718Google Scholar
  26. Wang Y, Cai ZX, Zhang QF (2011a) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 18(1):153–177Google Scholar
  27. Wang Y, Cai Z, Zhang Q (2011b) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66Google Scholar
  28. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications, SAGA 2009. Lecture Notes in Computer Sciences, vol 5792, pp 169–178Google Scholar
  29. Yang XS (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Springer, Berlin, pp 240–249Google Scholar
  30. Yang XS, Deb S (2009) Cuckoo search via Levy flights. World Congress on nature & biologically inspired computing (NaBIC 2009). IEEE Publication, USA, pp 210–214Google Scholar
  31. Yang XS, Gandomi Amir H (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRefGoogle Scholar
  32. Yildiz AR, Saitou KN (2011) Topology synthesis of multicomponent structural assemblies in continuum domains. J Mech Des 133(1):011008CrossRefGoogle Scholar
  33. 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):367–376CrossRefGoogle Scholar
  34. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88MathSciNetCrossRefGoogle Scholar
  35. Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912CrossRefGoogle Scholar
  36. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1–4):55–61CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer Science and Information TechnologyNortheast Normal UniversityChangchunChina

Personalised recommendations