Comparative Analysis of the Cuckoo Search Algorithm

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 516)

Abstract

Cuckoo Search Algorithm (CS) is a population based, elitist evolutionary search algorithm proposed for the solution of numerical optimization problems. Despite its wide use, the algorithmic process of CS has been scarcely studied in detail. In this chapter, the algorithmic structure of CS and its effective problem solving success have been studied. Fifty benchmark problems were used in the numerical tests performed in order to study the algorithmic behavior of CS. The success of CS in solving benchmark problems was compared with three widely used optimization algorithms (i.e., PSO, DE, and ABC) by means of Kruskal–Wallis statistical test. The search strategy of CS, which utilizes the Lèvy distribution, enables it to analyze the search space in a very successful manner. The statistical results have verified that CS has the superior problem-solving ability as a search strategy.

Keywords

Cuckoo search Stable distributions Lèvy-walk  Kruskal–Wallis test 

Notes

Acknowledgments

The studies in this chapter have been supported within the scope of the scientific research project of 110Y309 supported by TUBITAK.

References

  1. 1.
    Yang, X.S., Deb, S.: Cuckoo search via Lèvy flights. World Congress on Nature and Biologically Inspired Computing’NaBIC-2009, Coimbatore, India, vol. 4, pp. 210–214 (2009)Google Scholar
  2. 2.
    Civicioglu, P., Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39, 315–346 (2013)CrossRefGoogle Scholar
  3. 3.
    Dhivya, M., Sundarambal, M.: Cuckoo Search for data gathering in wireless sensor networks. Int. J. Mob. Commun. 9, 642–656 (2011)CrossRefGoogle Scholar
  4. 4.
    Layeb, A.: A novel quantum inspired cuckoo search for knapsack problems. Int. J. Bio-Insp. Comput. 3, 297–305 (2011)Google Scholar
  5. 5.
    Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos. Soliton. Fract. 44, 710–718 (2011)CrossRefGoogle Scholar
  6. 6.
    Abdul Rani, K.N., Abd Malek, M.F., Siew-Chin, N.: Nature-inspired cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array. Radioengineering 21, 865–874 (2012)Google Scholar
  7. 7.
    Durgun, I., Yildiz, A.R.: Structural design optimization of vehicle components using Cuckoo Search Algorithm. Mater. Test. 54, 185–188 (2012)CrossRefGoogle Scholar
  8. 8.
    Gherboudj, A., Layeb, A., Chikhi, S.: Solving 0–1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int. J. Bio-Insp. Comput. 4, 229–236 (2012)CrossRefGoogle Scholar
  9. 9.
    Marichelvam, M.K.: An improved hybrid cuckoo search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems. Int. J. Bio-Insp. Comput. 4, 200–205 (2012)CrossRefGoogle Scholar
  10. 10.
    Moravej, Z., Akhlaghi, A.: A new approach for DG allocation in distribution network with time variable loads using cuckoo search. Int. Rev. Electr. Eng-I. 7, 4027–4034 (2012)Google Scholar
  11. 11.
    Natarajan, A., Subramanian, S., Premalatha, K.: A comparative study of cuckoo search and bat algorithm for Bloom filter optimisation in spam filtering. Int. J. Bio-Insp. Comput. 4, 89–99 (2012)CrossRefGoogle Scholar
  12. 12.
    Srivastava, P.R., Sravya, C., Ashima, S., Kamisetti, S., Lakshmi, M.: Test sequence optimisation: an intelligent approach via cuckoo search. Int. J. Bio-Insp. Comput. 4, 139–148 (2012)CrossRefGoogle Scholar
  13. 13.
    Srivastava, P.R., Varshney, A., Nama, P., Yang, X.-S.: Software test effort estimation: a model based on cuckoo search. Int. J. Bio-Insp. Comput. 4, 278–285 (2012)CrossRefGoogle Scholar
  14. 14.
    Burnwal, S., Deb, S.: Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int. J. Adv. Manuf. Tech. 64, 951–959 (2013)CrossRefGoogle Scholar
  15. 15.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013)CrossRefGoogle Scholar
  16. 16.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global. Optim. 11, 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. IEEE. C. Evol. Computat. 1–3, 1785–1791 (2005)Google Scholar
  18. 18.
    Igel, C., Hansen, N., Roth, S.: Covariance matrix adaptation for multi-objective optimization. Evol. Comput. 15, 1–28 (2007)CrossRefGoogle Scholar
  19. 19.
    Tsoulos, I.G., Stavrakoudis, A.: Enhancing PSO methods for global optimization. Appl. Math. Comput. 216(10), 2988–3001 (2010)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Clerc, M., Kennedy, J.: The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)CrossRefGoogle Scholar
  21. 21.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inform. Sci. 13, 2232–2248 (2009)CrossRefGoogle Scholar
  22. 22.
    Civicioglu, P.: Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput. Geosci. 46, 229–247 (2012)CrossRefGoogle Scholar
  23. 23.
    Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219, 8121–8144 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global. Optim. 39, 459–471 (2007)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13, 398–417 (2009)CrossRefGoogle Scholar
  27. 27.
    Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10, 646–657 (2006)CrossRefGoogle Scholar
  28. 28.
    Tasgetiren, M.F., Suganthan, P.N., Pan, Q.K.: An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl. Math. Comput. 215, 3356–3368 (2010)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10, 281–295 (2006)CrossRefGoogle Scholar
  30. 30.
    He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intel. 20, 89–99 (2007)CrossRefGoogle Scholar
  31. 31.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B. 26, 29–41 (1996)CrossRefGoogle Scholar
  32. 32.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRefGoogle Scholar
  33. 33.
    Ishibuch, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE. Trans. Evol. Comput. 7, 204–223 (2003)CrossRefGoogle Scholar
  34. 34.
    Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K.: Application of genetic programming for multicategory pattern classification. IEEE Trans. Evol. Comput. 4, 242–258 (2000)CrossRefGoogle Scholar
  35. 35.
    de Carvalho, M.G., Laender, A.H.F., Goncalves, M.A., et al.: A genetic programming approach to record deduplication. IEEE Trans. Knowl. Data. Eng. 24, 399–412 (2012)CrossRefGoogle Scholar
  36. 36.
    Liang, Y., Chen, W.: A survey on computing lèvy stable distributions and a new MATLAB toolbox. Signal. Process. 93, 242–251 (2013)CrossRefGoogle Scholar
  37. 37.
    Hu, Y., Zhang, J., Di, Z., Huan, D.: Toward a general understanding of the scaling laws in human and animal mobility. Europ. Phys. Lett. 96, 38006–p1-p6 (2011).Google Scholar
  38. 38.
    Humphries, N.E., Weimerskirch, H., Queiroza, N., Southall, E.J., Sims, D.W.: Foraging success of biological lèvy flights recorded in situ. PNAS 109, 7169–7174 (2012)CrossRefGoogle Scholar
  39. 39.
    Derrac, J., Garcia, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1, 3–18 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.College of Aviation, Department of Aircraft Electrics and ElectronicsErciyes UniversityKayseriTurkey
  2. 2.Department of Geomatic EngineeringErciyes UniversityKayseriTurkey

Personalised recommendations