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A novel cuckoo search algorithm with multiple update rules

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Abstract

In this paper, we propose a novel cuckoo search algorithm with multiple update rules, referred to as a hybrid CS algorithm (HCS). In the presented approach, to overcome the mutual interference among dimensions and enhance the local search capability, two different one-dimensional update rules are integrated into CS framework for acquiring the candidate solutions. Moreover, using the characteristic of occasionally long jumps in Levy distribution, the proper selection between the one-dimensional update rules and Levy flight random walk is achieved by setting a limit value, so as to further enhance the exploration ability. The performance of the presented algorithm is then extensively investigated on 49 benchmark test functions including 11 common instances, 10 instances introduced in CEC 2005, and 28 instances presented in CEC 2013. The experimental results indicate that HCS algorithm is better than other CS variants in terms of solution accuracy and robustness, and it also outperforms the seven state-of-the-art intelligent algorithms.

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References

  1. Saka MP, Hasançebi O, Geem ZW (2016) Metaheuristics in structural optimization and discussions on harmony search algorithm. Swarm Evol Comput 28:88–97

    Article  Google Scholar 

  2. Adarsh BR, Raghunathan T, Jayabarathi T et al. (2016) Economic dispatch using chaotic bat algorithm. Energy 96:666–675

    Article  Google Scholar 

  3. Han XH, Quan L, Xiong XY et al. (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7

    Article  Google Scholar 

  4. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks pp 1942–1948

  5. Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Opt 11(4):341–359

    Article  MathSciNet  Google Scholar 

  6. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  7. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Opt 39(3):459–471

    Article  MathSciNet  Google Scholar 

  8. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  9. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  Google Scholar 

  10. Yang XS, Deb S (2010) Engineering optimisation by Cuckoo search. Int J Math Mod Num Opt 1(4):330–343

    MATH  Google Scholar 

  11. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  12. Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24 (1):169–174

    Article  Google Scholar 

  13. Rakhshani H, Rahati A (2017) Snap-drift cuckoo search: a novel cuckoo search optimization algorithm. Appl Soft Comput 52:771–794

    Article  Google Scholar 

  14. Kordestani JK, Firouzjaee HA, Meybodi MR (2018) An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems. Appl Intell 48(1):97–117

    Article  Google Scholar 

  15. Cheng JT, Wang L, Xiong Y (2017) Modified cuckoo search algorithm and the prediction of flashover voltage of insulators. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3179-1

    Article  Google Scholar 

  16. Firouzjaee HA, Kordestani JK, Meybodi MR (2017) Cuckoo search with composite flight operator for numerical optimization problems and its application in tunneling. Eng Opt 49(4):597–616

    Article  Google Scholar 

  17. Bhattacharjee KK, Sarmah SP (2017) Modified swarm intelligence based techniques for the knapsack problem. Appl Intell 46(1):158–179

    Article  Google Scholar 

  18. Naumann DS, Evans B, Walton S, Hassan O (2016) A novel implementation of computational aerodynamic shape optimisation using modified Cuckoo search. Appl Math Mod 40:4543–4559

    Article  MathSciNet  Google Scholar 

  19. Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53:764–779

    Article  Google Scholar 

  20. Valian E, Valian E (2013) A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems. Eng Opt 45(11):1273–1286

    Article  MathSciNet  Google Scholar 

  21. Kim MK (2015) Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms. IET Gener Transm Distrib 9(13):1553–1563

    Article  Google Scholar 

  22. Sait SM, Bala A, El-Maleh AH (2016) Cuckoo search based resource optimization of datacenters. Appl Intell 44(3):489–506

    Article  Google Scholar 

  23. Valian E, Mohanna S, Tavakoli S (2011) Improved cuckoo search algorithm for global optimization. Int J Commun Inf Technol 1(1):31–44

    MATH  Google Scholar 

  24. Wang J, Zhou BH (2011) A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Neural Comput Appl 27(6):1511– 1517

    Article  Google Scholar 

  25. Walton S, Hassan O, Morgan K et al. (2011) Modified Cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Frac 44:710–718

    Article  Google Scholar 

  26. Daniel E, Anitha J, Gnanaraj J (2017) Optimum laplacian wavelet mask based medical image using hybrid cuckoo search grey wolf optimization algorithm. Knowl-Based Syst 131:58–69

    Article  Google Scholar 

  27. Kanagaraj G, Ponnambalam SG, Jawahar N et al. (2014) An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Eng Opt 46(10):1331–1351

    Article  MathSciNet  Google Scholar 

  28. Mlakar U, Fister I Jr, Fister I (2016) Hybrid self-adaptive cuckoo search for global optimization. Swarm Evol Comput 29:47– 72

    Article  Google Scholar 

  29. Kiran MS, Hakli H, Gunduz M et al. (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157

    Article  MathSciNet  Google Scholar 

  30. Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675

    Article  Google Scholar 

  31. Huang L, Ding S, Yu SH et al. (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Math Mod 40:3860–3875

    Article  MathSciNet  Google Scholar 

  32. Ding XM, Xu ZK, Cheung NJ et al. (2015) Parameter estimation of TakagiSugeno fuzzy system using heterogeneous cuckoo search algorithm. Neurocomputing 151:1332–1342

    Article  Google Scholar 

  33. Wang LJ, Zhong YW, Yin YL (2016) Nearest neighbour cuckoo search algorithm with probabilistic mutation. Appl Soft Comput 49:498–509

    Article  Google Scholar 

  34. Cui ZH, Sun B, Wang GG et al. (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Dist Comput 103:42–52

    Article  Google Scholar 

  35. Wang LJ, Yin YL, Zhong YW (2015) Cuckoo search with varied scaling factor. Front Comput Sci 9 (4):623–635

    Article  Google Scholar 

  36. Alcalá-Fdez J, Sánchez L et al. (2009) Garcí,a S KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  37. Liang JJ, Qu BY, Suganthan PN et al. (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization Technical Report

  38. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15

    Article  MathSciNet  Google Scholar 

  39. Omran MGH, Engelbrecht AP, Salman A (2009) Bare bones differential evolution. Eur J Oper Res 196:128–139

    Article  MathSciNet  Google Scholar 

  40. Hakli H, Uguz H (2014) A novel particle swarm optimization algorithm with Levy flight. Appl Soft Comput 23:333–345

    Article  Google Scholar 

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Acknowledgements

The research is supported by the National Natural Science Foundation of China under Project Code (51669006 and 61773314).

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Authors and Affiliations

  1. The Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Jiatang Cheng, Lei Wang & Qiaoyong Jiang

  2. The Engineering College, Honghe University, Mengzi, 661199, China

    Jiatang Cheng & Yan Xiong

Authors
  1. Jiatang Cheng
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  2. Lei Wang
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  3. Qiaoyong Jiang
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  4. Yan Xiong
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Corresponding author

Correspondence to Lei Wang.

Appendix

Appendix

The 11 common benchmark functions and 10 instances introduced in CEC 2005 are as follows.

f1::

Sphere function.

f2::

Schwefel function 2.22.

f3::

Schwefel function 1.2.

f4::

Schwefel function 2.21.

f5::

Rosenbrock function.

f6::

Schwefel function 2.26.

f7::

Rastrigin function.

f8::

Ackley function.

f9::

Griewank Function.

f10::

Penalized function 1.

f11::

Penalized function 2.

F1::

Shifted sphere function.

F2::

Shifted Schwefel function 1.2.

F3::

Shifted rotated high conditioned elliptic function.

F4::

Shifted Schwefel function 1.2 with Noise.

F5::

Schwefel function 2.6 with global optimum on bounds.

F6::

Shifted Rosenbrock function.

F7::

Shifted rotated Griewank function without bounds.

F8::

Shifted rotated Ackley function with global optimum on bounds.

F9::

Shifted Rastrigin function.

F10::

Shifted rotated Rastrigin function.

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Cheng, J., Wang, L., Jiang, Q. et al. A novel cuckoo search algorithm with multiple update rules. Appl Intell 48, 4192–4211 (2018). https://doi.org/10.1007/s10489-018-1198-y

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