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Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit

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Abstract

Levy flight random walk is one of the important operators of cuckoo search (CS) algorithm, and it employs the fixed step size factor to generate new candidate solutions. In this work, the memory mechanism is introduced into CS algorithm to dynamically select the appropriate step size, which differs from many CS variants by incorporating some existing algorithms into CS framework. To investigate the effectiveness of the presented version, two well-known test suites are employed. Experimental results demonstrate that CS with memory (CSM) exhibits better optimization performance compared with other CS variants. Then, a vibration fault diagnosis model of hydroelectric generating unit (HGU) based on CSM combined with BP neural network is established. Diagnostic results show that the combined model has higher classification accuracy in tackling two diagnostic examples, and also prove the superiority of the proposed algorithm in solving practical problems.

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References

  1. Kiani M, Yildiz AR (2016) A Comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization. Arch Comput Methods Eng 23(4):723–734

    Article  MathSciNet  MATH  Google Scholar 

  2. Thilak KD, Amuthan A (2018) Cellular automata-based improved ant colony-based optimization algorithm for mitigating DDoS attacks in VANETs. Future Gener Comput Syst 82:304–314

    Article  Google Scholar 

  3. Pholdee N, Bureerat S, Yildiz AR (2017) Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame. Int J Veh Des 73(1–3):20–53

    Article  Google Scholar 

  4. MiarNaeimi F, Azizyan G, Rashki M (2017) Multi-level cross entropy optimizer (MCEO): an evolutionary optimization algorithm for engineering problems. Eng Comput. https://doi.org/10.1007/s00366-017-0569-z

    Google Scholar 

  5. Yildiz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26:327–333

    Article  Google Scholar 

  6. Yildiz BS, Lekesiz H (2017) Fatigue-based structural optimisation of vehicle components. Int J Veh Des 73(1–3):54–62

    Article  Google Scholar 

  7. Rao RV, More KC (2015) Optimal design of the heat pipe using TLBO (teaching-learning-based optimization) algorithm. Energy 80:535–544

    Article  Google Scholar 

  8. Zang WK, Ren LY, Zhang WQ et al (2018) A cloud model based DNA genetic algorithm for numerical optimization problems. Future Gener Comput Syst 81:465–477

    Article  Google Scholar 

  9. Kumar N, Vidyarthi DP (2016) A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems. Eng Comput 32(1):35–47

    Article  Google Scholar 

  10. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  11. Yildiz BS (2017) A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems. Int J Veh Des 73(1–3):208–218

    Article  Google Scholar 

  12. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53:1168–1183

    Article  Google Scholar 

  13. Hasançebi O, Azad SK (2015) Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput Struct 154:1–16

    Article  Google Scholar 

  14. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Varaee H, Ghasemi MR (2017) Engineering optimization based on ideal gas molecular movement algorithm. Eng Comput 33(1):71–93

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. 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 

  19. Lin YH, Liang Z, Hu HP (2016) Cuckoo search algorithm with beta distribution. J Nanjing Univ Nat Sci 52(4):638–646 (in Chinese)

    MATH  Google Scholar 

  20. Yang B, Miao J, Fan ZC et al (2018) Modified cuckoo search algorithm for the optimal placement of actuators problem. Appl Soft Comput 67:48–60

    Article  Google Scholar 

  21. Chi R, Su YX, Zhang DH et al (2017) A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3012-x

    Google Scholar 

  22. 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 

  23. Wang GG, Gandomi AH, Zhao XJ et al (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

    Article  Google Scholar 

  24. Boushaki SI, Kamel N, Bendjeghaba O (2018) A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl 96:358–372

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  29. Cheng JT, Wang L, Xiong Y (2018) Modified cuckoo search algorithm and the prediction of flashover voltage of insulators. Neural Comput Appl 30(2):355–370

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Karagöz S, Yildiz AR (2017) A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. Int J Veh Des 73(1–3):179–188

    Article  Google Scholar 

  32. Fouladgar N, Hasanipanah M, Amnieh HB (2017) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput 33(2):181–189

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  34. Li XN, Yang GF (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–372

    Article  Google Scholar 

  35. Cheng JT, Wang L, Xiong Y (2017) An improved cuckoo search algorithm and its application in vibration fault diagnosis for a hydroelectric generating unit. Eng Optim. https://doi.org/10.1080/0305215X.2017.1401067

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  37. Suganthan PN, Hansen N, Liang JJ et al (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, technical report

  38. 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 

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

    Article  Google Scholar 

  40. Chen XY, Hai HQ, Sun JP (2016) Research on the identification of axis orbit in hydro-generator unit based on PSO-BP and combined moment invariants. J Vib Meas Diagn 36(1):108–114 (in Chinese)

    Google Scholar 

  41. Li CS, Zhou JZ, Xiao J et al (2013) Hydraulic turbine governing system identification using T-S fuzzy model optimized by chaotic gravitational search algorithm. Eng Appl Artif Intell 26:2073–2082

    Article  Google Scholar 

  42. Zhang XY, Zhou JZ, Guo J et al (2012) Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine. Expert Syst Appl 39:2621–2628

    Article  Google Scholar 

  43. Li CS, Zhou JZ, Xiao J et al (2013) Vibration fault diagnosis of hydroelectric generating unit using gravitational search based kernel clustering method. Proc CSEE 33(2):98–104 (in Chinese)

    Google Scholar 

  44. Fu WL, Zhou JZ, Li CS et al (2014) Vibrant fault diagnosis for hydro-electric generating unit based on support vector data description improved with fuzzy K nearest neighbor. Proc CSEE 34(32):5788–5795 (in Chinese)

    Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (51669006, 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

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

    Jiatang Cheng & Yan Xiong

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  1. Jiatang Cheng
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  2. Lei Wang
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  3. Yan Xiong
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Correspondence to Lei Wang.

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Cheng, J., Wang, L. & Xiong, Y. Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit. Engineering with Computers 35, 687–702 (2019). https://doi.org/10.1007/s00366-018-0627-1

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