Skip to main content
SpringerLink
Log in

A Novel Pigeon Nesting Algorithm Based on the Nesting Behaviour of Pigeon for Health Monitoring of Structure

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Introduction

The development of different optimization algorithms following the No Free-Lunch Theory has led the researchers to explore and develop new optimization algorithms which may be used to perform complicated mathematical solutions.

Objectives

This paper proposes a novel Pigeon Nesting Algorithm (PNA), a single solution-based SI algorithm, which is capable of escaping the local optima due to the combination of nesting and homing behaviour of pigeon and provide accurate optimization results.

Method

In order to check the validity of proposed PNA algorithm, it has been applied to the 29 benchmark functions and compared with the existing single solution and population-based meta-heuristic algorithms. In order to check the application PNA for SHM of a practical structure, its performance for damage detection of ASCE Benchmark structure using stiffness-based objective function has been compared with other well-known optimization algorithms.

Results

The PNA gives good competition to the other algorithms when tested for the 29 benchmarks functions. PNA proves to be a robust algorithm for health monitoring of ASCE benchmark structure as it shows an approximate error of 1%. It has also been found that the algorithm can be used for detecting damage even in the presence of noise of 5%.

Conclusion

The algorithm also outperforms other single solution-based algorithms when used for optimization of design problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Wolpert DH, Macready WG (1997) No free lunch theorem for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  2. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 9:46–61

    Google Scholar 

  3. Yang XS (2018) Nature-inspired algorithms and applied optimization. Springer, Cham, p 330

    Google Scholar 

  4. Kennedy J, Elberhart R (1995) Particle swarm optimization. Neural Netw IEEE 4:1942–1948

    Google Scholar 

  5. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  6. Yang XS (2008) Nature-inspired Metaheuristic Algorithms, Luniver Press

  7. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Adv Soft Comput 4529(2007):789–798

    Google Scholar 

  8. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization. Springer, Berlin, Heidelberg, pp 65–74

    Google Scholar 

  9. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing

  10. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 2015(83):80–98

    Google Scholar 

  11. Mishra M, Barman SK, Maity D, Maiti DK (2018) Ant lion optimisation algorithm for structural damage detection using vibration data. J Civ Struct Heal Monit. https://doi.org/10.1007/s13349-018-0318-z

    Article  Google Scholar 

  12. Duan H, Qiao P (2014) Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intell Comput Cybern 7(1):24–37

    MathSciNet  Google Scholar 

  13. Zhong Y, Wang L, Lin M, Zhang H (2019) Discrete pigeon-inspired optimization algorithm with Metropolis acceptance criterion for large-scale traveling salesman problem. Swarm Evol Comput 48:134–144

    Google Scholar 

  14. Das S, Saha P (2016) Damage identification in a multi-storeyed building using modal based health monitoring techniques. In: Structural engineering convention (SEC-2016) CSIR-SERC

  15. Das S, Saha P, Parto SK (2016) Vibration based damage detction techniques used for health monitoring of structrues: a review. J Civ Struct Heal Monit 6(3):477–507

    Google Scholar 

  16. Guo J, Jiao J, Fujita K, Takewaki I (2019) Damage identification for frame structures using vision-based measurement. Eng Struct 199:109634

    Google Scholar 

  17. Law SS, Li XY, Zhu XQ, Chan SL (2005) Structural damage detection from wavelet packet sensitivity. Eng Struct 27(9):1339–1348

    Google Scholar 

  18. Yin T, Jiang Q-H, Yuen K-V (2017) Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Eng Struct 132:260–277

    Google Scholar 

  19. Perera R, Ruiz A, Manzano C (2007) An evolutionary multiobjective framework for structural damage localization and quantification. Eng Struct 29(10):2540–2550

    Google Scholar 

  20. Aloisio A, Pasca D, Tomasi R, Fragiacomo M (2020) Dynamic identification and model updating of an eight-storey CLT building. Eng Struct 213:110593

    Google Scholar 

  21. Nazori A, Behmanesh I, Yousefianmoghadam S, Moaveni B, Stavridisc A (2017) Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building. Eng Struct 151:540–553

    Google Scholar 

  22. Tiachacht S, Bouazzouni A, Khatir S, Wahab MA, Behtani A, Capozucca R (2018) Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm. Eng Struct 177:421–430

    Google Scholar 

  23. Yang X-S (2018) Optimization techniques and applications with examples. Wiley, Hoboken

    Google Scholar 

  24. Luna F, Isasi P (2012) Multi-objective metaheuristics for multi-disciplinary engineering applications. Eng Optim 44(3):241–242

    Google Scholar 

  25. Guo H, Li H, Xiong J, Yu M (2019) Indoor positioning system based on particle swarm optimization algorithm. Measurement 134:908–913

    Google Scholar 

  26. Chen B, Nagarajaiah S (2011) Observer-based structural damage detection using genetic algorithm. Struct Control Health Monit 20(4):520–531

    Google Scholar 

  27. Mora C, Davison C, Wild J, Walker M (2004) Magnetoreception and its trigeminal mediation in the homing pigeon. Nature 432(7016):508–511

    Google Scholar 

  28. Sims DW, Humphries NE, Bradford RW, Bruce BD (2012) Le´vy flight and Brownian search patterns of a free-ranging predator reflect different prey field characteristics. J Anim Ecol 81:432–442

    Google Scholar 

  29. Brown C, Liebovitch LS, Glendon R (2007) L´evy flights in Dobe Ju/’hoansi foraging patterns. Hum Ecol 35:129–138

    Google Scholar 

  30. Barthelemy P, Bertolotti J, Wiersma DS (2008) A Le’vy flight for light. Nature 453(7194):495–498

    Google Scholar 

  31. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numerical Optim 1(4):330–343

    Google Scholar 

  32. Mirjalili S (2015) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073

    Google Scholar 

  33. Brown R (2020) nearestneighbour.m (https://www.mathworks.com/matlabcentral/fileexchange/12574-nearestneighbour-m). MATLAB central file exchange

  34. Yang XS (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. Springer, Berlin, Heidelberg, pp 240–249

    Google Scholar 

  35. Roy S, Kundu CK (2020) State-of-the-art review on the use of optimization algorithms in steel truss. Int J Sci Technol Res 9(3):160–165

    Google Scholar 

  36. Rahman CM, Rashid TA (2019) Dragonfly algorithm and its applications in applied science survey. Comput Intell Neurosci. https://doi.org/10.1155/2019/9293617

    Article  Google Scholar 

  37. Abdel-Basset M, Shawky LA (2018) Flower pollination algorithm: a comprehensive review. Artif Intell Rev 52(4):2533–2557

    Google Scholar 

  38. Reddy DP, Reddy VCV, Manohar G (2018) Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. J Electr Syst Inf Technol 5(3):663–680

    Google Scholar 

  39. Johnson EA, Lam HF, Ktafygiotis LS, Beck JL (2004) Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. J Eng Mech 130(1):3–15

    Google Scholar 

  40. Das S, Saha P (2018) Structural health monitoring techniques implemented on IASC–ASCE benchmark problem: a review. J Civ Struct Heal Monit 8(4):689–718

    Google Scholar 

  41. Das S, Saha P, Satapathy SC, Jena JJ (2020) Social group optimization algorithm for civil engineering structural health monitoring. Eng Optim. https://doi.org/10.1080/0305215X.2020.1808974

    Article  Google Scholar 

  42. Das S, Saha P (2021) Performance of swarm intelligence based chaotic meta-heuristic algorithms in civil structural health monitoring. Measurement 169:108533

    Google Scholar 

  43. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. IEEE swarm intelligence symposium. 68–75

  44. Jamil M, Yang X-S (2013) A literature survey og benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194

    Google Scholar 

  45. Islam MM, Shareef H, Mohamed A, Wahyudie A (2016) A binary variant of lightning search algorithm: BLSA. Soft Comput 21:2971–2990

    Google Scholar 

  46. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  47. Arora JS (2004) Introduction to optimum design. Academic Press, Cambridge

    Google Scholar 

  48. Coello CAC, Mezura EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Google Scholar 

  49. Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287

    MathSciNet  Google Scholar 

  50. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    Google Scholar 

  51. Sadollah A, Sayyaadi H, Yadav A (2018) A dynamic metaheuristic optimization model inspired by biological nervous systems: neural network algorithm. Appl Soft Comput 71:747–782

    Google Scholar 

  52. Hassiotis S, Jeong GD (1993) Assessment of structural damage from natural frequencies measurements. Comput Struct 49(4):679–691

    Google Scholar 

  53. Hassiotis S (2000) Identification of damage using natural frequencies and Markov parameters. Comput Struct 47:365–373

    Google Scholar 

  54. Das S, Saha P (2018) A review of some advanced sensors used for health diagnosis of civil engineering structures. Measurement 129:68–90

    Google Scholar 

  55. Kaveh A, Dadras A (2017) Structural damage identification using an enhanced thermal exchange optimization algorithm. Eng Optim. https://doi.org/10.1080/0305215X.2017.1318872

    Article  Google Scholar 

  56. Yuen KV, Au SK, Beck JL (2004) Two-stage structural health monitoring approach for phase i benchmark studies. J Eng Mech 130(1):16–33

    Google Scholar 

  57. Majumdar A, Maiti DK, Maity D (2012) Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Appl Math Comput 2012(218):9759–9772

    Google Scholar 

  58. Alkayem NF, Cao M (2017) Damage identification in three-dimensional structures using single-objective evolutionary algorithms and finite element model updating: evaluation and comparison. Eng Optim 50(10):1695–1714

    Google Scholar 

  59. Chandrupatla TR, Belegundu AD (eds) (2002) Introduction to finite elements in engineering [Indian Edition], 3rd edn. PHI Learning Private Limited, New Delhi

    Google Scholar 

  60. MATLAB (2016) Version 9.0.0.341360 (2016a). Mathwork

  61. Hosseinzadeh AZ, Amiri GG, Abyaneh MJ, Razzaghi SAS, Hamzehkolaei AG (2020) Baseline updating method for structural damage identification using modal residual force and grey wolf optimization. Eng Optim 52(4):549–566

    Google Scholar 

  62. Mohan SC, Maiti DK, Maity D (2013) Structural damage assessment using FRF employing particle swarm optimization. Appl Math Comput 2013(213):10387–10400

    MathSciNet  Google Scholar 

  63. Majumdar A, Nanda B, Maiti DK, Maity D (2014) Structural damage detection based on modal parameters using continuous ant colony optimization. Adv Civil Eng. https://doi.org/10.1155/2014/174185

    Article  Google Scholar 

  64. Das S, Saha P (2020) Performance of hybrid decomposition algorithm under heavy noise condition for health monitoring of structure. J Civ Struct Heal Monit. https://doi.org/10.1007/s13349-020-00412-5

    Article  Google Scholar 

  65. Hosseinzadeh AZ, Amiri GG, Abyaneh MJ, Razzaghi SAS, Hamzehkolaei AG (2019) Baseline updating method for structural damage identification using modal residual force and grey wolf optimization. Eng Optim. https://doi.org/10.1080/0305215X.2019.1593400

    Article  Google Scholar 

  66. Mirjalili S (2020) Ant lion optimizer (ALO). MATLAB central file exchange

  67. Xu HJ, Liu JK, Lu ZR (2016) Structural damage identification based on cuckoo search algorithm. Adv Struct Eng. https://doi.org/10.1177/1369433216630128

    Article  Google Scholar 

  68. Hosseinzadeh AZ, Amiri GG, Koo KY (2016) Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix. Eng Optim 48(4):543–561

    Google Scholar 

  69. Yang XS (2020) Cuckoo search (CS) Algorithm. MATLAB central file exchange. (https://www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm)

  70. Khatir S, Dekemele K, Loccufier M, Khatir T, Wahab MA (2018) Crack identification method in beam-like structures using changes in experimentally measured frequencies and particle swarm optimization. CR Mec 346(2):110–120

    Google Scholar 

  71. Tran-Ngoc H, Khatir S, DeRoeck G, Bui-Tien T, Nguyen-Ngoc L, Wahab MA (2018) Model updating for Nam O bridge using particle swarm optimization algorithm and genetic algorithm. Sensors 18:4131

    Google Scholar 

  72. Mirjalili S (2020) A simple implementation of particle swarm optimization (PSO) Algorithm. MATLAB central file exchange

  73. Khatir S, Belaidi I, Serra R, Wahab MA, Khatir T (2016) Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm. J Vibroeng 18(1):202–213

    Google Scholar 

  74. Gupta A (2020) BAT optimization Algorithm MATLAB central file exchange

Download references

Author information

Authors and Affiliations

  1. C.V. Raman Global University, Bhubaneswar, Odisha, India

    Swagato Das

  2. KIIT, Deemed to Be University, Bhubaneswar, Odisha, India

    Purnachandra Saha

Authors
  1. Swagato Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Purnachandra Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swagato Das.

Ethics declarations

Conflict of interest

The authors confirm that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 23 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, S., Saha, P. A Novel Pigeon Nesting Algorithm Based on the Nesting Behaviour of Pigeon for Health Monitoring of Structure. J. Vib. Eng. Technol. 12, 3265–3287 (2024). https://doi.org/10.1007/s42417-023-01043-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-023-01043-y

Keywords

Search

Navigation