Advancements in pigeon-inspired optimization and its variants


The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization (PIO), which is a novel mono-objective continuous optimization algorithm, is inspired by the hidden mechanism behind the remarkable navigation capacity of homing pigeons. Since their development, PIO and its variants have been widely applied to various fields ranging from combinatorial optimization to multi-objective optimization in many areas, such as aerospace, medicine, and energy. This study aims to review the modifications of PIO from four aspects of improvement measures, namely, component replacement, operation addition, structure adjustment, and application expansion. It also summarizes the problems of existing research and plots the course of future effort.

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  1. 1

    Blechman A D. Pigeons: the Fascinating Saga of the World’s Most Revered and Reviled Bird. New York: Grove Press, 2007

    Google Scholar 

  2. 2

    Katzung Hokanson B R. Saving grace on feathered wings: homing pigeons in the first world war. Gettysburg Hist J, 2018, 17: 7

    Google Scholar 

  3. 3

    Wiltschko W, Wiltschko R. Homing pigeons as a model for avian navigation? J Avian Biol, 2017, 48: 66–74

    Article  MATH  Google Scholar 

  4. 4

    Guilford T, Roberts S, Biro D, et al. Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models. J Theory Biol, 2004, 227: 25–38

    MathSciNet  Article  Google Scholar 

  5. 5

    Duan H B, Qiao P X. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intel Comput Cyber, 2014, 7: 24–37

    MathSciNet  Article  Google Scholar 

  6. 6

    Whiten A. Operant study of sun altitude and pigeon navigation. Nature, 1972, 237: 405–406

    Article  Google Scholar 

  7. 7

    Keeton W T. The mystery of pigeon homing. Sci Am, 1974, 231: 96–107

    Article  Google Scholar 

  8. 8

    Walcott C. Magnetic orientation in homing pigeons. IEEE Trans Magn, 1980, 16: 1008–1013

    Article  Google Scholar 

  9. 9

    Mora C V, Davison M, Wild J M, et al. Magnetoreception and its trigeminal mediation in the homing pigeon. Nature, 2004, 432: 508–511

    Article  Google Scholar 

  10. 10

    Niefiner C, Denzau S, Peichl L, et al. Magnetoreception in birds: I. Immunohistochemical studies concerning the cryptochrome cycle. J Exp Biol, 2014, 217: 4221–4224

    Google Scholar 

  11. 11

    Wiltschko R, Gehring D, Denzau S, et al. Magnetoreception in birds: II. Behavioural experiments concerning the cryptochrome cycle. J Exp Biol, 2014, 217: 4225–4228

    Google Scholar 

  12. 12

    Dell’Ariccia G, Dell’Omo G, Wolfer D P, et al. Flock flying improves pigeons’ homing: GPS track analysis of individual flyers versus small groups. Animal Behav, 2008, 76: 1165–1172

    Article  Google Scholar 

  13. 13

    Biro D, Guilford T, Dell’Omo G, et al. How the viewing of familiar landscapes prior to release allows pigeons to home faster: evidence from GPS tracking. J Exp Biol, 2002, 205: 3833–3844

    Google Scholar 

  14. 14

    Vyssotski A L, Dell’Omo G, Dell’Ariccia G, et al. EEG responses to visual landmarks in flying pigeons. Curr Biol, 2009, 19: 1159–1166

    Article  Google Scholar 

  15. 15

    Hagstrum J T. Atmospheric propagation modeling indicates homing pigeons use loft-specific infrasonic ‘map’ cues. J Exp Biol, 2013, 216: 687–699

    Article  Google Scholar 

  16. 16

    Blaser N, Guskov S I, Entin V A, et al. Gravity anomalies without geomagnetic disturbances interfere with pigeon homing — a GPS tracking study. J Exp Biol, 2014, 217: 4057–4067

    Article  Google Scholar 

  17. 17

    Zhang Z Q, Wu T F, P˘aun A, et al. Universal enzymatic numerical P systems with small number of enzymatic variables. Sci China Inf Sci, 2018, 61: 092103

    MathSciNet  Article  Google Scholar 

  18. 18

    Mahesh A, Sandhu K S. Optimal sizing of a PV/Wind hybrid system using pigeon inspired optimization. In: Proceedings of the 7th Power India International Conference, Bikaner, 2016

    Google Scholar 

  19. 19

    Arshad H, Batool S, Amjad Z, et al. Pigeon inspired optimization and enhanced differential evolution using time of use tariff in smart grid. In: Proceedings of International Conference on Intelligent Networking and Collaborative Systems, Toronto, 2017. 563–575

    Google Scholar 

  20. 20

    Lei X J, Ding Y L, Wu F X. Detecting protein complexes from DPINs by density based clustering with pigeon-inspired optimization algorithm. Sci China Inf Sci, 2016, 59: 070103

    Article  Google Scholar 

  21. 21

    Rajendran S, Sankareswaran U M. A novel pigeon inspired optimization in ovarian cyst detection. Curr Med Imag Rev, 2016, 12: 43–49

    Article  Google Scholar 

  22. 22

    Hao R, Luo D L, Duan H B. Multiple UAVs mission assignment based on modified pigeon inspired optimization algorithm. In: Proceedings of the 6th IEEE Chinese Guidance, Navigation and Control Conference, Yantai, 2014. 2692–2697

    Google Scholar 

  23. 23

    Jia Z X, Sahmoudi M. A type of collective detection scheme with improved pigeon-inspired optimization. Int J Intell Comput Cyber, 2016, 9: 105–123

    Article  Google Scholar 

  24. 24

    Chen S J, Duan H B. Fast image matching via multi-scale Gaussian mutation pigeon-inspired optimization for low cost quadrotor. Aircraft Eng Aerosp Tech, 2017, 89: 777–790

    Article  Google Scholar 

  25. 25

    Lin N, Huang S M, Gong C Q. UAV path planning based on adaptive weighted pigeon-inspired optimization algorithm. Comput Simul, 2018, 35: 38–42

    Google Scholar 

  26. 26

    Tao G J, Li Z. A crossed pigeon-inspired optimization algorithm with cognitive factor. J Sichuan Univ (Nat Sci Edit), 2018, 55: 295–330

    MATH  Google Scholar 

  27. 27

    Zhou K, Jiang W Z, Chen D A, et al. Research on cooperative target assignment based on improve pigeon inspired optimization. Fire Control Command Control, 2017, 42: 84–98

    Google Scholar 

  28. 28

    Li H H, Duan H B. Bloch quantum-behaved pigeon-inspired optimization for continuous optimization problems. In: Proceedings of the 6th IEEE Chinese Guidance, Navigation and Control Conference, Yantai, 2014. 2634–2638

    Google Scholar 

  29. 29

    Zhang S J, Duan H B. Multiple UCAVs target assignment via bloch quantum-behaved pigeon-inspired optimization. In: Proceedings of the 34th Chinese Control Conference, Hangzhou, 2015. 6936–6941

    Google Scholar 

  30. 30

    Xian N, Chen Z L. A quantum-behaved pigeon-inspired optimization approach to explicit nonlinear model predictive controller for quadrotor. Int J Intell Comput Cyber, 2018, 11: 47–63

    Article  Google Scholar 

  31. 31

    Pei J Z, Su Y X, Zhang D H. Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Sci China Technol Sci, 2017, 60: 425–433

    Article  Google Scholar 

  32. 32

    Liu Z Q, Duan H B, Yang Y J, et al. Pendulum-like oscillation controller for UAV based on Lévy-flight pigeon-inspired optimization and LQR. In: Proceedings of IEEE Symposium Series on Computational Intelligence, Athens, 2016. 7850282

    Google Scholar 

  33. 33

    Dou R, Duan H B. Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system. Aerosp Sci Technol, 2017, 61: 11–20

    Article  Google Scholar 

  34. 34

    Zhang D F, Duan H B, Yang Y J. Active disturbance rejection control for small unmanned helicopters via Lévy flight-based pigeon-inspired optimization. Aircraft Eng Aerosp Tech, 2017, 89: 946–952

    Article  Google Scholar 

  35. 35

    Zhang D F, Duan H B. Identification for a reentry vehicle via Lévy flight-based pigeon-inspired optimization. Proc Inst Mech Eng Part G-J Aerosp Eng, 2018, 232: 626–637

    Article  Google Scholar 

  36. 36

    Yang Z Y, Duan H B, Fan Y M. Unmanned aerial vehicle formation controller design via the behavior mechanism in wild geese based on Lévy flight pigeon-inspired optimization. Sci Sin Technol, 2018, 48: 161–169

    Article  Google Scholar 

  37. 37

    Duan H B, Yang Z Y. Large civil aircraft receding horizon control based on Cauthy mutation pigeon inspired optimization. Sci Sin Technol, 2018, 48: 277–288

    Article  Google Scholar 

  38. 38

    Yang Z Y, Duan H B, Fan Y M, et al. Automatic carrier landing system multilayer parameter design based on Cauchy mutation pigeon-inspired optimization. Aerosp Sci Technol, 2018, 79: 518–530

    Article  Google Scholar 

  39. 39

    Li C, Duan H B. Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function. Aerosp Sci Tech, 2014, 39: 352–360

    Article  Google Scholar 

  40. 40

    Sun H, Duan H B. PID controller design based on prey-predator pigeon-inspired optimization algorithm. In: Proceedings of the 11th IEEE International Conference on Mechatronics and Automation, Tianjin, 2014. 1416–1421

    Google Scholar 

  41. 41

    Zhang B, Duan H B. Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment. IEEE/ACM Trans Comput Biol Bioinf, 2017, 14: 97–107

    Article  Google Scholar 

  42. 42

    Zhang S J, Duan H B. Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration. Chinese J Aeronaut, 2015, 28: 200–205

    Article  Google Scholar 

  43. 43

    Hu Y W, Duan H B. Gaussian entropy weight pigeon-inspired optimization for rectangular waveguide design. In: Proceedings of the 7th IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, 2016. 1951–1956

    Google Scholar 

  44. 44

    Deng Y M, Zhu WR, Duan H B. Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design. Sci China Technol Sci, 2016, 59: 1435–1441

    Article  Google Scholar 

  45. 45

    Duan H B, Wang X H. Echo state networks with orthogonal pigeon-inspired optimization for image restoration. IEEE Trans Neural Netw Learn Syst, 2016, 27: 2413–2425

    MathSciNet  Article  Google Scholar 

  46. 46

    Cheng X J, Ren L, Cui J, et al. Traffic flow prediction with improved SOPIO-SVR algorithm. In: Proceedings of the 19th Monterey Workshop on Challenges and Opportunity with Big Data, Beijing, 2016. 184–197

    Google Scholar 

  47. 47

    Jiang P P, Zhou K, Zhu Q K, et al. Route planning of armed helicopter based on pigeon-inspired optimization with threat heuristic. Electron Opt Control, 2017, 24: 56–61

    Google Scholar 

  48. 48

    Sushnigdha G, Joshi A. Re-entry trajectory design using pigeon-inspired optimization. In: Proceedings of AIAA Atmospheric Flight Mechanics Conference, Denver, 2017

    Google Scholar 

  49. 49

    Sushnigdha G, Joshi A. Re-entry trajectory optimization using pigeon inspired optimization based control profiles. Adv Space Res, 2018, 62: 3170–3186

    Article  Google Scholar 

  50. 50

    Hua B, Liu R P, Wu Y H, et al. Intelligent attitude planning algorithm based on the characteristics of low radar cross section characteristics of microsatellites under complex constraints. Proc Inst Mech Eng Part G-J Aerosp Eng, 2019, 233: 4–21

    Article  Google Scholar 

  51. 51

    Xu X B, Deng Y M. UAV power component-DC brushless motor design with merging adjacent-disturbances and integrated-dispatching pigeon-inspired optimization. IEEE Trans Magn, 2018, 54: 1–7

    Article  Google Scholar 

  52. 52

    Sun Y B, Duan H B, Xian N. Fractional-order controllers optimized via heterogeneous comprehensive learning pigeoninspired optimization for autonomous aerial refueling hose-drogue system. Aerosp Sci Tech, 2018, 81: 1–13

    Article  Google Scholar 

  53. 53

    Khan N, Javaid N, Khan M, et al. Harmony pigeon inspired optimization for appliance scheduling in smart grid. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications, Cracow, 2018. 1060–1069

    Google Scholar 

  54. 54

    Li S Q, Deng Y M. Quantum-entanglement pigeon-inspired optimization for unmanned aerial vehicle path planning. Aircraft Eng Aerosp Tech, 2019, 91: 171–181

    Article  Google Scholar 

  55. 55

    Deng Y M, Duan H B. Control parameter design for automatic carrier landing system via pigeon-inspired optimization. Nonlinear Dyn, 2016, 85: 97–106

    MathSciNet  Article  Google Scholar 

  56. 56

    Duan H B, Qiu H X, Fan Y M. Unmanned aerial vehicle close formation cooperative control based on predatory escaping pigeon-inspired optimization. Sci Sin Tech, 2015, 45: 559–572

    Google Scholar 

  57. 57

    Mohamed M S, Duan H B, Fu L. Flying vehicle longitudinal controller design via prey-predator pigeon-inspired optimization. In: Proceedings of IEEE Symposium Series on Computational Intelligence, Honolulu, 2017. 1650–1655

    Google Scholar 

  58. 58

    Zhang D F, Duan H B. Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning. Neurocomputing, 2018, 313: 229–246

    Article  Google Scholar 

  59. 59

    Qiu H X, Duan H B. Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design. Sci China Technol Sci, 2015, 58: 1915–1923

    Article  Google Scholar 

  60. 60

    Qiu H X, Duan H B. A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inform Sci, 2018. doi:

    Google Scholar 

  61. 61

    Deng X W, Shi Y Q, Li S L, et al. Multi-objective pigeon-inspired optimization localization algorithm for large-scale agricultural sensor network. J Huaihua Univ, 2017, 36: 37–40

    Google Scholar 

  62. 62

    Shan X, Wang Y, Ji Z C. Energy efficiency optimization for discrete workshop based on parametric knowledge pigeon swarm algorithm. J Syst Simul, 2017, 29: 2140–2148

    Google Scholar 

  63. 63

    Bolaji A L, Babatunde B S, Shola P B. Adaptation of binary pigeon-inspired algorithm for solving multidimensional knapsack problem. In: Proceedings of the 1st International Conference on Soft Computing: Theories and Applications, Jaipur, 2018. 743–751

    Google Scholar 

  64. 64

    Nagy M, Akos Z, Biro D, et al. Hierarchical group dynamics in pigeon flocks. Nature, 2010, 464: 890–893

    Article  Google Scholar 

  65. 65

    Williams C D, Biewener A A. Pigeons trade efficiency for stability in response to level of challenge during confined flight. Proc Natl Acad Sci USA, 2015, 112: 3392–3396

    Article  Google Scholar 

  66. 66

    Scarf D, Boy K, Reinert A U, et al. Orthographic processing in pigeons (Columba livia). Proc Natl Acad Sci USA, 2016, 113: 11272–11276

    Article  Google Scholar 

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This work was partially supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 61425008, 61333004, 91648205) and Aeronautical Science Foundation of China (Grant No. 2015ZA51013).

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Correspondence to Haibin Duan.

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Duan, H., Qiu, H. Advancements in pigeon-inspired optimization and its variants. Sci. China Inf. Sci. 62, 70201 (2019).

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  • pigeon-inspired optimization
  • homing pigeon
  • bio-inspired computing
  • map and compass operator
  • landmark operator