logo

SCIENCE CHINA Information Sciences, Volume 62, Issue 7: 070201(2019) https://doi.org/10.1007/s11432-018-9752-9

Advancements in pigeon-inspired optimization and its variants

More info
  • ReceivedAug 17, 2018
  • AcceptedNov 30, 2018
  • PublishedApr 23, 2019

Abstract

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.


Acknowledgment

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).


References

[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] 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] Wiltschko W, Wiltschko R. Homing pigeons as a model for avian navigation?. J Avian Biol, 2017, 48: 66-74 CrossRef Google Scholar

[4] Guilford T, Roberts S, Biro D. Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models.. J Theor Biol, 2004, 227: 25-38 CrossRef PubMed Google Scholar

[5] Duan H, Qiao P. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int Jnl Intel Comp Cyber, 2014, 7: 24-37 CrossRef Google Scholar

[6] Whiten A. Operant Study of Sun Altitude and Pigeon Navigation. Nature, 1972, 237: 405-406 CrossRef ADS Google Scholar

[7] Keeton W T. The Mystery of Pigeon Homing. Sci Am, 1974, 231: 96-107 CrossRef ADS Google Scholar

[8] Walcott C. Magnetic orientation in homing pigeons. IEEE Trans Magn, 1980, 16: 1008-1013 CrossRef ADS Google Scholar

[9] Mora C V, Davison M, Martin Wild J. Magnetoreception and its trigeminal mediation in the homing pigeon. Nature, 2004, 432: 508-511 CrossRef PubMed ADS Google Scholar

[10] Nie$\beta$ner C, Denzau S, Peichl L. Magnetoreception in birds: I. Immunohistochemical studies concerning the cryptochrome cycle.. J Exp Biol, 2014, 217: 4221-4224 CrossRef PubMed Google Scholar

[11] Wiltschko R, Gehring D, Denzau S. Magnetoreception in birds: II. Behavioural experiments concerning the cryptochrome cycle.. J Exp Biol, 2014, 217: 4225-4228 CrossRef PubMed Google Scholar

[12] Dell'Ariccia G, Dell'Omo G, Wolfer D P. Flock flying improves pigeons' homing: GPS track analysis of individual flyers versus small groups. Animal Behaviour, 2008, 76: 1165-1172 CrossRef Google Scholar

[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] Vyssotski A L, Dell'Omo G, Dell'Ariccia G. EEG responses to visual landmarks in flying pigeons.. Curr Biol, 2009, 19: 1159-1166 CrossRef PubMed Google Scholar

[15] Hagstrum J T. Atmospheric propagation modeling indicates homing pigeons use loft-specific infrasonic 'map' cues.. J Exp Biol, 2013, 216: 687-699 CrossRef PubMed Google Scholar

[16] Blaser N, Guskov S I, Entin V A. Gravity anomalies without geomagnetic disturbances interfere with pigeon homing--a GPS tracking study.. J Exp Biol, 2014, 217: 4057-4067 CrossRef PubMed Google Scholar

[17] Zhang Z, Wu T, P?un A. Universal enzymatic numerical P systems with small number of enzymatic variables. Sci China Inf Sci, 2018, 61: 092103 CrossRef Google Scholar

[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] 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] Lei X, Ding Y, Wu F X. Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm. Sci China Inf Sci, 2016, 59: 070103 CrossRef Google Scholar

[21] Rajendran S, M. Sankareswaran U. A Novel Pigeon Inspired Optimization in Ovarian Cyst Detection. CMIR, 2016, 12: 43-49 CrossRef Google Scholar

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

[23] Jia Z, Sahmoudi M. A type of collective detection scheme with improved pigeon-inspired optimization. Int Jnl Intel Comp Cyber, 2016, 9: 105-123 CrossRef Google Scholar

[24] Chen S, Duan H. Fast image matching via multi-scale Gaussian mutation pigeon-inspired optimization for low cost quadrotor. Aircraft Eng Aerospace Tech, 2017, 89: 777-790 CrossRef Google Scholar

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

[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] 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] 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] Xian N, Chen Z. A Quantum-behaved Pigeon-Inspired Optimization approach to Explicit Nonlinear Model Predictive Controller for quadrotor. Int Jnl Intel Comp Cyber, 2018, 11: 47-63 CrossRef Google Scholar

[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 CrossRef Google Scholar

[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] Dou R, Duan H. Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system. Aerospace Sci Tech, 2017, 61: 11-20 CrossRef Google Scholar

[34] Zhang D, Duan H, Yang Y. Active disturbance rejection control for small unmanned helicopters via Levy flight-based pigeon-inspired optimization. Aircraft Eng Aerospace Tech, 2017, 89: 946-952 CrossRef Google Scholar

[35] Zhang D, Duan H. Identification for a reentry vehicle via Levy flight-based pigeon-inspired optimization. Proc Institution Mech Engineers Part G-J Aerospace Eng, 2018, 232: 626-637 CrossRef Google Scholar

[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 Levy flight pigeon-inspired optimization. Sci Sin-Tech, 2018, 48: 161-169 CrossRef Google Scholar

[37] Jiang B, Li C, Liu M. Progress in biomolecular cryo-electron microscopy. Sci Sin-Chim, 2018, 48: 277-281 CrossRef Google Scholar

[38] Yang Z, Duan H, Fan Y. Automatic Carrier Landing System multilayer parameter design based on Cauchy Mutation Pigeon-Inspired Optimization. Aerospace Sci Tech, 2018, 79: 518-530 CrossRef Google Scholar

[39] Li C, Duan H. Target detection approach for UAVs via improved Pigeon-inspired Optimization and Edge Potential Function. Aerospace Sci Tech, 2014, 39: 352-360 CrossRef Google Scholar

[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] Zhang B, Duan H. 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 CrossRef PubMed Google Scholar

[42] Zhang S, Duan H. Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration. Chin J Aeronautics, 2015, 28: 200-205 CrossRef Google Scholar

[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] Deng Y M, Zhu W R, 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 CrossRef Google Scholar

[45] Duan H, Wang X. Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration.. IEEE Trans Neural Netw Learning Syst, 2016, 27: 2413-2425 CrossRef PubMed Google Scholar

[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] 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] 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] Sushnigdha G, Joshi A. Re-entry trajectory optimization using pigeon inspired optimization based control profiles. Adv Space Res, 2018, 62: 3170-3186 CrossRef ADS Google Scholar

[50] Hua B, Liu R P, Wu Y H. Intelligent attitude planning algorithm based on the characteristics of low radar cross section characteristics of microsatellites under complex constraints. Proc Institution Mech Engineers Part G-J Aerospace Eng, 2019, 233: 4-21 CrossRef Google Scholar

[51] Xu X, Deng Y. UAV Power Component-DC Brushless Motor Design With Merging Adjacent-Disturbances and Integrated-Dispatching Pigeon-Inspired Optimization. IEEE Trans Magn, 2018, 54: 1-7 CrossRef ADS Google Scholar

[52] Sun Y, Duan H, Xian N. Fractional-order controllers optimized via heterogeneous comprehensive learning pigeon-inspired optimization for autonomous aerial refueling hose-drogue system. Aerospace Sci Tech, 2018, 81: 1-13 CrossRef Google Scholar

[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] Li S, Deng Y. Quantum-entanglement pigeon-inspired optimization for unmanned aerial vehicle path planning. Aircraft Eng Aerospace Tech, 2019, 91: 171-181 CrossRef Google Scholar

[55] Deng Y, Duan H. Control parameter design for automatic carrier landing system via pigeon-inspired optimization. NOnlinear Dyn, 2016, 85: 97-106 CrossRef Google Scholar

[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 CrossRef Google Scholar

[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] Zhang D, Duan H. Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning. Neurocomputing, 2018, 313: 229-246 CrossRef Google Scholar

[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 CrossRef Google Scholar

[60] Qiu H X, Duan H B. A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inform Sci, 2018. Doi: 10.1016/j.ins.2018.06.061. Google Scholar

[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] 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] 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] Nagy M, ákos Z, Biro D. Hierarchical group dynamics in pigeon flocks. Nature, 2010, 464: 890-893 CrossRef PubMed ADS arXiv Google Scholar

[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 CrossRef PubMed ADS arXiv Google Scholar

[66] Scarf D, Boy K, Uber Reinert A. Orthographic processing in pigeons (Columba livia).. Proc Natl Acad Sci USA, 2016, 113: 11272-11276 CrossRef PubMed Google Scholar

  • Figure 1

    (Color online) Pigeon-inspired optimization process.

  • Figure 2

    (Color online) Development of pigeon-inspired optimization by adopting mature concepts.

  • Table 1   Existing variants of pigeon-inspired optimization
    Classification Author (year) Variant Modification
    Component Hao et al. (2014) [17] Modify map and compass factor using fractional calculus
    replacement Jia and Sahmoudi (2016) [18] ECPIO Modify map and compass factor using population
    dispersion degree
    Chen and Duan (2017) [19] MGMPIO Modify map and compass factor using variable parameter
    mechanism
    Lin et al. (2018) [20] AWPIO Add a nonlinear dynamic inertia weight coefficient to map
    and compass operator
    Tao and Li (2018) [21] CPIO Add a cognitive factor and a compressive factor to map and
    compass and landmark operators, respectively
    Zhou et al. (2017) [22] MAIPIO Replace center and global best with personal bests' weighted
    average and anterior neighbor's personal best
    Li and Duan (2014) [23], BQPIO Replace map and compass operator with quantum mutation
    Zhang and Duan (2015) [24], operator
    Xian and Chen (2018) [25]
    Pei et al. (2017) [26] QCPIO Replace landmark operator with quantum mutation operator
    Liu et al. (2016) [27], LFPIO Replace map and compass operator with Lévy-flight-based
    Dou and Duan (2017) [28], search operator
    Zhang et al. (2017, 2018) [29,30],
    Yang et al. (2018) [31]
    Duan and Yang (2018) [32], CMPIO Replace center and global best with Cauchy variants
    Yang et al. (2018) [33]
    Operation Hao et al. (2014) [17] Add crossover operation
    addition Li and Duan (2014) [34] SAPIO Add simulated annealing operation
    Sun and Duan (2014) [35], PPPIO Add prey-predator operation
    Zhang and Duan (2017) [36]
    Zhang and Duan (2015) [37], GPIO Add Gaussian mutation operation
    Hu and Duan (2016) [38]
    Chen and Duan (2017) [19] MGMPIO Add multi-scale Gaussian mutation operation
    Deng et al. (2016) [39] HMCPIO Add communication operation
    Duan and Wang (2016) [40] OPIO Add orthogonal initialization
    Cheng et al. (2016) [41] SOPIO Add sub-space division orthogonal initialization
    Pei et al. (2017) [26] QCPIO Add chaotic local search operation
    Zhou et al. (2017) [22] MAIPIO Add competition operation
    Jiang et al. (2017) [42] Add threat heuristic operation
    Sushnigdha and Joshi Add constraints handling operation
    (2017, 2018) [43,44]
    Hua et al. (2019) [45] Add personal best learning operation
    Xu and Deng (2018) [46] ADID-PIO Add adjacent-disturbance operation
    Sun et al. (2018) [47] HCLPIO Add heterogeneous comprehensive learning operation
    Khan et al. (2018) [48] HPIO Add new harmony improvisation operation
    Li and Deng (2019) [49] QEPIO Add Quantum entanglement combing operation
    Structure Li and Duan (2014) [34] SAPIO Conduct one of the two operators probabilistically
    adjustment Deng and Duan (2016) [50]
    Duan et al. (2015) [51] PEPIO Combine the two operators
    Tao and Li (2018) [21] CPIO Conduct one of the two operators crosswise
    Duan et al. (2015) [51], PEPIO Divide pigeons into predators and escapees
    Mohamed et al. (2017) [52]
    Xu and Deng (2018) [46] ADID-PIO Divide pigeons into the top, medium and inferior
    Zhang and Duan (2018) [53] SCPIO Divide pigeons into different ranks
    Application Qiu and Duan (2015, 2018) [54,55], MPIO Extend to multi-objective optimization
    expansion Deng et al. (2017) [56]
    Shan et al. (2017) [57] DKPIO Extend to discrete optimization
    Bolaji et al. (2018) [58] BPIO Extend to combinatorial optimization

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1