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

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


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.


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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  • 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
    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

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