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



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