SCIENCE CHINA Information Sciences, Volume 62 , Issue 7 : 070207(2019) https://doi.org/10.1007/s11432-018-9753-5

Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

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  • ReceivedAug 20, 2018
  • AcceptedNov 30, 2018
  • PublishedJun 6, 2019


The pigeon-inspired optimization (PIO) algorithm is a newly presented swarm intelligence optimization algorithm inspired by the homing behavior of pigeons. AlthoughPIO has demonstrated effectiveness and superiority in numerous fields, particularly in practicalengineering optimization, there have been few results concerning the theoretical foundations of PIO. This paper conductsconvergence analysis of basic PIO in a continuous search space in two aspects. First, we analyze the convergence of each pigeon'sexpected position using a difference equation and prove that the average position of each pigeon inthe swarm will converge to the same value. To further study the stochastic globalconvergence property of the pigeon swarm, we apply the martingale theory to investigatethe basic PIO swarm sequence, and achieve a sufficient condition to guarantee global convergenceof the basic PIO. Our theoretical analysis shows that this convergence depends upon the accumulation of the minimum probability with which the pigeon swarm jumps to the global-optimal region at each iteration. The mathematical methods proposed in this study, particularly the martingale technique, also provide a new effective approach for the theoretical analysis of bio-inspired algorithms in continuous optimization.


This work was supported by Natural Science Foundation of China-Guangdong Joint Fund (Grant No. U1501254), National Natural Science Foundation of China (Grant Nos. 61772225, 61876207), Guangdong Natural Science Funds for Distinguished Young Scholar (Grant No. 2014A030306050), the Ministry of Education - China Mobile Research Funds (Grant No. MCM20160206), Guangdong High-level Personnel of Special Support Program (Grant No. 2014TQ01X664), International Cooperation Project of Guangzhou (Grant No. 201807010047), and Science and Technology Program of Guangzhou (Grant Nos. 201804010276, 201707010227, 201707010228).


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    Algorithm 1 Basic pigeon-inspired optimization (PIO) algorithm

    3. Landmark operations $t=N{c_{1\max~}}+1$ to $N{c_{2\max~}}$ Rank all pigeon individuals according to their fitness values; ${N_p}(t)~=~{\mathop{\rm~ceil}\nolimits}~(~{\frac{{{N_p}(t~-~1)}}{2}}~)$;

    Keep ${N_p}(t)$ individuals with better fitness values and abandon the others; Calculate ${{\boldsymbol~X}_C}(t)$ and update ${{\boldsymbol~x}_k}(t)$, $k~=~1,~\ldots,~{N_p}$ according to Eqs. (4) and (5); Evaluate ${{\boldsymbol~x}_k}(t)$, $k~=~1,~\ldots,~{N_p}$ and update ${{\boldsymbol~P}_g}(t)$;

    4. Output ${{\boldsymbol~P}_g}(N{c_{2\max~}})$ is output as the global optimum.

    Require:${N_p}$: number of individuals in a pigeon swarm. $D$: dimension of the search space. $R$: the map and compass factor. $N{c_{1\max~}}$: maximum number of generations for which the map-and-compass operation is performed. $N{c_{2\max~}}$: maximum number of generations for which the landmark operation is performed.

    Output: ${{\boldsymbol~P}_g}$: the global best position. 1. Initialization

    Set initial values for $N{c_{1\max~}}$, $N{c_{2\max~}}$, ${N_p}$, $D$, and $R$.

    Set initial position ${{\boldsymbol~x}_k}$ and velocity ${{\boldsymbol~v}_k}$ for each pigeon, $k~=~1,~\ldots,~{N_p}$.2. Map and compass operations

    for $t=1$ to $N{c_{1\max~}}$

    for $k=1$ to ${N_p}$

    Calculate ${{\boldsymbol~v}_k}(t)$ and ${{\boldsymbol~x}_k}(t)$ according to Eqs. (1) and (2);

    end for

    Evaluate ${{\boldsymbol~x}_k}(t)$, $k~=~1,~\ldots~,~{N_p}$ and update ${{\boldsymbol~P}_g}(t)$;

    end for

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