Heterogeneous pigeon-inspired optimization


Pigeon-inspired optimization (PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator, and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally, which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity—HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of “exploitation” and “exploration”, so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

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  1. [1]

    Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Massachusetts: Addison-Wesley Professional, 1989

    Google Scholar 

  2. [2]

    Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, Perth, 1995. 1942–1948

    Google Scholar 

  3. [3]

    Liang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput, 2006, 10: 281–295

    Article  Google Scholar 

  4. [4]

    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

    MathSciNet  Article  Google Scholar 

  5. [5]

    Duan H, Luo Q. New progresses in swarm intelligence-based computation. Int J Bio-Inspired Comput, 2015, 7: 26–35

    Article  Google Scholar 

  6. [6]

    Duan H, Wang X. 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 

  7. [7]

    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

    Article  Google Scholar 

  8. [8]

    Li Z, Liu J, Wu K. A Multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Trans Cybern, 2018, 48: 1963–1976

    Article  Google Scholar 

  9. [9]

    Wang S, Liu J. A multi-objective evolutionary algorithm for promoting the emergence of cooperation and controllable robustness on directed networks. IEEE Trans Netw Sci Eng, 2018, 5: 92–100

    MathSciNet  Article  Google Scholar 

  10. [10]

    Xin L, Xian N. Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV. Sci China Technol Sci, 2017, 60: 1577–1584

    Article  Google Scholar 

  11. [11]

    West D B. Introduction to Graph Theory. 2nd ed. New Jersey: Prentice Hall, 2001

    Google Scholar 

  12. [12]

    Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440–442

    Article  MATH  Google Scholar 

  13. [13]

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

    Article  Google Scholar 

  14. [14]

    Couzin I D, Krause J, Franks N R, et al. Effective leadership and decision-making in animal groups on the move. Nature, 2005, 433: 513–516

    Article  Google Scholar 

  15. [15]

    Cavagna A, Cimarelli A, Giardina I, et al. Scale-free correlations in starling flocks. Proc Natl Acad Sci USA, 2010, 107: 11865–11870

    Article  Google Scholar 

  16. [16]

    Liu C, Du W B, Wang W X. Particle swarm optimization with scale-free interactions. PLoS ONE, 2014, 9: e97822

    Article  Google Scholar 

  17. [17]

    Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput, 2004, 8: 204–210

    Article  Google Scholar 

  18. [18]

    Albert R, Barabási A L. Statistical mechanics of complex networks. Rev Mod Phys, 2002, 74: 47–97

    MathSciNet  Article  MATH  Google Scholar 

  19. [19]

    Gao Y, Du W B, Yan G. Selectively-informed particle swarm optimization. Sci Rep, 2015, 5: 9295

    Article  Google Scholar 

  20. [20]

    Yao X, Liu Y, Lin G M. Evolutionary programming made faster. IEEE Trans Evol Comput, 1999, 3: 82–102

    Article  Google Scholar 

  21. [21]

    Liang J J, Suganthan P N, Deb K, et al. Novel composition test functions for numerical global optimization. In: Proceedings of 2005 IEEE Swarm Intelligence Symposium, Pasadena, 2005. 68–75

    Google Scholar 

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This work was supported by National Key Research and Development Program of China (Grant No. 2016YFB1200100), National Natural Science Foundation of China (Grant Nos. 61425014, 61521091, 91538204, 61671031, 61722102).

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Correspondence to Xi Zhu.

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Wang, H., Zhang, Z., Dai, Z. et al. Heterogeneous pigeon-inspired optimization. Sci. China Inf. Sci. 62, 70205 (2019). https://doi.org/10.1007/s11432-018-9713-7

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Key words

  • heuristic optimization
  • pigeon-inspired optimization
  • particle heterogeneity
  • network-based topology
  • scale-free network
  • selective-informed learning