Abstract
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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References
Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Massachusetts: Addison-Wesley Professional, 1989
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, Perth, 1995. 1942–1948
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
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
Duan H, Luo Q. New progresses in swarm intelligence-based computation. Int J Bio-Inspired Comput, 2015, 7: 26–35
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
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
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
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
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
West D B. Introduction to Graph Theory. 2nd ed. New Jersey: Prentice Hall, 2001
Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440–442
Nagy M, Ákos Z, Biro D, et al. Hierarchical group dynamics in pigeon flocks. Nature, 2010, 464: 890–893
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
Cavagna A, Cimarelli A, Giardina I, et al. Scale-free correlations in starling flocks. Proc Natl Acad Sci USA, 2010, 107: 11865–11870
Liu C, Du W B, Wang W X. Particle swarm optimization with scale-free interactions. PLoS ONE, 2014, 9: e97822
Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput, 2004, 8: 204–210
Albert R, Barabási A L. Statistical mechanics of complex networks. Rev Mod Phys, 2002, 74: 47–97
Gao Y, Du W B, Yan G. Selectively-informed particle swarm optimization. Sci Rep, 2015, 5: 9295
Yao X, Liu Y, Lin G M. Evolutionary programming made faster. IEEE Trans Evol Comput, 1999, 3: 82–102
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
Acknowledgements
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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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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DOI: https://doi.org/10.1007/s11432-018-9713-7