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