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Improved binary pigeon-inspired optimization and its application for feature selection

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Abstract

The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.

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Acknowledgements

This paper was supported by National Natural Science Foundation of China with grant number NSF 61872085, Natural Science Foundation of Fujian Province with grant number 2018J01638, and project 2018Y3001 of Fujian Provincial Department of Science and Technology.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Jeng-Shyang Pan, Ai-Qing Tian & Shu-Chuan Chu

  2. College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, SA, 5042, Australia

    Shu-Chuan Chu

  3. Harbin Institute of Technology, Harbin, China

    Jun-Bao Li

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Pan, JS., Tian, AQ., Chu, SC. et al. Improved binary pigeon-inspired optimization and its application for feature selection. Appl Intell 51, 8661–8679 (2021). https://doi.org/10.1007/s10489-021-02302-9

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