Abstract
Clustering is a popular data mining technique widely used in many fields. Recently, researches on swarm intelligence-based and bionic approaches for handing these clustering problems have made significant achievements. In this contribution, a bionic algorithm inspired by the intrinsic adaptability of plant root foraging behavior is designed and developed for data clustering. Especially, the foraging behaviors of plant root involve elongation, branching, and tropism based on the auxin-regulated mechanism. By incorporating the self-adaptive population-varying mechanism and self-adaptive root growth strategy, a new root system growth algorithm with adaptive population variation (RSGA_APV) is designed based on the root foraging and auxin-based regulation of the root system. The comprehensive experimental analysis is implemented that the proposed RSGA_APV is benchmarked against several state-of-the-art reference algorithms on a set of scalable benchmarks. Then, RSGA_APV is applied to resolve data clustering problems. Computational results verify the effectiveness and efficiency of our proposed algorithm.
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Acknowledgments
This work is supported by the National Science Foundation for Distinguished Young Scholars of China under Grant No. 61225012 and No. 71325002; National Natural Science Foundation of China under Grant No. 61503373, 61502318 and No. 61572123; Natural Science Foundation of Liaoning Province under Grand 2015020002, Specialized Research Fund of the Doctoral Program of Higher Education for the Priority Development Areas under Grant No. 20120042130003, and Liaoning BaiQianWan Talents Program under Grant No. 2013921068.
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Ma, L., Wang, X., Yu, R. et al. Biomimicry of plant root growth using bioinspired foraging model for data clustering. Neural Comput & Applic 29, 819–836 (2018). https://doi.org/10.1007/s00521-016-2480-8
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DOI: https://doi.org/10.1007/s00521-016-2480-8