Biomimicry of plant root growth using bioinspired foraging model for data clustering.

ROOT growth; BIOMIMICRY; DATA mining; SWARM intelligence; ALGORITHMS

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. [ABSTRACT FROM AUTHOR]

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