This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid\nartificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm\nthe new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search\nby combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth\noperators are guided systematically. With root-to-root communication, individuals exchange information in different efficient\ntopologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population\ndriven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well\nmaintained.The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed\nHARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational\nresults of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization\naccuracy computation efficiency.
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