Hierarchical Artificial Bee Colony Optimizer for Multilevel Threshold Image Segmentation

This paper presents a novel optimization algorithm, namely hierarchical artificial bee colony optimization (HABC) for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extends the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information change mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divideand-conquer approach, each subpopulation runs the original ABC method in parallel for part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC algorithm to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.