Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Abstract

Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.