An Adaptive Bi-Mutation-Based Differential Evolution Algorithm for Multi-Threshold Image Segmentation

Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposite learning strategy is introduced into the initial population to improve the quality of the initial population; secondly, a threshold-value-based mutation strategy is proposed to balance the exploration and development capabilities of the algorithm, and the number of successfully evolved individuals is considered as a threshold value to adaptively adjust the evolution of superior and inferior individuals. Experiments demonstrate that the proposed algorithm has better performance in enhancing accuracy and speeding up the convergence.