Elite levy spreading differential evolution via ABC shrink-wrap for multi-threshold segmentation of breast cancer images

Abstract

Differential Evolution (DE) is a commonly used metaheuristic algorithm in different optimization scenarios. However, the original DE suffers from stagnation and premature convergence in solving complex problems. In this study, an Artificial Bee Colony (ABC)-inspired Shrink-wrap (SW) strategy is proposed by combining the idea of a restart operator in ABC. Furthermore, an Elite Levy Spreading (ELS) strategy is proposed, inspired by the ideas of dimensional variation in cuckoo search and linear reduction of the number of flames in the moth-flame optimization. Based on these two strategies, an Enhanced DE (EDE) is proposed, where the ABC-inspired SW strategy is used to enhance the global exploration capability, and the ELS strategy is dedicated to improving the optimal solution quality even further. Based on CEC 2017 test, scalability experiments were conducted on EDE, and performance was compared with state-of-the-art and champion algorithms. The results were analyzed using the Wilcoxon signed rank, Friedman test, and post-hoc statistical tests. Finally, Image Segmentation experiments were performed with some peers on nine breast cancer images to validate the performance of EDE. The above experimental results show that EDE performs satisfactorily in optimization cases and multi-threshold segmentation of breast cancer images.