A Logistic Chaotic Barnacles Mating Optimizer with Masi Entropy for Color Image Multilevel Thresholding Segmentation

Abstract

Barnacles mating optimizer (BMO) is an evolutionary algorithm that simulates the mating and reproductive behavior of barnacle population. In this article, an improved Barnacles mating optimizer based on logistic model and chaotic map (LCBMO) was proposed to produce the high-quality optimal result. Firstly, the logistic model is introduced into the native BMO to realize the automatic conversion parameters. This strategy maintains a proper relationship between exploitation and exploration. Then, the chaotic map is integrated to enhance the exploitation capability of the algorithm. After that, six variants based on LCBMO are compared to find the best algorithm on benchmark functions. Moreover, to the knowledge of the authors, there is no previous study on this algorithm for multilevel color image segmentation. LCBMO takes Masi entropy as the objective function to find the optimal threshold. By comparing different thresholds, different types of images, different optimization algorithms, and different objective functions, our proposed technique is reliable and promising in solving color image multilevel thresholding segmentation. Wilcoxon rank-sum test and Friedman test also prove that the simulation results are statistically significant.