Multilevel thresholding image segmentation using mixed strategy improved convergence based whale optimization algorithm

Abstract

This paper presents a novel multilevel image segmentation method that leverages an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in solving various optimization problems, its performance can be limited by susceptibility to local optima. To address this challenge, a Mixed-Strategy Improved Convergence WOA (MSICWOA) is proposed, which enhances the algorithm's optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization technique. The MSICWOA is then applied alongside Otsu's crossvariance and Kapur entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark optimization functions demonstrate that MSICWOA outperforms traditional optimization methods in terms of both search accuracy and convergence speed, effectively overcoming local optima. Furthermore, image segmentation experiments on standard datasets validate the effectiveness of the MSICWOA-Kapur method in quickly and accurately identifying multilevel thresholds.