ABSTRACT This study sheds light on a fundamental problem in image segmentation known as multilevel image thresholding. With the rapid growth of artificial intelligence applications that rely on image processing such as medical imaging, remote sensing, and pattern recognition the demand for more effective techniques has become increasingly urgent. Traditional methods suffer from significant limitations, including slow convergence and premature convergence to local optima, particularly when applied to complex or high‐dimensional images. To address these challenges, this study proposes a novel approach based on metaheuristic algorithms, specifically elephant herding optimization (EHO) and symbiotic organism search (SOS). Although these algorithms have shown promising results due to their adaptability and exploratory capabilities, they still face performance bottlenecks resulting from insufficient diversity in the search process. To overcome these limitations, enhanced variants of EHO and SOS are introduced by integrating opposition‐based learning (OBL) and chaos theory to achieve a better balance between exploration and exploitation. These improved algorithms, OCEHO and OCSOS, are applied to the multilevel thresholding problem using Otsu's variance, Kapur's entropy and Masi's entropy as objective functions. The proposed methods are evaluated on 75 standard benchmark images, with segmentation quality evaluated using PSNR, SSIM, and FSIM metrics. Experimental results on 75 standard benchmark images show that the proposed OCEHO algorithm achieves PSNR values up to 37.51 dB, SSIM scores of 0.972, and FSIM values of 0.986, significantly outperforming baseline and hybrid variants. Furthermore, statistical analyzes, including the Wilcoxon rank sum test, confirm the superior stability and convergence speed of OCEHO over its counterparts. These results validate the effectiveness and robustness of the proposed approach for high‐quality image segmentation.
Chakraborty et al. (Fri,) studied this question.