A Hybrid Multi-Strategy Optimization Metaheuristic Algorithm for Multi-Level Thresholding Color Image Segmentation

Hybrid metaheuristic algorithms have been widely used to solve global optimization problems, making the concept of hybridization increasingly important. This study proposes a new hybrid multi-strategy metaheuristic algorithm named COSGO, which combines the strengths of grey wolf optimization (GWO) and Sand Cat Swarm Optimization (SCSO) to effectively address global optimization tasks. Additionally, a chaotic opposition-based learning strategy is incorporated to enhance the efficiency and global search capability of the algorithm. One of the main challenges in metaheuristic algorithms is premature convergence or getting trapped in local optima. To overcome this, the proposed strategy is designed to improve exploration and help the algorithm escape local minima. As a real-world application, multi-level thresholding for color image segmentation—a well-known problem in image processing—is studied. The COSGO algorithm is applied using two objective functions, Otsu’s method and Kapur’s entropy, to determine optimal multi-level thresholds. Experiments are conducted on 10 images from the widely used BSD500 dataset. The results show that the COSGO algorithm achieves competitive performance compared to other State-of-the-Art algorithms. To further evaluate its effectiveness, the CEC2017 benchmark functions are employed, and a Friedman ranking test is used to statistically analyze the results.