A Performant Clustering Approach Based on an Improved Sine Cosine Algorithm

Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.