Chimp optimization algorithm in multilevel image thresholding and image clustering

Multilevel image thresholding and clustering, two extensively used processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach yielding multiple threshold values for each color channel generate clustered segmented images appears be quite efficient it provides significant performance, although this method is computationally heavy. To ease complicated process, nature inspired optimization algorithms are handy tools. In paper, the performance Chimp Optimization Algorithm (ChOA) clustering segmentation has been analyzed, based on multilevel channel. evaluate ChOA regard, several metrics used, namely, Segment evolution function, peak signal-to-noise ratio, Variation information, Probability Rand Index, global consistency error, Feature Similarity Index Structural Blind/Referenceless Image Spatial Quality Evaluatoe, Perception Evaluator, Naturalness Evaluator. This compared with eight other well known metaheuristic algorithms: Particle Swarm Algorithm, Whale Salp Harris Hawks Moth Flame Grey Wolf Archimedes African Vulture using popular techniques-Kapur’s entropy Otsu’s class variance method. results demonstrate effectiveness competitive Algorithm.