Abstract
The standard crow search algorithm suffers from low convergence accuracy, insufficient stability, and susceptibility to getting stuck in local optima. To tackle these formidable challenges, this paper proposes a novel multi-strategy improved crow search algorithm (MSICSA) specifically designed for multi-level image segmentation. The proposed approach incorporates three key enhancements: firstly, opposition-based learning (OBL) is utilized to improve the quality of initial solutions within MSICSA; secondly, an adaptive awareness probability mechanism is introduced to better balance the trade-off between exploration and exploitation; lastly, two differential mutation operators are developed to enhance global search capabilities, increase population diversity, and reduce the risk of converging on local optima. To validate the performance of the proposed algorithm, two sets of experiments are conducted. In the first set of experiments, CEC 2020 benchmark test functions are selected to compare the performance of MSICSA with other group intelligent optimization algorithms. In the second set of experiments, Otsu's method and fuzzy entropy are employed as objective functions for performing multilevel threshold segmentation on twelve grayscale images. The experimental results demonstrate that MSICSA outperforms seven comparison algorithms in terms of both convergence speed and segmentation quality.