<p>Circle search algorithm (CSA) is a meta-heuristic optimization algorithm based on the characteristics of circle geometry. In order to overcome the shortcomings of the original CSA in the optimization process, such as low convergence accuracy and easily falling into local optimal, this paper proposed a multi-strategy enhanced CSA named APRDCSA. Firstly, a mutation strategy guided by multiple superior solutions is proposed, which increases the diversity of the population and the global exploration ability of the algorithm. Secondly, random replacement strategy is introduced in the late iteration to avoid falling into local optima. Finally, a population evolution state assessment framework with adaptive parameter (APSE) based on improvement rate is proposed to improve the probability of finding a better solution. A series of comparative experiments on IEEE CEC 2014 demonstrate the superior convergence speed and solution quality of APRDCSA, as well as its enhanced reliability in avoiding local optima. In addition, to further illustrate the improved performance of APRDCSA and its ability to solve practical applications, we apply it to the multi-thresholding image segmentation (MIS) method based on non-local mean two-dimensional histogram and Kapur entropy. Experimental results show that the proposed method achieves better segmentation results at both low and high threshold levels.</p>