Multilevel thresholding is a simple and important method for image segmentation in various applications that has drawn widespread attention in recent years. However, the computational complexity increases correspondingly when the threshold levels increase. To overcome this drawback, a modified water wave optimization (MWWO) algorithm with the elite opposition-based learning strategy and the ranking-based mutation operator for underwater image segmentation is proposed in this paper. The elite opposition-based learning strategy increases the diversity of the population and prevents the search from stagnating to improve the calculation accuracy. The ranking-based mutation operator increases the selection probability. MWWO can effectively balance exploration and exploitation to obtain the optimal solution in the search space. To objectively evaluate the overall performance of the proposed algorithm, MWWO is compared with six state-of-the-art meta-heuristic algorithms by maximizing the fitness value of Kapur’s entropy method to obtain the optimal threshold through experiments on ten test images. The fitness value, the best threshold values, the execution time, the peak signal to noise ratio (PSNR), the structure similarity index (SSIM), and the Wilcoxon’s rank-sum test are used as important metrics to evaluate the segmentation effect of underwater images. The experimental results show that MWWO has a better segmentation effect and stronger robustness compared with other algorithms and an effective and feasible method for solving underwater multilevel thresholding image segmentation.
