Metaheuristic algorithms have been widely used since they were proposed in the 1960s as they can effectively reduce the amount of computation and improve the efficiency of optimization. The algorithms are characterized by imitating various operating mechanisms in nature, have the characteristics of self-regulation, and have solved the problems like low computational efficiency and poor convergence of traditional optimization algorithms such as gradient descent, Newton's method, and conjugate descent. The algorithms have sound effects in combination optimization, production scheduling, and image processing. In this study, an improved metaheuristic optimization algorithm-NBAS algorithm is proposed, which is obtained by mixing binary discrete beetle antennae search algorithm (BBAS) and the original antennae search algorithm (BAS). NBAS balances the local and global search, and effectively solves the problem like the local optimum. It is concluded that the algorithm balances the local and global search, which effectively compensates the shortcomings of the algorithm that is easy to fall into local optimum. In order to verify the effectiveness of the NBAS algorithm, this study combines the NBAS algorithm with the two-dimensional Kaniadakis entropy algorithm, and proposes a fast and accurate NBAS-K entropy image segmentation algorithm. The NBAS-K entropy solves the problems that the optimization algorithms used for image threshold segmentation function are easy to fall into local optimum, and have the large number of optimization individuals and the high design complexity, which results in large amount of computation and time-consuming. Finally, the NBAS algorithm is combined with the two-dimensional K entropy algorithm to generate a fast and accurate NBAS-K entropy image segmentation algorithm. The experimental results of the NBAS-K entropy algorithm, BAS-K entropy algorithm, BBAS-K entropy algorithm, Genetic-K entropy algorithm (GA-K entropy), particle swarm optimization-K entropy algorithm (PSO-K entropy), and grasshopper optimization-K entropy algorithm (GOA-K entropy) on Berkeley datasets, artificially noisy images, and remote sensing images show that the proposed method not only has better anti-noise performance, but also has higher precision and robustness, and can realize complex image segmentation more effectively.