摘要:元启发式算法自20世纪60年代提出以后，由于其具有可以有效地减少计算量、提高优化效率等优点而得到了广泛应用.该类算法以模仿自然界中各类运行机制为特点，具有自我调节的特征，解决了诸如梯度法、牛顿法和共轭下降法等这些传统优化算法计算效率低、收敛性差等缺点，在组合优化、生产调度、图像处理等方面均有很好的效果.提出了一种改进的元启发式优化算法——NBAS算法.该算法通过将传统天牛须算法（BAS）离散化得到二进制离散天牛须算法（BBAS），并与原始天牛须算法进行混合得出.算法平衡了局部与全局搜索，有效地弥补了算法容易陷入局部最优的不足.为了验证NBAS算法的有效性，将NBAS算法与二维K熵算法结合，提出了一种快速、准确的NBAS-K熵图像分割算法.该方法解决了优化图像阈值分割函数的优化算法易陷入局部最优、算法寻优个体数多、设计复杂度高所导致的计算量大、耗时长等问题.NBAS-K熵算法与BAS-K熵算法、BBAS-K熵算法、遗传K熵算法（GA-K熵）、粒子群K熵算法（PSO-K熵）和蚱蜢K熵算法（GOA-K熵）在Berkeley数据集、人工加噪图像以及遥感图像上的实验结果表明，该分割方法不仅具有较好的抗噪性能，而且具有较高的精度和鲁棒性，能够较为有效地实现复杂图像分割.



Abstract: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.



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