A metaheuristic algorithm called Harris hawks optimization (HHO) is gaining its popularity among its clan and useful for optimization. In this algorithm, the prey gets completely exhausted when the escape energy is equal to zero, therefore it fails to explore further. The random operator chosen in the existing method is a wastage of search agents (Harris hawk). To overcome this issue, we propose an adaptive Harris Hawks optimization (AHHO) technique. In this work, the mutation is employed to restrict the escape energy within the range $\left[0,2\right]$, except for the mutation interval. Our method adaptively decides the chance of the Harris hawk would do perch along with the other family members or move to a random tall tree with the help of average fitness. The proposed AHHO algorithm is benchmarked with 23 classical test functions and 30 modern test function from CEC 2014 test suite consisting of unimodal, multimodal, hybrid and composite functions. The qualitative and quantitative analysis, which include metrics such as statistical results, convergence curves, $p$-value from Wilcoxon rank-sum test and Friedman mean rank. It reveals that AHHO provides good results when compared with other well-known nature-inspired algorithms. It can be used for multilevel thresholding which is an optimization problem. Recently, 2D histogram based multilevel image thresholding techniques are becoming more popular for different image processing applications. The local averaging scheme used for the construction of a 2D histogram in existing methods fails to preserve the edge information. The choice of the diagonal pixels only results in the loss of information making the earlier multi-level thresholding methods inefficient to retain the spatial correlation information. Although the computation of 2D histogram based on grey gradient information is a better way to threshold an image, it faces problems due to the presence of the edge magnitude peaks. These problems are solved by investigating an improved 2D grey gradient (I2DGG) method, a new technique is suggested in this paper to suppress high edge magnitudes. The I2DGG is a maximization problem, which requires an exhaustive search process. Therefore, AHHO is used to obtain the optimal threshold values. The result of our proposed AHHO based multilevel thresholding using the I2DGG method is obtained using all the 500 images from the Berkeley Segmentation Data set (BSDS 500). When we compare the proposed method I2DGG with 2D Tsallis entropy and 1D Tsallis entropy based multilevel thresholding, the I2DGG outperforms other methods. The experimental results are also compared with the state-of-art optimization-based multilevel thresholding methods, which shows our proposed method is beneficial to the segmentation field of image processing.

一种称为哈里斯·霍克斯优化（HHO）的元启发式算法在其家族中越来越流行，并且对优化很有用。在该算法中，当逃逸能量等于零时，猎物会完全耗尽，因此无法进一步探索。现有方法中选择的随机算子是搜索代理程序的浪费（哈里斯·霍克）。为克服此问题，我们提出了一种自适应哈里斯霍克斯优化（AHHO）技术。在这项工作中，采用突变将逃逸能量限制在$\left[0，2\right]$，除了突变间隔。我们的方法适应性地决定了Harris鹰与其他家庭成员一起栖息的机会，或在平均适应度的帮助下搬到随机的高大树上的机会。拟议的AHHO算法以CEC 2014测试套件中的23个经典测试函数和30个现代测试函数为基准，该测试套件由单峰，多峰，混合和复合函数组成。定性和定量分析，包括统计结果，收敛曲线，$p$Wilcoxon秩和检验的平均价值和Friedman均值。它表明，与其他著名的自然启发算法相比，AHHO提供了良好的结果。它可以用于多级阈值优化，这是一个优化问题。近来，基于2D直方图的多级图像阈值化技术在不同的图像处理应用中变得越来越流行。现有方法中用于构建二维直方图的局部平均方案无法保留边缘信息。对角像素的选择仅导致信息丢失，这使得较早的多级阈值处理方法无法有效保留空间相关信息。尽管基于灰度梯度信息的2D直方图计算是对图像进行阈值处理的更好方法，由于边缘幅度峰值的存在，它面临问题。通过研究改进的2D灰度梯度（I2DGG）方法解决了这些问题，本文提出了一种抑制高边缘幅度的新技术。I2DGG是一个最大化问题，需要详尽的搜索过程。因此，AHHO用于获得最佳阈值。我们使用I2DGG方法提出的基于AHHO的多阈值阈值化的结果，是使用伯克利细分数据集（BSDS 500）的所有500张图像获得的。当我们将建议的方法I2DGG与基于2D Tsallis熵和基于1D Tsallis熵的多级阈值进行比较时，I2DGG的性能优于其他方法。还将实验结果与基于最新优化的多级阈值化方法进行了比较，