harris hawks optimization algorithm

Breast cancer is one of the deadly diseases among women and every year millions of people around the world die due to this disease.If breast cancer is detected in the early stages,the chances of survival will increase.One of the methods of breast cancer diagnosis is the use of knowledge discovery methods such as machine learning. Machine learning methods can discover the pattern of breast cancer by analyzing the information of patients and their records.The important advantage of using machine learning methods to diagnose breast cancer is to reduce diagnosis costs and help more accurate diagnosis by specialist doctors. One of the methods of breast cancer diagnosis is the use of a support vector machine. Support vector machine is a method for classifying samples with the aim of reducing operational risk in classification.One of the important challenges of the support vector machine is the output error of the model due to the lack of optimal selection of the learning parameters.In the proposed method to reduce the classification error of malignant and benign people, Harris Hawks's optimization algorithm has been used.The role of Harris Hawks's algorithm in the proposed method is to optimize the parameters of the support vector machine to reduce the diagnosis error of malignant patients.The evaluations have been done in the MATLAB programming environment and on the Wisconsin dataset.The evaluations show that the proposed method in breast cancer diagnosis has an accuracy of 99.31% and is more accurate than methods such as Wall's optimization algorithm in breast cancer diagnosis.

This paper presents an intelligent method for online voltage stability margin (VSM) assessment using optimized adaptive ANFIS. Harris Hawks Optimization Algorithm (HHOA) is used to train the ANFIS and conventional wavelet transform (WT) is also  applied as a feature extraction technique on the network voltage profile. The network voltage profile is used as the main data to estimate VSM because it contains the necessary information about the network structure, load levels, production pattern, and control system performance in the network. Using wavelet transform technique with high resolution, the necessary features for entering the ANFIS block are extracted, but due to the variety and multiplicity of these features, especially for large networks, the Principal Component Analysis (PCA) method is used to select the appropriate features and remove additional data. The characteristic of this hybrid algorithm is that it can be used both in dynamic and static conditions of the network. Finally, the proposed VSM estimation algorithm is applied to the 39-bus and 118-bus IEEE test systems, and its results are evaluated. The comparison of the results with other VSM methods shows that the proposed algorithm is effective for large power grids.