Harris Hawk Optimization

Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm used to solve complex optimization problems, particularly in machine learning and engineering applications. Current research focuses on leveraging HHO to optimize various model architectures, including neural networks (e.g., multilayer perceptrons, Elman networks, convolutional neural networks) and support vector machines, for tasks such as intrusion detection, grid balancing in smart grids, and medical diagnosis (e.g., brain tumor detection, heart failure prediction, COVID-19 classification). The effectiveness of HHO lies in its ability to efficiently explore and exploit the search space, leading to improved performance compared to other optimization algorithms in diverse applications. This makes HHO a valuable tool for enhancing the accuracy and efficiency of various models across multiple scientific and engineering domains.