Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.

Uses Harris Hawk and Whale Nature Inspired Algorithm to Train the weights of Neural Network. An approach to adjust the parameters of NN connection weights using the hybrid of Harris Hawk Optimization and Whale Optimization algorithm was proposed. The results showed that the hybrid algorithm has been successfully applied to train neural networks. The results showed that there is no superiority of one algorithm over another, however, the results of the proposed algorithm are a competitive alternative to other P-Metaheuristic algorithms. Hybrid Harris Hawk with Whale optimization to train weights of the neural network was used to increase the efficiency of fraud detection and cancer datasets. Our method for anomaly detection is a supervised method based on classification. The performance of Harris Hawk with Whale is acceptable and has promising results that nominate it for other optimization applications such as scheduling.