4.1.1. Neural Networks

Moayedi et al. [126] synthesized a new EHO-MLP ensemble with a multi-layer perceptron (MLP) neural network to predict cooling load. The results revealed that EHO-MLP performed efficiently for adjusting biases of the MLP and the neural weights. It also outperformed the ACO [55] and EHO [105] optimization algorithms both in training and testing accuracies. Meanwhile, EHO-MLP took less time than ACO [55] and EHO [105] with regard to the time-effectiveness of the models.

Kowsalya et al. [127] used EHO to optimize neural network weights. The performance of the proposed method was evaluated on evaluation metrics. It was concluded that the proposed method provided better accuracy than existing classifiers.

Sahlol et al. [128] applied EHO to neural networks to classify each cell for the acute lymphoblastic leukemia problem. In the proposed method, the weights and biases of the network were updated by the EHO algorithm. The research results showed that EHO outperformed other classification methods.

#### 4.1.2. Underwater Sensor Networks

Kaur et al. [129] used EHO to solve underwater sensor networks optimization tasks. The research outcomes indicated that the proposed approach showed better performance than other strategies for most parameters.

#### 4.1.3. Unmanned Aerial Vehicle Path Planning

Alihodzic et al. [130] considered an approximation algorithm, adjusted EHO (AEHO), to solve the unmanned aerial vehicle (UAV) path planning problem. AEHO was used for adjusting the UAV path planning problem and it was compared with other state-of-the-art algorithms. The simulation experiments showed that AEHO obtained a safe flight path and was an excellent choice for the UAV path planning problem.
