4.2.7. Image Processing

Tuba et al. [151] used the EHO algorithm to solved multilevel image thresholding problems based on Kapur and Otsu's criteria. The proposed algorithm was compared with four swarm intelligence approaches. The experimental results concluded that the EHO algorithm successfully solved multilevel thresholding problems and additionally had smaller variance.

Jino et al. [152] presented the short review of nature-inspired optimization algorithms, such as EHO [105], BAs [91], ACO [55], ABCs [53], PSO [2], FAs [69], bumble bees mating (BBM), and CSO [104]. These algorithms were applied to advanced image processing fields.

Jayanth et al. [153] used the EHO algorithm to classify the high spatial resolution multispectral image classification. According to the fitness function, EHO determines the information of class and multispectral pixels. When compared with the SVM method, the experimental results of two datasets demonstrated that the proposed method improved overall accuracy by 10.7% for dataset 1 and 6.63% for dataset 2.

Cardoso et al. [154] used EHO to improve the search for the maximum correlation point of the image. The search process was implemented in software based on an embedded general purpose processor. The performance results showed that the proposed method outperformed other optimization metaheuristics, which were PSO [2] and ES [79].

#### 4.2.8. Wireless Sensor Networks

Correia et al. [155] applied the EHO algorithm to solve the energy-based source localization problem for wireless sensors networks. The energy decay model between two sensor nodes was matched through key optimized parameters of the EHO algorithm. Comparing the performance between the proposed method and existing non-metaheuristic algorithms, EHO significantly reduced the estimation error in environments with high noise power. In addition, EHO represented an excellent balance between estimation accuracy and computational complexity.

Strumberger et al. [156] solved localization problems for wireless sensor networks using the EHO algorithm. According to the simulation results and comparative analysis with other state-of-the-art algorithms, EHO found the coordinates of unknown nodes randomly deployed in the monitoring field, which proved to be robust and efficient metaheuristics when tackling wireless sensor network localization.

Kaur et al. [157] proposed a novel and energy-efficient approach based on EHO to improve the span of energy in nodes of an underwater network. In the proposed approach, a dynamic cluster head in underwater wireless networks was formed by the behavior of the elephants selecting their heads. It was demonstrated that the EHO algorithm was a promising algorithm for tackling multiple parameters of underwater networks.

#### 4.2.9. Feature Selection

Xu et al. [158] proposed an improved elephant herding optimization (IEHO) algorithm for feature selection in several datasets and distributed environments, which effectively reduced the running time of the algorithm under the premise of ensuring classification accuracy. The experiments showed that the classification efficiency of the IEHO algorithm significantly outperformed other optimization algorithms, such as PSO [2] and EHO [105].

#### 4.2.10. Optimal Power Flow

Dhillon et al. [159] applied EHO to mitigate frequency deviations under sudden variations in demand on the automatic generation control of an interconnected power system. The outcomes of the EHO-based automatic generation control was compared with PSO-based automatic generation control. It was concluded that the settling time of the EHO-based strategy took less time than the PSO-based strategy.

Kuchibhatla et al. [160] used an EHO algorithm to improve the power quality (PQ) and reduce the harmonic distortion in a photovoltaic (PV) interconnected wind energy conversion system (WECS). The performances of three methods (EHO [105], BAs [91], and FAs [69]) were evaluated. The obtained results showed that the proposed method enhanced the performance of the grid-connected hybrid energy system.

Sambariya et al. [161] used EHO to adjust the parameters of a PID controller for the load frequency control of a single-area reheat power system. The solution results showed that the proposed technique obtained better robustness compared with the PID controller.
