3.1.5. k-Means EHO

Tuba et al. [111] introduced data clustering into EHO in which the local search ability of EHO was improved through *k*-means. The proposed *k*-means EHO was tested on six benchmark datasets. The clustering results showed that *k*-means EHO found better clusters than other algorithms.

#### 3.1.6. Oppositional-Based Learning EHO

Chakraborty et al. [112] proposed improved EHO with a dynamic Cauchy mutation (EHO-DCM) to solve the multilevel image thresholding for image segmentation problems. In EHO-DCM, oppositional-based learning (OBL) and DCM were introduced, in which OBL and DCM were employed to accelerate the conventional and mitigate the premature convergence, respectively. The results were compared with five metaheuristic algorithms (EHO [105], CSO [104], ABCs [53], BAs [91], and PSO [2]). It was demonstrated that EHO-DCM provided promising performance in view of optimized fitness value, feature similarity index, and structure similarity index.

#### 3.1.7. Adaptive Whale EHO

Chowdary et al. [113] proposed a hybrid mixture model based on the adaptive whale EHO (AWEHO) algorithm, which is the integration of three technologies: EHO [105], the whale optimization algorithm (WOA), and the adaptive concept. In the proposed method, the AWEHO algorithm was applied to perform optimal sensing by using the foraging behavior of whales and the herding behavior of elephants. The analysis of the computational results indicated that the herding and foraging behavior of the AWEHO achieved efficient spectrum sensing in the cognitive radio network.

#### *3.2. Hybrid EHO Algorithms*

The hybrid EHO algorithms are presented in Table 2. The details are included in the following sections.

#### 3.2.1. CBEHO, ATEHO, and BIEHO

Rashwan et al. [114] studied three approaches, which are cultural-based EHO (CBEHO), alpha-tuning EHO (ATEHO), and biased initialization EHO (BIEHO), to enhance the performance of standard EHO. A comparative experiment from CEC 2016 was done between three EHO approaches and the other optimization methods. It was demonstrated that the performances of the three EHO approaches were superior to other comparison methods. In order to further verify the performance of the three EHO approaches, various experiments were carried out on engineering problems such as the welded beam, gear train, continuous stirred tank reactor, and three-bar truss design problem.


#### **Table 2.** The hybrid EHO algorithms.
