3.2.8. ELM-EHO

Satapathy et al. [121] proposed a combination model named EHO-ELM with a combination of the advantages of extreme learning machine (ELM) and EHO. In this model, EHO-ELM was used to determine the input weights of an ELM model. EHO-ELM was tested on three different brain image datasets. The results demonstrated that EHO-ELM outperformed the basic ELM model in the three brain image datasets.

#### 3.2.9. Global and Local Search EHO

Hakli et al. [122] developed a new EHO approach to solve constrained optimization problems. The EHO variants (GL-EHO) were adapted to implement constrained optimization. Experimental results showed that GL-EHO was capable of overtaking EHO.

#### *3.3. Variants of EHO*

Different variants of the EHO algorithm are presented in Table 3. The detailed methods are presented herein.

#### **Table 3.** Different variants of EHO.

