4.1.4. Clustering

Rani et al. [131] proposed a new detection approach for dynamic protein complexes by using Markov clustering with EHO (MC-EHO). The MC-EHO method divided the protein–protein interaction (PPI) network into a set of dynamic sub-networks and employed the clustering analysis on every sub-network. The experimental analysis was employed on 11 various widespread datasets and four different benchmark databases. The results showed that MC-EHO surpassed various existing approaches in terms of accuracy measures.

Jaiprakash et al. [132] formulated EHO to perform a clustering task by minimizing intra-cluster distance. The simulation was verified on six benchmark datasets and three synthetic datasets. The superior percentage accuracy of EHO was demonstrated by comparing it with other algorithms in the form of box plots.


**Table 5.** A summary of the EHO applications in engineering optimization.

**Figure 5.** Engineering optimization/applications.
