# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Clustering for Network Applications  
Various studies have focused on place detection in complex networks in recent years [39; 109]. Single-goal strategies that produce the handiest one-optimization function may additionally have defects, consisting of just a single solution that may be executed. [PERSON] et al. in [80] proposed a spectral clustering approach primarily based on hybridizing a multi-objective HSA, called SCAH-MOHSA. The proposed method was integrated with a new local search method to find the creation of clusters in complex networks. First, a more suitable spectral technique was used to transform the local search problem into an information clustering problem. Meanwhile, various representations of the HM were controlled. Then, an adaptive fusion multi-goal HSA was applied to decide the multi-objective problem to address community production. The experiments were conducted using synthetic and real international networks. The results illustrated that the proposed technique achieved better results than healthy employment.  
[PERSON] et al., in [81], introduced a nature-stimulated approach that can increase the Optimum-Path Forest (OPF) clustering method by finding the optimal parameters in a discrete framework. Experiments that were conducted with two comprehensive datasets have shown that the proposed approach can carry out comparable parameters' rates associated with the exhaustive exploration search. Although the proposed technique is more agile than the regular algorithm, it is less useful for intrusion discovery machines in large-scale visitor networks.  
[PERSON] and [PERSON] [88] proposed a new method based on HSA to distribute graphs using the modularity measure. This method was examined with five different real-world network problems. In addition, the method achieved the best-known modularity measure, and the most advanced subsets were produced using this measure. The obtained results showed that the HSA provides faster results compared with other similar methods. However, the proposed algorithm required a larger HM size and more iterations to achieve the best modularity values.  
[PERSON] et al., in [62], presented a Protein-Protein Interaction (PPI) network systems exposure algorithm based on the HSA (HMS-CD). Compared with the simple HSA, the converting parameters of HMCR and BW are included to decorate the search method. The proposed approach is usually recommended to obtain a collection of nodes having a higher widespread series coefficient within the PPI network, which is the goal feature of the proposed methods. The obtained outcomes on a real benchmark dataset such as the PPI community showed that the proposed technique usingthe HSA achieved a better detection cost than the primary HSA and standard MCODE set of rules. Moreover, it can recognize complexes in the PPI network more accurately.  
Network applications are one of the most studied applications by using HSA, especially wireless sensor networks. This means that HSA can reach better results in this domain. Most of the researches have been conducted using several versions of the HSA to solve wireless sensor networks applications, which means that wireless sensor networks applications still have space for further investigations and analysis.