# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 3 Variants of the HSA
### Hybrid Versions of the HSA  
Numerous methods for tackling clustering problems may be found in the literature. Of these, meta-heuristic optimization strategies show particular promise. When a hybrid of two algorithms is established to take advantage of the most useful features of each, more dependable results may be expected. HSA and K-means, the methods discussed in [59], provide the best results for clustering; when combined in a hybrid, they give even higher outcomes. [PERSON] et al. in [49] proposed a new improved algorithm to address internet document clustering using a hybridization of the basic HSA and the K-means approach called IGBHSK. The proposed method determines the optimal area of clusters. The hybrid HSA produces an extensive search for exploration within the available search space. The K-means approach is applied to determine the optimal solution in a neighborhood. The proposed IGBHSK algorithm was evaluated on the Reuters-21578 and DMOZ datasets. The algorithm obtains promising results in terms of precision compared with other similar methods. [PERSON] et al. in [50] proposed a new hybrid optimization method based on using the HSA to address the clustering problem. The proposed method works in two steps. In the first step, the HSA examines the search place of the supplied dataset to locate the nearest vital clusters. The centers are selected through the HSA and then assessed by means of the adapted C-means technique. In the second step, the excellent centers from the first step are utilized as preliminary centers for the C-means technique. Standard datasets were used to assess the performance of the hybrid method. The proposed HSA solved the problem of finding the initial centroids efficiently. [PERSON] and [PERSON] in [51] proposed a new hybrid technique based on using a stepped-forward HSA with K-means. The proposed method is meant to solve the weaknesses of the initial centroids. The results showed that the proposed HSA is a reliable clustering technique, obtaining higher outcomes in terms of precision than other similar algorithms. [PERSON] and [PERSON] in [55] adopted a hybrid clustering approach using Time Frequency and Inverse Document Frequency (TF-IDF) to measure significance, and HSA with K-means to cluster the documents. In the proposed method, an attempt is made to apply the concept factorization approach for record clustering and to get better clusters in a significant amount of time. A comparison was performed with many other methods, and the assessment proved that the proposed concept factorization produces better clustering results. In [69], another approach based on the use of the HSA with the concept factorization method for document clustering is proposed. The obtained results show that concept factorization can enhance the performance of the HSA. [PERSON] et al. in [57] introduced a novel hybrid clustering algorithm based on adjusting the parameters of the HSA. The new parameter adaptive HSA (PAHS) is used to improve the cluster centers, which are themselves utilized in initializing the HSA clustering method. The proper number of clusters is defined through four similar cluster validation criteria. The proposed algorithm was assessed on three real-life data clustering datasets and its performance was contrasted with that of K-means, FCM, and HS. The proposed method produced better clustering results in regard to the weighted average, precision, recall, G-measure, and F-measure. [PERSON] et al. in [29] introduced a new data clustering method based on a the KHA-HSA hybrid strategy called Harmony-KHA for enhancing the data clustering procedure. This hybrid approach attempts to improve the global search capability of the basic KHA by adding a global search from the HSA for exploration near the optimal solution in the KHA. Thus, krill positions proceed to the global best solution. The proposed method is used to find the best solution through position calculation. Experiments were carried out using four datasets from the UCI Machine Learning Repository Repository.