# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Clustering Applications for Image Segmentation  
[PERSON] et al., in [74], introduced a novel effective clustering method, called DCHS, based on the HSA. In the proposed algorithm, the basic HSA is improved to automatically develop the appropriate number of groups and areas of cluster centers. By combining the idea of position length in each solution, DCHS can convert the changeable number of nominee cluster centers at each iteration. DCHS was employed on standard real images, and the obtained results showed that DCHS finds a suitable number of clusters and locations of cluster centers. Further, it obtained promising results compared to other similar clustering algorithms.  
[PERSON] et al., in [77], introduced a new clustering method for automatic segmentation in MRI images. The proposed method is based on using a new effective clustering technique in intelligent behavior, called DCHS, that hybridizes the basic HSA with FCM. The idea of changeable positions in each solution was employed to convert the number of nominee centers at each repetition. Moreover, a unique operator was included to assist the choice of negative choice variables in the solution. Furthermore, a subset of Haralick construction features and pixel depth values was adopted as a feature location in DCHS to describe tumor sizes. The obtained results were statistically assessed versus manually described data for four patients. The proposed algorithm obtained promising results, with a Dice coefficient of 0.72.  
In [90], [PERSON] et al. introduced a novel automatic clustering technique that uses a developed Parameter HSA (PAHS) as the underlying algorithm. It utilizes the real-coded variable-period solution, which can understand the routes of clusters automatically. Recent ideas regarding threshold putting and cutoff were implemented to improve the optimization procedure. The distribution of information factors to various cluster centers was executed based on the produced weighted Euclidean distance, as opposed to the unique degree. The proposed approach found high-density clusters with a geometrical shape. Moreover, it was used for the automated segmentation of grayscale and coloration snapshots, and its overall performance was similar to that of various currently used algorithms. The acquired results showed that the proposed method is sensible and beneficial.  
Furthermore, in [102], [PERSON] et al. proposed a Self-Adaptive Multi-Objective HSA-based Fuzzy Clustering technique (SAMOHSFC) for solving the clustering problem in image segmentation. The proposed SAMOHSFC method develops different cluster centers in one solution and optimizes multiple objectives. In addition, the spatial data of the image are considered as a characteristic of the input attributes of the grey data of the input image. The superiority of the proposed method over three comprehensive segmentation strategies was confirmed using synthetic and picture datasets with regard to quantitative and visual functions. Furthermore, various instructions regarding spatial facts were analyzed based on the segmentation achievement of the proposed SAMOHSFC.