# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 3 Variants of the HSA
### Modified Versions of the HSA  
[PERSON] et al. in [48] introduced a new integrated method for clustering and feature selection applications using an improved HSA. The method performs the feature selection task as a fundamental component of the global clustering search process. It aims to overcome the problem of clustering by encouraging locally optimal solutions in the feature selection and clustering process without any previous assumptions regarding the number of cluster centroids. A shifting composite design is proposed to convert both given applications. Moreover, local search procedures are employed to enhance feature selection and cluster centers. The results show that the proposed HSA is a robust clustering technique and obtains better results in regard to precision measures than other similar algorithms. [PERSON] and [PERSON] in [53] proposed a new clustering approach based on using HSA with the capabilities of K-means and ABC. Because HSA fails to converge to the critical area in the given search space, the exploitation feature of the ABC algorithm is utilized to enhance the solutions of the HSA based on their fitness values, and thus to increase the convergence velocity. The proposed method achieves more powerful outcomes than comparable techniques, such as HS and PSO, in regard to precision and convergence speed. [PERSON] et al. in [56] introduced a new modified version of the HSA to solve the clustering problem. The two parameters HMCR and PAR play an essential role in improvising a new solution. This research examined the influence on the outcomes when K-means are initialized with the solution delivered by the proposed HSA. The influence of HM size was also examined. The experiments were conducted on data clustering problems using nine benchmark datasets. The performance of the proposed clustering method is on par with K-means, FCM, genetic algorithm (GA), and four similar variants of the HSA. The results obtained are promising and confirm that the proposed clustering method gives much better results in regard to precision, recall, inter-cluster, G-measure, and intra-cluster distances. [PERSON] and [PERSON] in [60] proposed a new clustering method, based on using an improved HSA, to address the energy problem. The improved version of the HSA was used for protocol clustering in mobile-based WSNs. A new objective function is employed for arranging the cluster sources. In addition, mobile sink re-localization methods are also used to decrease and adjust power damage. The performance of the proposed method was assessed by changing various network scenarios and comparing it with other similar methods. The results showed that the proposed HSA is a robust clustering technique and obtains better clustering results. [PERSON] and [PERSON] in [61] presented P-HSA, a new approach for developing network lifetime by utilizing PSO algorithm-based clustering and HSA-based routing in WSNs. Global optimal group energy is chosen, and gateway nodes are included to reduce energy loss while transferring aggregated data to a base station (BS). Alongside this, the HSA-based local search strategy is used to determine the best routing path for gateway nodes to the BS. The results show that the proposed P-HSA is a robust clustering technique and produces good clustering results in terms of accuracy measure. [PERSON] in [63] proposed a new strength-green grouping tool for WSNs that can decrease total network strength dissipation and increase the network lifetime. The proposed method is divided into two sections. The first component deals with building infrastructure for the assigned WSN. An improved algorithm is used based on the HSA to outline the most popular variety of clusters and allocate sensors into precise clusters. The second component is involved with transmitting sensed facts from nodes to their cluster source.