# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 1 Introduction  
Optimization is a universal technique, whether it is engaged in engineering drawing or industrial applications, scientific, mathematic operation, etc. [1]. Usually, optimization techniques are utilized and employed to solve various optimization problems intelligently by determining the best of an extensive number of possible solutions [2, 3]. Meta-heuristic Algorithms (MAs), especially the Harmony Search Algorithm (HSA), have gained a reputation of being better than other search methods in addressing optimization problems because of their robustness and the simplicity of the outcomes they generate. These algorithms have been used in widely different fields, including ad-hoc and sensor networks [4], linear dynamic domains [5], autonomous smart microgrids [6], feature selection [7], benchmark functions [8], text clustering [9], parallel machine scheduling [10], and data clustering [11]. The meta-heuristic community has established many research projects, which include the launching of new optimization methods, applications, and performance examination. Recently, real-world optimization problems have become more complex and challenging to explain, because they are determined in high-dimensional search areas where there are insufficient mathematical notations to express and present the problem. Because of these difficulties, traditional search techniques cannot find complicated solutions. The current trend is toward the use of MAs, which can provide adequately optimal solutions by an iterative development search process [12]. There are two classes of search algorithms: (1) single-solution algorithms and (2) population-based algorithms. A single-solution algorithm typically runs using a set of candidate solutions (randomly generated); then, a new solution is created using the available candidate solutions, and this improvement process is iterated. Some examples are hill climbing, simulated annealing, \(\beta\) hill climbing, and Tabu search [13]. A population-based algorithm also starts with a set of random candidate solutions. The algorithm works iteratively to identify a subset of high-performing solutions among them. During the improvement processes, the solutions iteratively concentrate near (at least locally, and often globally) optimal solutions utilizing optimization operators. Some examples are the Ant Colony Optimization (ACO) algorithm [14], Spherical Search Optimizer (SSO) [15], Particle Swarm Optimization (PSO) algorithm [16], Group Search Optimizer (GSO) [17], Multi-verse Optimizer Algorithm (MOA) [18], Cuckoo Search Algorithm (CSA) [19], Gravitational Search Algorithm (GSA) [20], Krill Herd Algorithm (KHA) [21], Grasshopper Optimization Algorithm (GOA) [22], and HSA [23]. The two most critical issues in optimization algorithms are exploration/diversification and exploitation/intensification [24]. Exploration search strategy is the strength to explore the research area that encourages the algorithm to examine the different sections of the search area while evading getting stuck in local optima diversely. On the other hand, exploitation search strategy is the convergent technique that maintains a specific exploration along with the process or state of converging. To obtain useful outcomes in a reasonable time, the search algorithm should make suitable trade-offs between these two strategies. [PERSON] et al. in [23] proposed the HSA, a population-based meta-heuristic optimization algorithm, in 2001. The HSA imitates the design of a unique harmony in music to address an optimization problem. It has provided excellent results across an enormous range of sophisticated problems, such as flow for power systems [25], university timetables [26], congestion management [27], job shop scheduling [28], clustering [29], structural design, renewable energy [30], neural networks [31], water distribution [32], and data mining [33]. The main advantages of the HSA are its clarity of execution, its record of success, and its ability to tackle several complex problems.