# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 5 Discussion  
This section discusses the theoretical aspects, assessment, and evaluation of HSA. Global mathematical optimization problems, such as benchmark test functions, yield near-optimal solutions of a mathematical representation by determining the desired minimum or maximum function value. Due to the increasing complexity of optimization problems, such as engineering and real-world problems, the development of powerful stochastic methods is becoming much more necessary, critical, and important than before. Over the past decade, several optimization methods have been used in different fields of biology or nature studies. The distribution of the addressing rule is classified into two stages, which is the prime advantage of multi-solution methods (i.e., stochastic search and exploration and exploitation). The above-mentioned exploration indicates a manner where the population (i.e., solutions) tends to be developed repeatedly and the encouraging areas of the search space are examined as wide as possible. By contrast, the search solutions are controlled by the exploitation or intensification to approach the optimal solution obtained in the global search stage. Meta-heuristic algorithms are advantageous for many reasons. First, the random style encourages these algorithms to avoid falling into local optima and converge to the near-optimal solution. The objective is not to receive the optimum solution of the problem but to obtain the near-optimal solution (with a high fineness value within a reasonable running time). The main purpose of this is to ensure proper stability across search procedures. The purpose of exploration is to discover the more promising areas in a complex and wide search area. Consequently, the exploitation procedure is intensified by the local search procedure in these promising areas to yield better solutions. The optimal accomplishment and effectiveness of a special optimization technique is the trade-off of these techniques, and the better ability will be performed. The current meta-heuristic algorithms adjust the trade-off between these two approaches. They could be further optimized and adjusted for local or global search procedures. Two or more optimization techniques are mixed by combining their elements to obtain advantageous characteristics superior to those of each one alone while avoidingtheir disadvantages as much as possible. Second, the completion of optimization techniques depends on its origin, abilities, simplicity, and experimental balance. Meta-heuristic optimization algorithms have been employed extensively to tackle complicated issues. However, for complicated issues, the largest portion of the optimization techniques still falls and is trapped in neighborhood search and fails to meet the close-to-global solution. The idea is that the week diversification search occurs inside the carried out method. Several different search procedures have been used to enhance the effectiveness of the optimization techniques and mitigate the disadvantages; these methods include hybridization and elitism (the survival of an elite as a dominating element in a gadget) [110; 111]. The HSA algorithm has emerged rapidly and has become a strong tool for addressing complex problems, similar to other optimization methods. It is an artificial intelligence technique using stochastic computational mechanisms to determine the near-optimal solution for multi-dimensional and one-dimensional purposes according to its objective function. The HSA functions in a common manner, so it is easily implemented and can be efficiently used in a wide class of domains. The results summarized in this paper indicate the effectiveness and accuracy of the obtained results of the HSA. This is determined by comparing the obtained results of the HSA with those of other recently published optimization techniques. Much like numerous optimization strategies, HSA has some advantages and some weaknesses.