# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 5 Discussion
Even though there is no combined proof for this optimizer, the final capabilities are summarized in this assessment to confirm that the HSA is superior to different optimization algorithms regarding the convergence inspired rate. Table 3 lists the strengths and weaknesses of HSA. The successful application of these algorithms, such as HSA, in industry and science indicates the advantages of using swarm techniques, due to the advantages of swarm algorithms. Firstly, swarm techniques retain information regarding the search space throughout iterations, whereas such data are rejected by evolutionary algorithms in each iteration. Secondly, there are fewer controlling parameters in swarm algorithms. Thirdly, the swarm algorithm is equipped with fewer operators than evolutionary algorithms. Finally, swarm techniques are adaptable, which make them easily applicable to problems in various fields. \begin{table}
\begin{tabular}{l} \hline \hline
**Advantages** \\ \hline - Combining HA with other algorithms is strongly satisfying [50]. \\ - An excellent convergence acceleration [53]. \\ - An elevated method of getting surprising answers [54]. \\ - Appropriate for many forms of optimization problems [44]. \\ - An efficient international scheme [54]. \\ - Fitting for extensive space [34]. \\ - Robust in dealing with a wide variety of determinations [79]. \\ - Has higher feasibility and efficiency in generating global optima [58]. \\ - Lower chance of getting stuck in nearby optima [68]. \\ - HSA is straightforward in its concept and implementation associated with other heuristic optimization processes [23]. \\ - Reasonable execution time [66]. \\ - Some parameter tuning [111]. \\ - Adaptability, robustness, and scalability are observed in critical characteristics. \\ \hline Disadvantages \\ \hline - The primary HSA has been proposed for discrete, single-objective, and multi-objective problems. \\ - No theoretical converging body. \\ - Suffers from premature convergence [91]. \\ - Possibility distribution changes with the needs of generations [86]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of HSA. The main difficulty of HSA is defining the probabilistic convergence properties of HSA, which are required to fully understand the given technique. The problem of early convergence (premature convergence) in the HSA normally causes the recovery procedure to become stuck during the exploitation search. This effect usually occurs when the solutions' divergence decreases and the solutions cannot avoid falling into the local optima. Furthermore, there is significant potential for researchers to employ and apply the advantages of HSA to approach complex industry and other real-world problems. The theoretical analyses are essential inside the HSA, though it is easy in theory. To observe the effect of varied parameter settings on the exploration performance, the parameters of the HSA were tested in the same manner as in [PERSON] et al. [111]. To analyze the answer clustering components in the HSA, the standard quantity of clusters varies in the course of the optimization technique were identified [54]. Further, the characterization of the population heterogeneity of the HSA has been proposed [86]. Many incomplete re-initializing answers procedures were tested to improve the population heterogeneity and to remove answers from nearby optima. The idea after the re-initialization is to create the possibility of solutions \"jumping out\" of neighborhood optima and to hold sufficient strength for the set of rules to yield \"suitable enough\" answers. The HSA is a typical example of a developmental swarm intelligence algorithm. The convergent and divergent traits in HSA correspond to the capacity developing and potential in higher answers, respectively.