# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 3 Harmony Search Applications in China
### Computer Sciences
[PERSON] and [PERSON] [30] introduced an improved HS algorithm in order to increase its performance. A simulation showed that the HS algorithm improved the ability of performing global search and convergence speed. In Ref. [31], a detailed review was made of the HS improvisation process, noting that improvisation was a common system of search. The global learning approach was developed to increase the efficiency of HS in order to maximize global search ability. An adjusted random sorting method was used to minimize the potential for optimum position. The analysis also provided a different method for change such as the alteration of the iteration model. The HS algorithms demonstrated that experimental findings were able to yield significant changes in efficiency. To minimize use of server energy, using an updated HS, [PERSON] and [PERSON] [32] examined information caching and upgrading techniques of Internet of Things. Simulations confirmed successful results  
Figure 3: Flowchart of MR-DHS [29]. using the proposed algorithm by contrasting it with certain benchmark algorithms and evaluating its performance in terms of the content distribution quality ratio reached, standardized energy usage, and overall network usefulness. In Ref. [33], an Improved Differential HS (IDHS) was used to solve mathematical benchmarks in order to resolve the limitations of the HS algorithm and to further boost its usefulness and performance. The revised IDHS included a new scheme to enhance the local discovery and exploration capability of both the DE/best/1/Bin and the DE/rand/l/Bin. A new pitch change law was added to increase the convergence pace of the HM. The empirical findings of a classic research analysis and the findings of a comparative analysis showed that the IDHS surpassed eleven algorithms recorded in this area, including HS variants and other established optimizers. In Ref. [34], an online Variable-Fidelity Surrogate-assisted HS algorithm (VFS-HS) was created for solving the costly problems of engineering design optimization. A new model-making technique was used in the VFS-HS which employed a multilevel screening method focused on non-dominated screening in order to test the numbers of low loyalty evaluations and to preserve the balance between screening and exploitation. The optimization results revealed that the VFS-HS surpassed all of the methods compared. In Ref. [35], an in-depth analysis was performed of the HS core definition and an examination was made of the more current feature optimization versions. They included a sample of about 40 recently published papers on the creative uses of HS in smart management. HS research has reached a remarkable state, and there are still many challenges and potential application areas continually emerging for HS. In Ref. [36], a Parallel Chaotic Local Search Modified Harmony Search (MHS-PCLS) algorithm was proposed. This algorithm conducted a simultaneous chaotic locally check through different initial points, decreasing the specificity of the original chaotic maps. This method enhanced the search's robustness. Several established benchmark problems were checked, and optimized tests concluded the MHS-PCLS performed superior in contrast to other HS models. In Ref. [37], an Enhanced HS algorithm with Circular Region Perturbation (EHS-CRP) was used to increase the search performance of the HS algorithm. The EHS-CRP algorithm was built to increase the algorithm's search speed by a global and local dimensional filtering technique. To enhance the balance between discovery and exploitation and circular zone destruction, a selection-operator focused on global and regional average rates was introduced. A simulation demonstrated a significant benefit for the EHS-CRP in terms of precision, consistency, stability and robustness. A Self-adaptive Global best HS (SGHS) for solving continuous optimization problems was proposed in Ref. [38].