# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 5 Harmony Search Applications in Korea
### [PERSON] Sciences  
Geem [85] introduced two additional features to improve the range of solutions held in an HM, to find regional, high-quality and more reliable optimized solutions. * Initial harmonies produced more than HMS. * The amount of the same harmonies contained in the harmonic storage was reduced. After simulation, reducing the amount of equivalent harmonies in HM increased the solution efficiency with respect to global optimum frequency and objective practical performance. [PERSON] and [PERSON] [86] developed the Modified HS (MHS) for resolving restricted optimization problems, using the embedded limit management technique of the Fitness Priority-Based Ranking Method (FPBRM). Practical MHS implementations for mathematical issues confirmed that the developed MHS was more precise, more robust, and more effective than other reported algorithms. In Ref. [87], selection methods in the HM of HS algorithm were used. Choosing the value of the design variable from every option in the HM is a question of concern. The adopted methods for choosing were proportional, tournament, linear rank, and exponential rank. The results indicated that the parameters of the selection method need to be altered for HS efficiency. In Ref. [88], there are a broad variety of optimization challenges. Excellent findings are shown with core functions and technical implementations of HS. These applications involved water/groundwater system, management, medical, robotics, control, power and energy, etc. Figure 7: AEHS flow chart for an integrated lithium-ion battery configuration used in the parameter estimation [83]. In Ref. [89], the Traveling Salesman Problem (TSP) was presented with an algorithm focused on Advanced HS and Ant Colony Optimization (AHS-ACO) approaches. The ACO was used to search for the optimum local alternative within the space of an algorithm and to use the HS to escape from ACO's local optimum to obtain a global optimum. The process of the hybrid AHS-ACO is illustrated in Figure 8.  
In Ref. [90], a technique for changing the hyper parameters of the Convolutional Neural Network (CNN) feature removal stage using the PSF-HS algorithm was created. The hyper parameter was set to harmony in the PSF-HS algorithm and HM was generated after harmony generation. Afterwards, depending on the absence of a CNN, the HM was revised. After simulation, the amount of weights and prejudices to be learned were reduced and the precision of classification was increased by changing the hyper parameters of a CNN. [PERSON] and [PERSON] [3] investigated and focused, instead of implementations, on recent trends in the algorithm framework. They proposed describing HS as standard along with a set of updated hybrid HS methods. Examples include:  
- adaptation of simple HS initial operators,  
- parameter modification,  
- hybrid methods,  
- management of multi-target problems and  
- limit control. To overcome Virtual Machine Consolidation (VMC) by operator design, [PERSON] et al. [91] employed a grouping representation method. Table 3 briefly summarizes and gives brief descriptions and classifications of papers in Korea studied in various fields of study. Figure 8: Flowchart of AHS-ACO used for solving TSP [89].