# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 3 Harmony Search Applications in China
### Computer Sciences  
In Ref. [23], the HS algorithm and Variable Neighborhood Descent (VND) were viewed as a hybrid heuristic algorithm. A Modified Harmony Search (MHS) algorithm was used during the research to deliver global exploration capabilities and to use the VND to scan locally. Population variation was exploited by entropy to prevent premature solution convergence. The tests of the Lackner problems concluded that the recommended algorithm was compatible with the literature's strongest current algorithms. To synergistically overcome complicated problems of optimization using a self-adaptive selection approach, [PERSON] et al. [24] developed a hybrid search algorithm mixing HS and Teaching Learning Based Optimization (TLBO). HS and TLBO were updated in the HSTLBO to accommodate the global minimum and discovery capacities. HS focused on discovering new areas and TLBO on using high-precision approaches rapidly in the well-known regions. Experimental findings were higher than five state-of-the-art HS models. A stronger exploration efficiency than five TLBO versions was observed. Figure 2: Flowchart of the proposed INGHS algorithm [22]. In Ref. [25], three main features described an enhanced HS algorithm, local opposition-based self-adaptation learning search called LHS:  
1. adaptive global pitch adjustment for increasing exploitation capability;
2. using the OBL approach to enhance solution diversity; and
3. use of competition selection strategies to increase solution precision and increase the potential of avoiding local optima. The LHS method was compared to HMS and HMCR. LHS was determined to be the best with respect to accuracy, conversion speed and robustness of the proposed LHS algorithm. [PERSON] et al. [26] formed an improved HS with Dual Strategies and Adaptive Parameters (DSAHS). DSAHS introduced the latest harmony-oriented, dynamically driven search techniques to manage the potential for discovery and manipulation. In comparison, with heuristic data from the search process, the DSAHS adapted its key parameters. The DSAHS exhibited satisfactory outcomes for most of the research functions, relying on tests and measurements focused on a set of well-established research functions. [PERSON] et al. [27] identified a novel Gaussian Mutation HS algorithm (GMHS) for Multi-objective Optimization Problems (MOPs). Several improvements were made in a GMHS, including modification of the HMC rule and using two bandwidths and chaotic maps to increase global search ability. A Gaussian mutation operator was applied to speed up the convergence rate and to escape from the local optimum. The experimental findings found GMHS competitive with other multi-objective evolutionary algorithms for convergence and diversity efficiency. [PERSON] et al. [28] developed an upgraded global HS with generalized OBL Strategies (GOGHS) to increase the operational efficiency of the regular HS. In the GOGHS, specific knowledge was used to boost the operating efficiency from the strongest harmony. A series of benchmarking test functions was evaluated and the output of GOGHS was compared to several HS variants. Data from the analysis demonstrated that the GOGHS produced successful outcomes. In Ref. [29], in order to address issues of conventional HS in complex function multi-objective optimization, a MOP HS parallel algorithm focused on cloud computing was introduced. The Map Reduce parallel programming framework was implemented in their analysis. The three-part parallel algorithm was given as follows. 1. started the master library of harmony and splitting the sub libraries. 2. initialized the Map Reduce HS (MR-DHS) algorithm parameters on the Hadoop clusters and set the dynamic parameter. 3. measured the reduction method and achieved the new solution. Experimental studies have shown that the proposed algorithm's optimization efficacy exceeds most known optimized HS versions. For comparison, Figure 3 displays the flowchart of the HS parallel functional parameter algorithm.