# CDDO-HS: Child Drawing Development Optimization-Harmony Search Algorithm
## 6 Testing, Results, and Discussion
### CDDO-HS vs. CDDO and HS
#### 6.4.1 Classical Benchmark Functions  
The classical benchmark functions are a set of standard functions used to evaluate the performance of optimization algorithms. These functions are commonly used in the field of optimization and have been studied extensively [70]. Functions f1-f7 are called the unimodal functions because they only have one global optimum and no local optimums. They have a single solution and can be exploited as variants (see Tables A1-A3). Therefore, using these unimodal functions, it was possible to assess the CDDO-HS exploitation capabilities. Table 2 demonstrates that, compared to the CDDO in these functions, CDDO-HS had improved exploitation capabilities in two of the seven. We also discovered that all seven equations had significant outcomes when we compared the results of this hybrid algorithm to those of the HS algorithm. The results of the CDDO algorithm's transitions were denoted with boldface type, while those of the HS algorithm's transitions were denoted with underlining.  
To evaluate our suggested algorithm's performance in terms of exploration, we used multimodal functions ranging from F8 to F13 (see Tables 1 and A2) to analyze the local optimum avoidance. The ability to start from the local optima and continue the search across a wide range of regions of the search area could be tested by utilizing multimodal features in an exploration capacity test. To put it another way, they ranked the depth of the variation investigation for a broad set of local optimums. Five out of six multimodal functions (Table 2) suggested that CDDO-HS performed better. Hence, it could be asserted that CDDO-HS enhanced CDDO's exploratory capabilities. Consequently, when the results of this method were compared to the results of the HS algorithm, six out of seven outcomes were significant.  
The third section of these benchmark functions included fixed-dimension functions ranging from F14 to F19 (see Tables 3 and A3). Hence, avoiding the local minima was one of the most difficult challenges, because only by finding an optimal balance between exploration and exploitation could a local minimum be avoided to some extent. Due to the large number of local minima, the multimodal fixed-dimension benchmark functions were employed to test the algorithm's ability to avoid them. The numerical results of thebenchmark functions from F14 to F19 showed that CDDO-HS outperformed the CDDO and standard HS algorithms in terms of local minima avoidance, except for F14 and F18, where the CDDO-HS algorithm performed worse than the other algorithms. After a while, CDDO-HS was shown to compete with other cutting-edge metaheuristic algorithms, as evidenced by its remarkable findings.  
This showed that the algorithm could avoid the local minima because it looked at a lot of good places in the design space and chose the best one. The method used to avoid the local minima was that all of the search agents would change suddenly in the early stages of the optimization process and then progressively converge towards the best solution. This strategy later ensured that cooperative search agents eventually converged on a spot in the search space.