# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## 5 Experimental Results
### Benchmark Function Validation
#### 5.1.4 Qualitative Analysis  
The purpose of this part is to undertake a qualitative study of CBBOA. In the first instance, the function of CEC14 is subjected to a feasibility study. The findings of the feasibility study of CBBOA and BOA are shown in Figure 5. In the illustration, there are five columns. The figure shows five columns of data, from left to right, indicating the three-dimensional distribution, two-dimensional distribution, and one-dimensional distribution of the search trajectory of CBBOA in the multidimensional space, the variation of the average fitness, and the convergence curve, respectively. In Figure 5b, the red dot shows the position of the optimum solution, and the black dot represents the location of the search for CBBOA, which is shown in the lower right corner. The fact that the black dots are dispersed over the whole search plane in the picture indicates that CBBOA is capable of traversing the solution space to the greatest extent conceivable. The black dots closest to the best answer are the densest, indicating that CBBOA can define the target region and perform additional development in this part of the world. The trajectory curve in Figure 5c fluctuates greatly in the early period and tends to stabilize in the later period. The fluctuation of the trajectory indicates that the algorithm is searching extensively in the early period. When the algorithm finds the target area, the trajectory becomes stable. In Figure 5d, the average fitness decreases during the iteration. The average fitness dropped to a lower value in the mid-term, indicating that the CBBOA has a good convergence speed. In Figure 5e, the convergence curve of CBBOA is lower than BOA. This shows that the quality of the solutions found by CBBOA is better. Figure 4: Convergence curves of CBBOA and its variants (First row: F1, F3, F8; second row: F10, F11, F16; third row: F21, F24, F25). Figure 6 shows the results of the balanced analysis of CBBOA and BOA, tested on the same functions as the diversity analysis above. The results show the presence of three curves, red, blue, and green, in each graph. Red indicates the exploration capability of the algorithm, blue indicates the exploitation capability, while green is the increment-decrement curve, which is used to describe the trend of the red and blue curves, with the curve rising, indicating the dominance of exploration. On the contrary, exploitation behavior dominates. From the figure, we can see that the two behaviors are at the same level when the incremental-decremental curve reaches the maximum. From the selected graphs, we can see that the added mechanisms have a great influence on the balance of the original BOA. The original BOA maintains a high exploration and low exploitation trend in functions. However, from the proportion of the two behaviors, we can see that the proportion of BOA exploration behavior is too high. For example, in F10, exploration accounts for more than 90%. This may lead to BOA not being able to get a high-quality solution. In contrast, the exploitation behavior of CBBOA accounts for a higher proportion. It means that it spends most of its time exploiting the target area. Figure 7 is the result of the diversity analysis. From the figure we can see that the diversity curves are all decreasing curves. The reason is that the algorithm randomly generates the population at the beginning, so the diversity at the beginning is large. In the process of algorithm iteration, the continuous narrowing of the search range makes the population diversity continue to decrease. As can be seen from Figure 7, the population diversity of BOA is maintained at a high value in multiple functions. This shows that BOA has been kept in a large search range and cannot determine the target area, which makes the algorithm sometimes unable to find high-quality solutions.