## 7 Experiments and Results Analysis
### Parameter Analysis
#### 7.2.3 The Analysis of Cycle  
Set \(SN\) = 30, _Limit_ = 30, when the _Cycle_ number is 30, 60, 90, respectively, the fitness of the path generated by the OEABC is shown in Figure 7.  
Figure 6: The fitness of the path when the _Limit_ is different.  
As can be seen from Figure 7, When _Cycle_ = 30, in the 30 paths generated by the algorithm, the change amplitude of fitness is greater than 10% of the average (that is, the fitness is more than 57 or less than 47), which is only the 16 th path with a value of 60, merely 3% of the paths are more than 10% of the average fitness. The reason is that _Cycle_ is too small and there are not enough iterations of the solution, so it is difficult to generate the path with higher fitness.  
When _Cycle_ = 60, compared with the previous round, the average fitness of the current round increased by nearly 10% from 52 to 58, because the _Cycle_ value is large enough to enable the solution to be fully iterated.  
When _Cycle_ = 90, compared with _Cycle_ = 60, the average fitness rose from 58 to 61, an increase of about 5%. Although the further increase of the _Cycle_ enables the solution to be further optimized and the best solution 70 appears, the improvement decreases gradually. This is because the initial solution of the ABC algorithm and the solution generated by the scout bee are both stochastic, if the random solution is not good, it is difficult to generate the solution with high fitness by simply increasing the number of iterations.  
Therefore, the bigger the _Cycle_ is, the higher the fitness of the algorithm generating path is. It can also be seen that when the _Cycle_ rises from 30 to 60, the average fitness increase is significantly greater than when the _Cycle_ rises from 60 to 90. That is to say, when the _Cycle_ reaches a certain value, the effect of improving path fitness will be worse and worse simply by adding to the _Cycle_ number. The _Cycle_ number not only maintains a proportional relationship with the _SN_, _Limit_ and other parameters, but also considers the computational ability of nodes to ensure that the solutilon can be quickly iterated.  
In summary, through the analysis of the _SN_, _Limit_ and _Cycle_ parameters, it can be seen that the fitness of the path generated by OEABC with different parameters changes greatly, which has a great impact on the performance of the OEABC. The _SN_ represents the size of the bee colony, and a larger colony will be able to cover a wider range of solution space, effectively improving the probability of finding an optimal solution. Through the roulette mechanism, OEABC implements positive feedback on the path of higher fitness, by conducting the _Cycle_ number of rounds of search in the neighborhood, iterate to find the path with higher fitness, but the problem is that it is easy to fall into a local optimum. The scout bee mechanism is used to solve this problem, and when a path cannot be optimized after the _Limit_ iterations, it will be discarded and a random path will be generated, ensuring an opportunity to continue searching for the optimal solution globally. In the specific application scenario, selecting parameters not only considers the performance of the OEABC algorithm itself, but also should take into account the size of the body area network, the computational ability of nodes and specific requirements that need to be addressed.  
Figure 7: The fitness of the path when the number of _Cycle_ is different.