# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 5 Experiments and Discussion
### Comparisons between IBSO and Several Heuristic Algorithms  
Besides being compared with BSO, IBSO was also compared with four original heuristic algorithms including the genetic algorithm (GA) [37], the ABC [8], the PSO [7], and the ASFA [9]. The parameters of the four algorithms are set based on Table 3.  
The performance of the algorithm is evaluated from two aspects. One is the quality of the solution, the other is the running time of the algorithm. The quality of the solution is expressed by \(T(V)\) \begin{table}
\begin{tabular}{c c c} \hline
**GA** & **ABC** & **AFSA** & **PSO** \\ \hline Population Size: 20 & Population Size: 20 & Fish Size: 20 & Particle Size: 20 \\ \hline \multirow{2}{*}{Crossover Factor: 0.618} & Food Source: (Population Size/2) & Times of foraging attempts: 10 & Inertia weight: 0.6 \\ \hline \multirow{2}{*}{Mutation Factor: 0.03} & Times Limitation of Food Source Not Update: 100 & Crowding Factor: 0.9 & Acceleration Coefficient 1: 0.7 \\ \cline{2-3}  & & Visual Range: \(N/2\) (\(N\) is nodes’ number) & Acceleration Coefficient 2: 0.3 \\ \cline{2-3}  & & Step Possibility: 2/7 & Upper and Lower Limits of Speed: 0.95 \\ \hline \end{tabular}
\end{table} 
Table 3: Parameter setting of four heuristic algorithms. Figure 6: Comparison between the BSO and the IBSO. For each benchmark, each algorithm is run 10 times and the results is averaged over the 10 runs. The experimental results are shown in Figure 7.  
It can be seen from Figure 7(a) that the \(T(V)\) of the IBSO algorithm is the minimum, which means that the IBSO algorithm can get the solution with the best quality among the six algorithms. The solution quality of the AFSA is approximate to that of the IBSO. The solution quality of the BSO is approximate to the solution quality of the GA and the PSO, but they are inferior to the IBSO. It can be seen from Figure 7(b) that the IBSO has the shortest running time. The BSO is approximate to the IBSO. The AFSA had the longest running time. From the two aspects of the solution quality and the running time, we can draw the conclusion that the IBSO performs best. In order to further demonstrate the performance of the IBSO, we introduce the quality gain \(\alpha\) and the running time gain \(\beta\) in this paper. The calculation of \(\alpha\) and \(\beta\) is shown in (6) and (7) respectively. \[\alpha=\frac{a-a^{*}}{a}\times 100\% \tag{6}\]  
\[\beta=\frac{b-b^{*}}{b}\times 100\%, \tag{7}\]  
where \(a^{*}\) and \(b^{*}\) are the \(T(V)\) and the running time of the IBSO, \(a\) and \(b\) are the \(T(V)\) and the running time of other algorithms compared to the IBSO. The experimental results about \(\alpha\) and \(\beta\) are shown in Table 4.  
It can be seen from Table 4 that compared with the BSO, the solution quality of the IBSO was improved by 4.0 to 16.8%, and the average improvement was 9.4%. In terms of the running time, compared with the BSO, the IBSO reduced 0.2 to 11.3%. As for other heuristics algorithms, the IBSO performs better in the quality of solution and the running time. For the AFSA, which was approximate to the IBSO in the quality of solution, the running time of the IBSO was shorter 85.8% than that of