# New Hybrid Algorithms for Prediction of Daily Load of Power Network
## 4 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Main Parameters Setting** \\ \hline GWO & \(Population\_number=300,Max\_iteration=100\) \\ GWO-DE & \(Population\_number=300,Max\_iteration=100,pCR=0.01,\lambda=[0.02,0.08]\) \\ SFLA & \(Population\_number=300,Max\_iteration=100,memeplex=5\), \\ & \(Parents\_number=3,Sons\_number=3,Memeplex\_iteration=2\) \\ SGWO & \(Population\_number=300,Max\_iteration=100,memeplex=5\), \\ & \(Memeplex\_iteration=2\) \\ SGWOD & \(Population\_number=300,Max\_iteration=100,pCR=0.01,\) \\ & \(\lambda=[0.02,0.08],memeplex=5,Memeplex\_iteration=2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters setting of each algorithm. Unimodal functions have only a global optimum and no local optima, so they benchmark the utilization of algorithms. SGWO performs better than GWO-DE except for \(f_{6}\), and SGWOD is better than other compared algorithms. In the functions of \(f_{1}\), \(f_{2}\), \(f_{4}\) and \(f_{5}\), SGWO and SGWOD are almost identical in the convergence curves, but SWGOD performs better than SGWO in \(f_{3}\), \(f_{6}\) and \(f_{7}\). From Figure 6, we find that they have faster searching ability in the function with only a global optimum. Multimodal functions have exponential local optima, they test the algorithm whether to avoid local optima. It is observed from the experimental results that the proposed algorithms perform better than other algorithms in most multimodal functions. In multimodal functions, the convergence curves of SGWO and SGWOD are almost identical in \(f_{8}\), \(f_{9}\) and \(f_{10}\). SGWO is superior to SGWOD in \(f_{11}\), while SGWOD is superior to SGWO in \(f_{12}\) and \(f_{13}\). They perform better than GWO, GWO-DE and SFLA. They can not only converge quickly, but also avoid local optimum and finally they find the global optimum, which shows that they effectively exchange global information. While in fixed-dimension multimodal functions, they perform better than other compared algorithms except that the final convergence of \(f_{14}\) is not as good as SFLA. Figure 7: Convergence curves of multimodal benchmark functions. Figure 8: Convergence curves of fixed-dimension multimodal benchmark functions. Composite multimodal functions have extremely complex structures with many randomly located global optimum and several randomly located deep local optima. In composite functions, although the compared algorithms are not good in the convergence curves, as seen from Figure 9, their convergence speed are very fast and they are better than other compared algorithms except for the final convergences of \(f_{24}\) and \(f_{27}\) are not as good as GWO-DE. It sees that GWO-DE has a large fluctuation, while SWGO and SGWOD haven't the problem. Their convergence curves are relatively smooth, which means that they do better than the previous generation for each iteration. From the Table 6 and Figures 6-9, we conclude that the proposed algorithms have improved the ability of global search and quickly converge in the iteration process. Therefore, they have better performances in terms of convergence speed and accuracy. the data before and after, and build the association model. Then it predicts the future through the historical data and the established model. The neural network is a method that uses the sum of squared errors as the fitness function and finds the optimum by gradient method. But it has some inherent defects, such as slow learning speed, low precision, and easy falling into local minima. The meta-heuristic algorithm is a global optimization process. It has been widely used in training the parameters of the neural network because it can find global solutions in the multi-dimensional search space. The parameters of the neural network are optimized by the meta-heuristic, then the neural network is used to further accurately optimize the acquired network parameters. So we use the proposed methods to train the network and finish the prediction.