# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.2 Analysis of IEALL  
In this experiment, the comparison algorithm of IGWO is particle swarm optimization (PSO) and original gray wolf optimization (GWO). The initial population of IGWO algorithm is set to 30, and the maximum number of population iterations is 500. Due to the optimization process of the algorithm, there is a certain randomness that makes the results inevitably have some deviations. In this experiment, all the algorithms are run 10 times, and the average of 10 times is the final result of the experiment, which can reduce the deviation caused by randomness and evaluate the performance of the algorithm more accurately. Experiment 1: Test and comparison of IGWO optimization performance. In order to prove that the IGWO algorithm proposed in this paper has stronger optimization performance than the classical optimization algorithm, this experiment is based on the Sphere function, Rastrigin function, and Grienwank function in the benchmark function, the results are compared and analyzed by using PSO algorithm, GWO algorithm, and IGWO algorithm, respectively. It is mainly evaluated from two aspects: global optimization accuracy and stability of the algorithm. (1) Analysis and comparison of global optimization accuracy  
In the experiment, PSO, GWO, and IGWO algorithms were used to optimize the Sphere function, Rastrigin function, and Grienwank function, respectively. The results of 10 experiments and averaging are shown in Table 4.  
According to Table 4, the IGWO algorithm found the optimal solution 0 after 10 experiments under the test of single-peak benchmark function Sphere. Therefore, under the test of unimodal benchmark function, IGWO optimization effect is better than PSO and GWO algorithm. The results of IGWO are all equal to 0 or the closest to 0 under the tests of multi-peak benchmark functions Rastrigin and Grienwank. Therefore, under the multi-peak benchmark function, IGWO can successfully jump out of the local optimal solution to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark Function** & **PSO** & **GWO** & **IGWO** \\ \hline Sphere & 0.1732 & \(2.21\times 10^{-6}\) & 0 \\ Grienwank & 0.0741 & \(9.65\times 10^{-5}\) & 0 \\ Rastrigin & 0.0431 & \(5.52\times 10^{-5}\) & \(2.01\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average results of convergence accuracy of different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **MAE** \\ \hline ARIMA & 8.7315 & 18.594 & 5.4372 \\ LSTM & 5.5331 & 11.5429 & 3.7613 \\ ARIMA-LSTM & 1.5762 & 6.5841 & 1.0961 \\ EALL & 0.9181 & 4.2325 & 0.7386 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of prediction results of each model. find the global optimal solution, and the optimization effect is better than PSO and GWO algorithms. In general, IGWO algorithm has better global optimization performance than PSO and GWO under different benchmark functions. (2) Stability analysis and comparison of algorithms  
Algorithm stability is also an important evaluation criterion for optimization algorithms. The mean square error results of 10 times of the above three models are shown in Table 5.  
It can be seen from Table 5 that under the unimodal benchmark function Sphere test, IGWO algorithm found the optimal solution in 10 trials, and the mean square error was 0. Therefore, under the unimodal benchmark function test, IGWO optimization stability is better than PSO and GWO algorithm. Under the multi-modal benchmark functions, Rastrigin and Grienwank test, the mean square error results of IGWO are smaller than those of PSO and GWO algorithms. In general, under different benchmark functions, IGWO algorithm has more stable optimization effects than PSO and GWO. The benchmark function optimization test results of the IGWO, PSO, and GWO algorithms were compared, and it was found that the IGWO method had higher convergence accuracy, faster convergence speed, and stronger stability than the PSO and GWO algorithms.