## 5 Experimental Simulation Analysis
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm Name** & \begin{tabular}{c} **Initial** \\ **Population** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **Iterative** \\ **Convergence** \\ **Times** \\ \end{tabular} & **Optimal Fitness** & **Average Fitness** & \
\begin{tabular}{c} **Initialization** \\ **Runtime/s** \\ \end{tabular} &
\begin{tabular}{c} **Iteration** \\ **Runtime Time/s** \\ \end{tabular} \\ \hline PSO algorithm & 500 & 1000 & 153 & 4618.3186 & 4631.182 & 0.167 & 82.169 \\ VANDIWPSO & 500 & 500 & 1 & 4637.3815 & 4637.3815 & 0.104 & 46.221 \\ IC-VANDIWPSO & 1000 & 500 & 1 & 4637.3815 & 4637.3815 & 0.604 & 43.509 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data comparison of three algorithms with three threats. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm Name** & \begin{tabular}{c} **Initial** \\ **Population** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **Iterative** \\ **Convergence** \\ **Times** \\ \end{tabular} & **Optimal Fitness** & **Average Fitness** & \
\begin{tabular}{c} **Initialization** \\ **Runtime/s** \\ \end{tabular} &
\begin{tabular}{c} **Iteration** \\ **Runtime Time/s** \\ \end{tabular} \\ \hline PSO algorithm & 500 & 1000 & 360 & 5721.9739 & 6058.1241 & 1.134 & 103.802 \\ VANDIWPSO & 500 & 500 & 158 & 5422.4877 & 5437.8974 & 0.754 & 53.884 \\ IC-VANDIWPSO & 1000 & 500 & 25 & 5196.9241 & 5229.1847 & 0.629 & 51.382 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Data comparison of three algorithms with five threats. Figure 15: The top view of the paths generated by the IC-VAINDIWPSO algorithm under three threats. The positions of the two threats are randomly and not repeatedly changed in every experiment, and 10 groups of distinctive environmental models are generated, performing 30 simulation tests on the above three algorithms in distinct environmental models and calculating the average value of the fitness function. The outcomes are shown in Figure 18. It can be seen from the analysis of the simulation consequences that in contrast with the PSO algorithm and the VAINDIWPSO algorithm, the fitness function value of the IC-VAINDIWPSO algorithm is typically lower, displaying a greater optimization ability. Figures 19-22 are the top views of the PSO algorithm and the IC-VAINDIWPSO algorithm in the fourth set of data and the ninth set of data generation paths. Figure 16: The top view of the paths generated by the PSO algorithm under five threats. Figure 17: The top view of the paths generated by the IC-VAINDIWPSO algorithm under five threats. Figure 18: The average value of fitness functions for different environments. Figure 19: The top view of the path generated by the fourth set of data for the PSO algorithm. Figure 20: The top view of the path generated by the fourth set of data for the IC-VAINDIWPSO algorithm. Table 8 shows the data in Scenario 4 of Reference [21]. It can be seen from the data that the fitness function value and convergence algebra of the PSO algorithm are higher than the quantum-behaved PSO algorithm (QPSO), the genetic algorithm (GA), and differential evolution (ABC). From the data in Table 5, it can be considered that the fitness function cost and convergence algebra of the IC-VAINDIWPSO are much higher quality than the PSO algorithm. Although the environment of this paper and the literature [21] are different, they are each chosen from Christmas Island, Australia, so evaluating Tables 6 and 8 horizontally, it can be seen that the IC-VAINDIWPSO algorithm is higher than the QPSO algorithm, the GA algorithm, and the ABC algorithm, so the IC-VAINDIWPSO algorithm, a variant of the PSO algorithm, is very valuable for in-depth study. According to all the experimental results and analysis, it can be concluded that the VAINDIWPSO algorithm introduces nonlinear decreasing dynamics inertia weight and adds random disturbances so that the algorithm can jump out of the local optimum.