## 4 Results and Discussion  
This section discusses the simulations used to assess the performance of our proposed algorithm in solving the UAV path planning problem. Seven algorithms are tested under three scenarios, including MSFOA, FOA, PSO, DE, ABC, IFFO, and MFOA. The 3D stereo displays of three scenarios are shown in Figure 1. The algorithm is coded using Matlab-2018b. Owing to their stochastic nature, evolutionary algorithms may arrive at better or worse solutions than they previously reached during their search for new solutions. For this reason, every tested algorithm is run 40 times independently for each scenario and the statistical results are used for performance evaluation and comparison. We coded the above algorithms and set the parameters according to their references. The main parameters of the seven tested algorithms are detailed in Table 1. The best values of each scenario are highlighted using boldface. The parameter configurations are all based on the suggestions in the corresponding references; where these values have a fair comparison, the same maximum iteration number is used for all algorithms as the stopping criterion, while the population size is set as 100 for all algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Population Size** & **Control Parameters** \\ \hline MSFOA & \(M_{pop}=100\) & The swarm amount \(G=5\), the mutation \\  & & parameters \(\mathit{coe}_{1}=0.8\), \(\mathit{coe}_{2}=0.2\). \\ FOA [18] & \(M_{pop}=100\) & No more parameters. \\ PSO [13] & \(M_{par}=100\) & The inertial weight coefficient \(w\) varies from 0.9 \\  & & to 0.4, the acceleration coefficients \(c_{1}=c_{2}=2\). \\ DE [10] & \(M_{sol}=100\) & The scaling factor \(F\) is set as a random value in \\  & & [0.2, 0.9], the crossover factor \(cr=0.9\). \\ ABC [11] & \(N_{e}=50\), \(N_{u}=50\) & The largest local searching times \(Limit=20\). \\ IFFO [24] & \(M_{pop}=100\) & The maximum radius \(\lambda_{max}=0.5\), the minimum \\  & & radius \(\lambda_{\text{min}}=0.01\). \\ MFOA [23] & \(M_{pop}=100\) & The multi-swarm amount \(M=5\). \\ \hline \hline \end{tabular} Abbreviations: MSFOA, multiple swarm fruit fly optimization algorithm; FOA, fruit fly optimization algorithm; PSO, particle swarm optimization; DE, differential evolution; ABC, artificial bee colony; IFFO, improved fruit fly optimization; MFOA, multi-swarm fruit fly optimization algorithm.  
\end{table} 
Table 1: The parameters used in experiments of different algorithms. The 3D stereo displays of the seven UAV paths in the three scenarios are shown in Figures 2-4, where (a)-(g) correspond to MSFOA, FOA, PSO, DE, ABC, IFFO, and MFOA, respectively. The statistical results of MSFOA, FOA, PSO, DE, ABC, IFFO, and MFOA during 40 runs on the three different scenarios are listed in Table 2, where the best of the cost values of each UAV are recorded. Figure 1: (a,c,e) are the three-dimensional (3D) displays of the three simulation scenarios, which show the terrain and threat sources in the mission map; (b,d,f) are the corresponding 2D views of the simulation scenarios. Figure 2: _Cont._  
Figure 3: _Cont._  
Figure 2: Two-dimensional displays of the best unmanned aerial vehicle (UAV) paths obtained by (**a**) MSFOA, (**b**) FOA, (**c**) PSO, (**d**) DE, (**e**) ABC, (**f**) IFFO, and (**g**) MFOA in the first scenario. Figure 4: _Cont._  
Figure 3: Two-dimensional displays of the best UAV paths obtained by (**a**) MSFOA, (**b**) FOA, (**c**) PSO, (**d**) DE, (**e**) ABC, (**f**) IFFO, and (**g**) MFOA in the second scenario. Figure 4: Two-dimensional displays of the best UAV paths obtained by (**a**) MSFOA, (**b**) FOA, (**c**) PSO, (**d**) DE, (**e**) ABC, (**f**) IFFO, and (**g**) MFOA in the third scenario. For the first scenario, all seven tested algorithms find the safe flight path for UAVs. It can be seen that the best path generated by MSFOA flies the smoothest.