# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 5 Experimental Analysis
### Performance Summary and Internal Cause Analysis  
From Figures 5 and 6, and Tables 4 and 5, it is apparent that the APC algorithm has some advantages for edge detection. In this section, we try to reveal the internal reasons for the experimental results presented in Sections 5.1 and 5.2. The performance comparison summary of different artificial intelligence algorithms is shown in Table 6, where the main indexes include the artificial swarm, time performance, fitness comparison, and variable population size. It is assumed that all algorithms use the same the population size \(m\) and iteration steps \(t\) to solve the edge detection problem in an image with \(n\) pixels. The artificial algorithms include particle swarm optimization (PSO) [18; 40], ant colony optimization (ACO) [19; 21], the artificial bee colony (ABC) [5; 41], the genetic algorithm(GA) [22; 42], the artificial fish swarm algorithm (AFSA) [23], the salp swarm algorithm (SSA) [24], bird swarm optimization (BSO) [25], the naked mole-rat algorithm (NMRA) [26], simulated annealing (SA) [26], the firefly algorithm (FA) [30; 41], the grey wolf optimizer (GWO) [32], the sparrow search algorithm (SSA) [43], and the whale optimization algorithm (WOA) [44]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection** & **Artificial** & **Time** & **Fitness** & **Variable** \\
**Algorithms** & **Swarm** & **Performance** & **Comparison** & **Speed** \\ \hline \hline APC & Artificial plant & & & \\  & community & & & \\ ABC [5; 41] & Artificial bees & \(O(mnt)\) & Often once & Fixed \(m\) \\ FCM [9; 14; 31; 32] & Fuzzy classes & \(O(mnt)\) & Often once & Fixed \(m\) \\ CNN [14; 15; 16; 17; 34; 35; 36; 37] & Artificial neurons & \(O(n^{2}m^{2}c_{in}c_{out})\) & Often once & Fixed \(m\) \\ PSO [18; 40] & Artificial particles & \(O(mnt)\) & Often once & Fixed \(m\) \\ ACO [19; 21] & The ants & \(O(mnt)\) & Often once & Fixed \(m\) \\ GA [22; 42] & Chromosomes & \(O(mnt)\) & Often once & Fixed \(m\) \\ AFSA [23] & Artificial fish & \(O(mnt)\) & Often once & Fixed \(m\) \\ SSA [24] & Salp swarm & \(O(mnt)\) & Often once & Fixed \(m\) \\ BSO [25] & Bird swarm & \(O(mnt)\) & Often once & Fixed \(m\) \\ NMRA [26] & Naked mole-rats & \(O(mnt)\) & Often once & Fixed \(m\) \\ SA [26] & Null & \(O(nt)\) & Often once & Null \\ FA [30; 41] & Fireflies & \(O(mnt)\) & Often once & Fixed \(m\) \\ GWO [32] & Grey wolves & \(O(mnt)\) & Often once & Fixed \(m\) \\ SSA [43] & Sparrows & \(O(mnt)\) & Often once & Fixed \(m\) \\ WOA [44] & Whales & \(O(mnt)\) & Often once & Fixed \(m\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison summary of different artificial intelligence algorithms. Table 6 shows that the time performance of the swarm learning algorithms can be described as \(O(mntt)\), which is nearly linear, and is related to the image scale \(n\), the population size \(m\), and the number \(t\) of iterations. Swarm learning algorithms with similar time performances include the proposed APC, the ABC [5,41], FCM [9,14,31,32], PSO [18,40], ACO [19,21], GA [22,42], AFSA [23], SSA [24], BSO [25], FA [30,41], GWO [32], SSA [43], and the WOA [44]. The time performance of the CNN [14, 15, 16, 17, 34, 35, 36, 37] is related to the scale \(n\) of the image, the population size \(m\) of the convolution cores, the number \(c_{in}\) of input channels, and the number \(c_{out}\) of output channels. However, there is no swarm learning mechanism in SA [26], and its time performance is decided by the image scale \(n\) and the number \(t\) of iterations. To sum up, some special characteristics can give the APC some advantages in terms of its solving performance. First, the three fitness comparisons conducted per iteration in the APC algorithm; this is higher than in most AI algorithms, which often only include one fitness comparison. The seeding, growing, and fruiting operations all compare fitness levels and select individuals with high fitness levels to survive, while individuals with low fitness levels die.