# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 6 Simulation and Analysis
### Comparison with Other Algorithms
#### 6.2.2 Environment with 30 \(\times\) 30 Grids  
In the complex environment, the start and end positions were assigned sequence numbers 871 and 30, respectively. The initial pheromone distribution of the EACI is depicted in Figure 20, while Figure 21 shows the pheromone distribution across the different iterations. To ensure a more objective evaluation and to minimize the impact of random factors, each simulation experiment was repeated 20 times. The simulation results of the four algorithms--ACO, IACO, CMEACO, and EACI--are shown in Figure 22, where each algorithm generates an optimal path. The convergence curves of the optimal paths for these algorithms are presented in Figure 23, while Figure 24 illustrates the number of lost ants produced by each algorithm. Additionally, the average values of the relevant parameters were calculated, with the detailed statistical results provided in Table 2.  
Figure 19: Number of lost ants generated by the four algorithms in the 20 \(\times\) 20 grid environment.  
As shown in Figures 20 and 21, the pheromone aggregation speed of the EACI algorithm was slower in a complex map than in a simple map. Figure 21a,b indicate that, within five iterations, a significant amount of pheromones has already accumulated near the optimal path. Figure 21c shows that, within 15 iterations, the pheromones have completely concentrated on the optimal path. The results in Figure 23 indicate that the EACI algorithm achieved optimal route detection as early as the second iteration, showcasing its efficient convergence.  
Figure 21: Pheromone distribution in the EACI algorithm at different iterations in the 30 \(\times\) 30 grid environment: (**a**) after 1 iteration, (**b**) after 5 iterations, (**c**) after 15 iterations, and (**d**) after 30 iterations.  
Figure 20: Initial pheromone distribution of EACI in the 30 \(\times\) 30 grid environment.  
Figure 23: Convergence curve of the optimal path for the four algorithms in the \(30\times 30\) grid environment.  
Figure 24: Number of lost ants generated by the four algorithms in the \(30\times 30\) grid environment.  
Figure 22: Optimal path planning results of the four algorithms in the \(30\times 30\) grid environment.  
As depicted in Figure 22, all four algorithms successfully identified a path from the starting points to the endpoints. The comparison in Figure 23 illustrates that EACI outperformed the other algorithms by producing the shortest path and converging at the fastest rate. Furthermore, Figure 24 illustrates that, as the iterative times increased, the occurrence of deadlock ants gradually decreased for all algorithms. Notably, EACI generated the fewest deadlock ants and exhibited the most rapid decline in their numbers. As presented in Table 2, the shortest path length generated by EACI was 44.53, which aligns with the results of IACO and CMEACO, but it was 0.83 shorter than that of ACO. Moreover, the average path length produced by EACI was also 44.53, which is comparable to that of CMEACO; however, it was 0.69 and 1.36 shorter than those of IACO and ACO, respectively. The two parameters that showed the most significant changes, as shown in Table 2, were the average iterations and the mean number of lost ants. The average iterative times calculated by IEAC was 1.4, which was approximately 1/11 of CMEACO, 1/21 of IACO, and 1/77 of ACO. Regarding the average number of lost ants, EACI produced a value of 27.5, which was 210.2 fewer than CMEACO, 384.3 fewer than IACO, and 3084.9 fewer than ACO. The total turning angle of the route was 765\({}^{\circ}\), which was slightly higher than those of CMEACO and ACO but lower than that of IACO. This observation suggests that there is still potential for improvement in optimizing the total turning angle within the EACI algorithm. Overall, the data above clearly demonstrate that EACI exhibited outstanding performance.