# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 6 Simulation and Analysis
### Comparison with Other Algorithms
#### 6.2.3 Environment with 40 \(\times\) 40 Grids and 50 \(\times\) 50 Grids
Figures 26 and 29 indicate that, in more complex map environments, the ACO algorithm struggled to converge within 100 iterations. Although the convergence speed of other optimization algorithms, including the proposed EACI algorithm, decreased as the map size increased, the EACI algorithm still achieved the fastest overall convergence. Figures 27 and 30 show that a large number of ants were lost during the iteration process of ACO. As the environmental complexity increased, the number of lost ants also rose for the other algorithms; however, the EACI algorithm consistently maintained the lowest number of lost ants throughout the process. Table 3 further validates the above conclusions. In the 40 \(\times\) 40 grid environment, the shortest path length generated by the EACI algorithm was 59.2548, which was 4.6863 and 2.5858 shorter than those of ACO and IACO, respectively, and it was approximately equal to the optimal path length obtained by the MAACO algorithm. In addition, the standard deviation of the optimal path length calculated by EACI over 20 simulations was 0.2335 (0.2335 \(<\) 0.3615 \(<\) 0.6412 \(<\) 2.1236), which was significantly lower than those of the other algorithms, indicating strong stability. The table also shows that the average number of iterations for EACI was 6.2, whereas those for MAACO and IACO were 2.53 and 4.21 times higher, respectively. ACO, on the other hand, reached the maximum iteration limit, further demonstrating its difficulty in converging in complex environments. As for the average number of lost ants, EACI reported only 47.6, which was considerably lower than CMEACO (189.3), IACO (694.5), and ACO (3993.8). In the 50 \(\times\) 50 grid environment, the shortest path generated by EACI was 72.8112, outperforming IACO (74.8112) and MAACO (74.5685). The path length obtained by ACO was 81.5980, which was 8.7868 longer than that of EACI. The standard deviation of EACI's optimal paths in this setting was 0.2707 (0.2707 \(<\) 0.3813 \(<\) 0.7122 \(<\) 2.5233), again lower than the other algorithms, indicating better stability. Regarding average iteration times, EACI required only 7.1 iterations, representing a reduction of 59.66% and 74% compared to MAACO and IACO, respectively. The average number of lost ants for EACI was 99.2, which was 295.2, 780, and 3889.5 fewer than those of MAACO, IACO, and ACO, respectively. Although EACI did not exhibit a significant advantage in the total number of path total corner angle compared to the other algorithms, it still stood out in terms of overall performance. In general, EACI was simulated in the scales of 20 \(\times\) 20, 30 \(\times\) 30, 40 \(\times\) 40, and 50 \(\times\) 50 respectively, and the simulation results were compared with other algorithms. It was found that EACI effectively improves the convergence rate and reduces the number of lost ants through the setting islands between the beginning position and the end position, improving the pheromone initialization strategy and pheromone updating rules. As such, it has good application prospects.