# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 1 Introduction
[33] introduced an improvement adaptive ant colony algorithm (IAACO), which refines the transition probabilities and incorporates an adaptive pheromone update mechanism to further optimize performance. To minimize the number of lost ants, [PERSON] et al. [34] implemented a rollback mechanism within the standard ACO framework to reduce the loss of ants, thereby improving target reachability and mitigating the disruptive effects of invalid pheromones on ACO evolution. [PERSON] et al. [35] introduced a penalty-based strategy that enhances the ants' ability to explore uncharted regions by accelerating pheromone evaporation on suboptimal paths. To address the challenge of deadlocks and improve global search efficiency, [PERSON] et al. [36] developed an adaptive polymorphic ant colony algorithm. Additionally, [PERSON] et al. [37] introduced an improved ant colony system (EACSPGO), which can effectively speed up convergence rate and produce a better solution, as well as further optimize the initial path through the path geometry optimization method. Based on the aforementioned studies, the performance of ACO has been enhanced using various optimization measures. However, certain limitations persist. One notable issue is the significant number of lost ants--particularly in complex environments--which has not been adequately addressed. Additionally, while the optimization path has seen improvements, there remains considerable potential for further optimization; however, only a few researchers have explored this aspect. Furthermore, the total turning angle of the shortest route requires further investigation to enhance overall efficiency. This paper proposes an improved ant colony optimization algorithm, called the Enhanced Ant Colony Algorithm based on Islands (EACI). Compared with other variants of ACO, the main contributions of this work are summarized as follows:  
1. To achieve efficient pathfinding, the algorithm introduces an island-based division strategy between the starting and ending points. Specifically, grid cells in the original map that are likely to cause deadlocks are first converted into obstacle grids to form an auxiliary map. Then, through using the search circle between the start and end points on the auxiliary map, several intermediate islands are identified and mapped back to the original grid. Ants travel from the starting point, sequentially passing through these intermediate islands, and then finally reach the destination. 2. To improve the efficiency of path planning between adjacent islands, the algorithm applies a non-uniform pheromone distribution during the initialization stage. Different concentrations of pheromones are preset between islands, which effectively accelerates the convergence speed. 3. To further enhance the global optimization ability of the algorithm, an adaptive pheromone evaporation coefficient is introduced to dynamically update pheromone levels. In addition, extra pheromones are added to the optimal path as a reward, while pheromones on the worst path are reduced as a penalty, thereby improving the search direction and stability. The rest of this paper is structured as follows. Section 2 focuses on environment modeling. Section 3 covers the ACO algorithm. Section 4 introduces the EACI algorithm. Section 5 presents the process of the EACI algorithm in path planning. Section 6 details the simulation experiments and analyzes the results. Section 7 validates the approach through real vehicle experiments. Finally, Section 8 summarizes this study with conclusions.