## 5 Conclusions  
This paper introduces an agent-based model based on the ACO paradigm where ants are memory-enhanced to explore how partitioning a colony into groups influences the algorithm's performance. The study specifically delves into the roles of group size and number in the context of a network analysis. Within the model, a collective of ants, starting from a specified location, collaboratively works to find the optimal path leading to the exit within a grid network. The overarching objectives of the colony include maximizing the number of ants successfully leaving and minimizing path costs. Three different kinds of analysis were conducted: an _overall analysis_, to analyze the performance of the whole colony concerning the two evaluation metrics when divided into groups of different sizes; a _group analysis_, to investigate the performance of each group into which the colony is partitioned; and a _pheromone distributions analysis_, to discern any correlation between the temporal distribution of pheromones on the edges and the number of ants navigating the network during the simulation. In the _overall analysis_, a nuanced relationship emerged between the number of groups and the algorithm's performance. At a higher evaporation rate, the optimal configuration involved 10 groups, obtaining the best success rate and path costs. It follows that under conditions of accelerated pheromone decay a moderate level of colony partitioning enhances effective collaboration. Conversely, at a lower evaporation rate, a superior performance was observed with 20 groups, emphasizing the role of prolonged pheromone lifespan in facilitating distributed collaboration and reducing overall costs. This finding underscores the significance of pheromone persistence, particularly in scenarios where environmental conditions permit longer-lasting pheromones. The _group analysis_ delved deeper into the performance of individual groups. Notably, the colony operating as a single group demonstrated consistent adaptability across different evaporation rates. However, as the number of groups increased, a complex landscape of performance rates emerged. Optimal values in success rate or path cost were typically achieved by one of the initial groups, with subsequent groups showing a decline in performance. This highlights the coordination challenges that escalate with the number of groups and their interactions, emphasizing the need for careful consideration in partitioning colonies, especially in larger-scale scenarios, to mitigate challenges associated with managing diverse groups effectively. The _pheromone distribution analysis_ provided additional insights into the correlation between pheromone dynamics, ant presence, and group behavior. As the number of ants increased, mean pheromone levels collectively rose, indicating active exploration. However, a subsequent decline, particularly noticeable at higher evaporation rates, emphasized the challenge of sustaining the pheromone quantity over time. The standard deviation analysis added depth to this understanding, revealing that a more uniform distribution, as indicated by lower values, could make it harder for ants to differentiate paths and exit efficiently. Conversely, higher standard deviation values suggested greater heterogeneity, enabling ants to effectively use pheromone signals for their exploration. The temporal dynamics of group entry and exit further highlighted the transient nature of group presence. Peaks in mean pheromone levels aligned with group entry, followed by decreases indicating exits. This temporal pattern underscores the dynamic nature of group interactions and their impact on pheromone distribution. In summary, our study reveals a correlation between the partitioning of ant colonies and the quality of solutions within an ACO framework, shedding light on the complex interplay between individual ant behaviors and collective dynamics.