## 1 Introduction
### The ACO Paradigm and Applications  
Ant Colony Optimization (ACO) derives its inspiration from the foraging behavior observed in natural ant colonies, where decentralized communication through chemical signals known as pheromones plays a pivotal role in finding the shortest path between their nest and a source of food. Initially proposed in [17], ACO has demonstrated remarkable efficacy in addressing a multitude of complex combinatorial optimization problems, including scheduling and, particularly, routing problems [18; 19; 20; 21; 22; 23]. More generally speaking, agent-based or swarm intelligence heuristics have been applied as well to coloring challenges [24; 25; 26], robot path planning for inaccessible areas [27; 28], transportation complexities and traffic problems [29; 30], and feature selection across diverse fields [31]. Beyond optimization, ACO demonstrates utility in modeling crowd dynamics. In [32], the authors translated ACO rules into conceptual frameworks rooted in social dynamics. They proposed an agent-based model to study collective crowd behaviors. Furthermore, ref. [33] provides a sensitivity analysis.  
The notion of dividing ant colonies into groups has received particular attention because of its potential to enhance optimization performance. In [34], an adaptive ACO was implemented, where the colony was separated into three groups: ordinary ants, using heuristic information; abnormal ants, seeking low pheromone paths; and random ants, exploring randomly. This collaborative approach improved the optimization performance, and was able, for instance, to effectively address the traveling salesman problem.  
Extending this exploration into multi-objective optimization problems, ref. [35] emphasized the performance benefits of employing multiple ant groups. In their study, a grouping approach was applied to enhance the efficacy of the ACO paradigm when dealing with multi-objective optimization problems.  
In a related effort, ref. [36] proposed a multi-objective ACO algorithm for community detection in complex networks. By partitioning the ant colony into groups and coordinating their exploration, the algorithm achieved a more efficient search ability for solutions. Further corroborating these findings, Ref. [37] introduced a group-based ACO algorithm for bi-objective optimization, where ants were organized into groups based on parameters related to the objective function, resulting in an enhanced overall algorithm performance. Recent advancements include the Grouping Ant Colony Optimization (GACO) algorithm presented by [38]. GACO specifically addresses the multi-UAV reconnaissance task allocation problem by dividing the ant colony into distinct subgroups. This division proves crucial as different UAVs focus on distinct targets, ultimately leading to superior optimality in the results when compared to existing methods reported in the literature. Lastly, we stress the possibility of future applications of the GACO algorithm to networks modeling the mobility of groups of people, providing, for instance, personalized recommendations of places of interest [39] and possibly analyzing the impact of restrictions to mobility due to extreme circumstances [40].  
Building upon the advancements in multi-objective optimization techniques, some recent studies have introduced innovative algorithms to address specific problems in different domains. For instance, in [30] the authors propose a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm for optimizing signalized traffic problems. This approach incorporates a dynamic leader selection mechanism, guiding particles toward elite solutions through angle comparisons. Similarly, in [41] a single deep reinforcement learning model is employed to tackle the multi-objective traveling salesman problem (MOTSP). The method makes use of an encoder-decoder framework with a routing encoder, efficiently extracting and aggregating information to generate approximate Pareto-optimal solutions in parallel.