**5. Future of ACO**

From this overview, it should be evident that the ACO metaheuristic is a robust foundation for tackling complicated combinatorial issues. Traveling Salesman Problem (TSP), Dynamic Traveling Salesman Problem (DTSP), Quadratic Assignment Problem (QAP), and other optimization challenges are particularly well suited to Ant Systems (AS). Compared to different powerful approaches, their superiority is undeniable, and the time savings (produced tours) paired with a high level of optimality cannot be overlooked in this context.

Ant System has a competitive advantage over highly communicative multiagent systems thanks to reinforcement learning and greedy searching concepts. Simultaneously, the capacity to self-train fast enables light and agile installations. Finally, by separating individual swarm agents from one another, AS can process massive data volumes with far less waste than competing algorithms.

ACO is a new area that combines straightforward functions with profound conceptualizations. There's not much doubt that further research will yield exciting results that could provide answers to currently unsolvable combinatorial problems in the future. Alternatively, AS and ACO have been shown to be effective in various TSP permutations.

New research can provide better solutions by increasing effectiveness while decreasing restrictions by studying the ACO and PSO to make future improvements. More options for dynamically determining the optimal destination can be developed through ACO. For example, a plan to equip PSO with fitness sharing technology is being tested to see if it can help improve performance. In the future, rather than relying solely on the current iteration, each individual's velocity will be updated by combining the best elements from all previous iterations.

*The Application of Ant Colony Optimization*
