A review of exploring recent advances in

Inspired by the foraging behavior of ants, the well-known metaheuristic Ant Colony Optimization (ACO) provides strong answers to challenging optimization issues in many spheres. This work investigates current developments in ACO algorithms with an emphasis on hybridization, employing methods including machine learning, adaptive mechanisms, and genetic algorithms to improve performance. Applications such as robotics, telecommunications, healthcare, and logistics show ACO's adaptability in handling path planning, resource allocation, and data optimization. Dynamic pheromone methods, multi-objective optimization, and domain-specific adaptations, which have raised computing efficiency, scalability, and solution quality, have been key advances. Notwithstanding these developments, problems, including parameter sensitivity and real-time adaptation, remain unresolved. Future studies include integrating real-time data, creating scalable adaptive algorithms, and tackling domain-specific restrictions to further increase ACO's relevance. This work emphasizes ACO's possible importance as a fundamental instrument for addressing problems of real-world optimization.