Marco Dorigo, Thomas Stützle
Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which utilizes pheromone trails to guide search procedures. This review examines the evolution and advancements of ACO since the introduction of the Ant System, the first algorithm in this domain. It highlights significant algorithmic developments that have improved performance, including variations that address diverse computational challenges. The paper discusses the establishment of a generic framework for ACO algorithms and showcases successful applications across a range of hard combinatorial problems, emphasizing the theoretical insights gained regarding ACO's properties and behaviors. Recent trends in ACO research are also explored, providing an updated perspective on ongoing developments and future directions in this rapidly evolving field. The findings suggest that ACO continues to play a vital role in solving complex optimization problems, leveraging both heuristic and adaptive elements to enhance performance. This overview serves not only to inform researchers of the current state of ACO but also aims to inspire new research avenues that build upon its foundational concepts.
@article{04442036-8049-4b27-bc1e-3e5a19cdd574,
title={An Introduction to Ant Colony Optimizati},
author={Marco Dorigo and Thomas Stützle},
year={2026},
language={en}
}TY - JOUR TI - An Introduction to Ant Colony Optimizati AU - Marco Dorigo AU - Thomas Stützle PY - 2026 LA - en ER -
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