Ant Colony Optimization (ACO) is a population-based metaheuristic algorithm designed to find approximate solutions to complex optimization problems. In ACO, artificial ants search for optimal solutions by incrementally building solutions on a weighted graph, where the pheromone model biases the solution construction process. The ants move along the graph, updating pheromone trails based on the quality of their solutions. The algorithm consists of an initialization step and three main components: constructing ant solutions, performing optional local search, and updating pheromone trails. The pheromone update rule aims to enhance good solutions and forget bad ones through evaporation and addition of pheromone based on solution quality. Key ACO algorithms include the Ant System (AS), Ant Colony System (ACS), and MAX-MIN Ant System (MMAS), each with variations in pheromone update mechanisms and decision rules. ACO has been applied to NP-hard combinatorial optimization problems and other domains like telecommunication network routing. Current research focuses on theoretical foundations, dynamic and multi-objective problems, and parallel implementations. The inspiration for ACO comes from the foraging behavior of ants, particularly the double-bridge experiment, which demonstrated how ants collectively optimize paths using pheromone trails.Ant Colony Optimization (ACO) is a population-based metaheuristic algorithm designed to find approximate solutions to complex optimization problems. In ACO, artificial ants search for optimal solutions by incrementally building solutions on a weighted graph, where the pheromone model biases the solution construction process. The ants move along the graph, updating pheromone trails based on the quality of their solutions. The algorithm consists of an initialization step and three main components: constructing ant solutions, performing optional local search, and updating pheromone trails. The pheromone update rule aims to enhance good solutions and forget bad ones through evaporation and addition of pheromone based on solution quality. Key ACO algorithms include the Ant System (AS), Ant Colony System (ACS), and MAX-MIN Ant System (MMAS), each with variations in pheromone update mechanisms and decision rules. ACO has been applied to NP-hard combinatorial optimization problems and other domains like telecommunication network routing. Current research focuses on theoretical foundations, dynamic and multi-objective problems, and parallel implementations. The inspiration for ACO comes from the foraging behavior of ants, particularly the double-bridge experiment, which demonstrated how ants collectively optimize paths using pheromone trails.