Ant Colony Optimization in Machine Learning
Nature has long been a source of inspiration for problem-solving algorithms in computer science. One such biologically inspired algorithm is Ant Colony Optimization (ACO) , a technique based on the behavior of real ant colonies. ACO has become a popular metaheuristic in machine learning and artificial intelligence due to its ability to solve complex combinatorial optimization problems efficiently. 1. What is Ant Colony Optimization (ACO)? Ant Colony Optimization is a probabilistic technique developed by Marco Dorigo in the early 1990s. It is inspired by the foraging behavior of ants , particularly how they find the shortest paths between food sources and their nest using pheromone trails . Key Principles: Ants explore their environment randomly. When ants find food, they return to the colony, laying down pheromones on their path. Other ants are more likely to follow paths with stronger pheromone concentrations. Over time, shorter paths accumulate more pheromones because they a...