title: Ant colony optimization and stochastic gradient descent
abstract: We study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques
keyphrases: ant colony optimization stochastic gradient descent empirical ACO algorithms pheromones combinatorial optimization heuristic reinforcement learning social insects swarm intelligence artificial life local search algorithms