In this article, 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.

heuristic, ant system, ant colony optimization, combinatorial optimization, stochastic gradient descent, reinforcement learning
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© 2002 Massachusetts Institute of Technology
2002
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