Ant colony optimization and stochastic gradient descent

被引：62
|
作者：
Meuleau, N [1 ]
Dorigo, M [1 ]
机构：
[1] Free Univ Brussels, IRIDIA, B-1050 Brussels, Belgium
来源：
ARTIFICIAL LIFE | 2002年 / 8卷 / 02期
关键词：
heuristic; ant system; ant colony optimization; combinatorial optimization; stochastic gradient descent; reinforcement learning;
D O I：
10.1162/106454602320184202
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：103 / 121
页数：19
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