Literature DB >> 12171633

Ant colony optimization and stochastic gradient descent.

Nicolas Meuleau1, Marco Dorigo.   

Abstract

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.

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Year:  2002        PMID: 12171633     DOI: 10.1162/106454602320184202

Source DB:  PubMed          Journal:  Artif Life        ISSN: 1064-5462            Impact factor:   0.667


  2 in total

1.  Automated red blood cells extraction from holographic images using fully convolutional neural networks.

Authors:  Faliu Yi; Inkyu Moon; Bahram Javidi
Journal:  Biomed Opt Express       Date:  2017-09-12       Impact factor: 3.732

2.  Entanglement-Gradient Routing for Quantum Networks.

Authors:  Laszlo Gyongyosi; Sandor Imre
Journal:  Sci Rep       Date:  2017-10-27       Impact factor: 4.379

  2 in total

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