A Stochastic Optimization Algorithm using Intelligent Agents: With Constraints and Rate of Convergence

Abstract

The problem of optimizing the average time latency of a network, using agents that are able to learn, is examined in this paper. The network design is constrained by a traffic matrix that dedicates specific flows between specific pairs of nodes. Although this is an application type of analysis, only the methodology is presented here, which includes an algorithm for optimization and a corresponding conservative rate of convergence based on no learning. The application part will be presented in the near future once data are available. It is expected that the tools developed in this paper can be used to optimize a wide range of objective functions that do not necessarily have to be the time latency. For example, it could be the cost of the network.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2010
Accession Number
ADA535435

Entities

People

  • Bao U. Nguyen

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Communication Networks
  • Engineering
  • Intelligent Agents
  • Learning
  • Markov Chains
  • Markov Processes
  • National Security
  • Networks
  • Operations Research
  • Optimization
  • Probability
  • Reinforcement Learning
  • Security
  • Undersea Warfare
  • Warfare

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Military History of the United States in the 20th Century.
  • Operations Research