A stochastic optimization algorithm using intelligent agents – A program with constraints and rate of convergence

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Authors
  1. Nguyen, B.U.
Corporate Authors
Defence R&D Canada - Centre for Operational Research and Analysis, Ottawa ON (CAN)
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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Keywords
Intelligent agents;optimization;Markov chain;rate of convergence;networks;time latencies;learning;matrices;induction
Report Number
DRDC-CORA-TM-2010-249 — Technical Memorandum
Date of publication
01 Nov 2010
Number of Pages
36
DSTKIM No
CA034648
CANDIS No
534235
Format(s):
Electronic Document(PDF)

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