Convergence Analysis of Genetic Algorithms for Topology Control in MANETs

Abstract

We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology control mechanism distributed among software agents. FGA uses local information to guide autonomous mobile nodes over an unknown geographical terrain to obtain a uniform node distribution. Analyzing the convergence characteristics of FGA is difficult due to the stochastic nature of GA-based algorithms. Ergodic homogeneous Markov chains are used to describe the convergence characteristics of our FGA. In addition, simulation experiments verify the convergence of our GA-based algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA522658

Entities

People

  • Cem S. Sahin
  • Christian Pizzo
  • Elkin Urrea
  • Giorgio Bertoli
  • M. U. Uyar
  • Michael Conner
  • Stephen Gundry

Organizations

  • City College of New York

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Application Software
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Machine Learning
  • Markov Chains
  • Mesh Networks
  • Mobile Ad Hoc Networks
  • Monte Carlo Method
  • Probability
  • Self Organizing Systems
  • Simulations
  • Software Agents
  • Stochastic Processes
  • Topology

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Networking
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Biotechnology