Convergence analysis of genetic algorithms for topology control in MANETs

Authors:
Cem Şafak Şahin;Stephen Gundry;Elkin Urrea;M. Ümit Uyar;Michael Conner;Giorgio Bertoli;Christian Pizzo
Affiliations:
Department of Elec. Eng., Graduate Center of The City University of New York, NY;Department of Elec. Eng., Graduate Center of The City University of New York, NY;Department of Elec. Eng., Graduate Center of The City University of New York, NY;Department of Elec. Eng., Graduate Center of The City University of New York, NY;Department of Elec. Eng., Graduate Center of The City University of New York, NY;US Army Communications-Electronics RD&E Center, Fort Monmouth, NJ;US Army Communications-Electronics RD&E Center, Fort Monmouth, NJ
Venue:
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Year:
2010

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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.