Convergence analysis of genetic algorithms for topology control in MANETs

Cem Safak Sahin,M Umit Uyar,Elkin Urrea,Giorgio Bertoli,Christian Pizzo,Michael Conner,Stephen Gundry

doi:10.1109/sarnof.2010.5469783

Convergence analysis of genetic algorithms for topology control in MANETs

Cem Safak Sahin, M Umit Uyar + Show 5 more

https://doi.org/10.1109/sarnof.2010.5469783

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Publication Date: Apr 1, 2010

Citations: 21

Affiliation: City University of New York, City College, City College of New York, The Graduate Center, CUNY, United States Department of the Army

#Ergodic Homogeneous Markov Chains #GA-based Algorithm + Show 8 more

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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.

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