Markov Chain Models for Genetic Algorithm Based Topology Control in MANETs

Abstract. We analyze the convergence properties of our force based genetic algorithm(fga) as a decentralized topology control mechanism distributed among software agents. fga guides autonomous mobile agents over an unknown geographical area to obtain a uniform node distribution. The stochastic behavior of fga makes it difficult to analyze the effects of various manet characteristics over its convergence rate. We present ergodic homogeneous Markov chains to analyze the convergence of our fga with respect to changing communication range of mobile nodes. Simulation experiments indicate that the increased communication range for the mobile nodes does not result in a faster convergence. Key words: Bio-inspired Algorithms, Genetic Algorithms, MANET, Markov Chains

• Type: Conference
• Year: 2010
• Where: EVOW
• Authors: Cem Safak Sahin, Stephen Gundry, Elkin Urrea, M. Ümit Uyar, Michael Conner, Giorgio Bertoli, Christian Pizzo
• Keywords: Artificial Intelligence, Convergence, EVOW 2010, Markov Chains, Mobile Nodes