Abstract
We present different approaches for knowledge sharing bio-inspired mobile agents to obtain a uniform distribution of the nodes over a geographical terrain. In this application, the knowledge sharing agents in a mobile ad hoc network adjust their speed and directions based on genetic algorithms (GAs). With an analytical model, we show that the best fitness value is obtained when the number of neighbors for a mobile agent is equal to the mean node degree. The genetic information that each mobile agent exchanges with other neighboring agents within its communication range includes the node’s location, speed, and movement direction. We have implemented a simulation software to study the effectiveness of different GA-based algorithms for network performance metrics including node densities, speed, and number of generations that a GA runs. Compared to random-walk and Hill Climbing approaches, all GA-based cases show encouraging results by converging towards a uniform node distribution.
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