Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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In mobile ad-hoc networks loss of communication paths can occur because the movement of nodes may create network partitions, effectively preventing communication between nodes in disjoint sections. Some methods were previously proposed to specifically address this issue. For instance, connection stability may be preserved by constraining and adapting the movements of each individual node in order to ensure that there is always some viable communication path between any two nodes on the network. This is accomplished by establishing a set of waypoints each node should transverse before arriving to its desired position. The order by which the nodes move to these waypoints is specified by the algorithm. However, multiple alternate solutions for the problem can be found by varying the sub-set of nodes that moves in each step, and the degree of concurrency in the movements of the nodes is directly dependant on the choices made. In this work, we point out that there is an opportunity for applying an optimization method that will enable to maximize the number of nodes that move towards their destinations in each step, while ensuring connectivity is maintained. We present such optimization and show how it impacts overall system performance.