Coevolutionary genetic algorithms for Ad hoc injection networks design optimization

When considering realistic mobility patterns, nodes in mobile ad hoc networks move in such a way that the networks most often get divided in a set of disjoint partitions. This presence of partitions is an obstacle to communication within these networks. Ad hoc networks are generally based on technologies allowing nodes in a geographical neighborhood to communicate for free, in a P2P manner. These technologies include IEEE802.11 (Wi-Fi), Bluetooth, etc. In most cases a communication infrastructure is available. It can be a set of access point as well as GMS/UMTS network. The use of such an infrastructure is billed, but it permits distant nodes to get in communication, through what we call "bypass links". The objective of our work is to improve the network connectivity by defining a set of long distance connections. To do this we consider the number of bypass links, as well as the two properties that build on the "small-world" graph theory: the clustering coefficient, and the characteristic path length. A fitness function, used for genetic optimization, is processed out of these three metrics. In this paper we investigate the use of two coevolutionary genetic algorithms (LCGA and CCGA) and compare their performance to a generational and a steady- state genetic algorithm (genGA and ssGA) for optimizing one instance of this topology control problem and present evidence of their capacity to solve it.

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