Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks

Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes.