Utilization of compact genetic algorithm for optimal shortest path selection to improve the throughput in mobile Ad-Hoc networks

In MANET, throughput of the network depends on the route selected between the source node and the destination node with the minimum distance. In the existing research work TSS algorithm is proposed for secured energy efficient data transmission in WSN. But it is not efficient in delay. In order to increase the efficiency in delay, compact genetic algorithm based shortest path selection is applied. The internal operations of the compact genetic algorithm improve the shortest path computation and selection. This is a probabilistic model called compact GA that receives learning models has input and provides new solution. The simulation results are compared with the existing approaches like TSS, dijkstra’s and optimal cost weight algorithm to evaluate the performance of the proposed approach in terms of objective function values, population size, path cost, computation time, throughput,and packet delivery ratio.