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This paper addresses the path selection problem from a known sender to thereceiver. The proposed work shows path selection using genetic algorithm(GA)andsimulated annealing (SA) approaches. In genetic algorithm approach, the multipoint crossover and mutation helps in determining the optimal path and alsoalternate path if required. The input to both the algorithms is a learnt modulewhich is a part of the cognitive router that takes care of four QoSparameters.The aim of the approach is to maximize the bandwidth along theforward channels and minimize the route length. The population size isconsidered as the N nodes participating in the network scenario, which will belimited to a known size of topology. The simulated results show that, by usinggenetic algorithm approach, the probability of shortest path convergence ishigher as the number of iteration goes up whereas in simulated annealing thenumber of iterations had no influence to attain better results as it acts onrandom principle of selection.

Comparison of Genetic Algorithm and Simulated Annealing Technique for Optimal Path Selection In Network Routing