On Routing IP Traffic Using Single- and Multi-objective Genetic Optimization

Abstract — We formulate the routing of traffic in emerging IP networks as both single and multi-objective optimization problems which are solved using classical and hybrid genetic optimization. We adopt the occurrence of links in the computed paths as a cost function used to achieve load balancing by reducing the interference among competing flows. We adapt the the Non-Sorting Genetic Algorithm(NSGA) to find a Pareto front for the multi-objective problem. This Pareto front expresses a set of solutions which represent plausible set of paths that minimize both the interference among competing flows and the queuing delay in the network. Using both multiplicative and additive composition rules, we compare the quality of the paths achieved by the different evolutionary optimization strategies. I