Solving the minimum-cost constrained multipath routing with load balancing in MPLS networks using an evolutionary method

This paper presents a flexible evolutionary method for minimum-cost multipath constrained routing with load balancing problem in MPLS networks. The proposed solution approach combines genetic algorithms with linear multi-commodity flow to enhance the efficiency of the solution attained. The goal is to determine the distribution of traffic demands over a given capacitated network topology to minimize the routing cost while balancing loads on various links. The constraints that should be satisfied are the maximum hop count, the total number of virtual paths and the link capacities. This problem is a highly constrained multiobjective optimization for which exact optimization methods become helpless to deal with such complexity. Using a case study from the literature, the proposed approach is evaluated and compared with the standard genetic algorithm. We also show how the proposed approach can be used to determine approximate Pareto points and compare them with the exact Pareto front.