A Hybrid Genetic Algorithm Approach for Ospf Weight Setting Problem

In this paper, a method based on hybrid genetic algorithm (GA), is presented to optimize administrative weights for OSPF routing. This method can be seen as an alternative to the localsearch method in [1] or another GA-based method in [8,10]. However, the GA as well as the objective function we use are different. Instead of minimizing a convex cost function we prefer to minimize the maximum and average utilization directly. This objective function is similar as one proposed in [6,7]. In addition we combine the population-based search feature of the GA with a search heuristic representing an individual-based search method and propose a new objective function to minimize weight changes for an existing operational network. We will demonstrate our method in a case of a small network and compare the results with MIP-based (Mixed Integer Programming) method from [6,7]. Afterwards we will show the results for a bigger network with increasing traffic demands, compared with the results of some conventional weight settings as well as with the lower bound of general optimal routing (linear program / LP solution).