Genetic Algorithm for Network Cost Minimization Using Threshold Based Discounting

We present a genetic algorithm for heuristically solving a cost minimization problem applied to communication networks with threshold based discounting. The network model assumes that every two nodes can communicate and offers incentives to combine of from different sources. Namely, there is a prescribed threshold on every link, and if the total of on a link is greater than the threshold, the cost of this of is discounted by a factor. A heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. The results are compared with former branch and bound results using the CPLEX(r)solver. For larger data instances we were able to obtain improved solutions using less CPU time, confirming the effectiveness of our heuristic approach.