Genetic Algorithm for Network Cost Minimization Using Threshold Based Discounting

摘要

We presetn 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 increntives to combine flow from different sources. Namely, there is a prescribed threshold on every link, and if the total flow on a link is greater than tht threshold, the cost of this flow is discounted by a factor α. A heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. The resultls are compared with former branch and bound results using the CPLEX solver. For larger data instances we were able to obtain improved solutions using less CPU time, confirming the effectiveness of our heuristic approach.