Comparative Study on Meta-heuristics for Achieving Parabolic Fairness in Wireless Channel Allocation

We present the results of a comparative study on the design of meta-heuristic algorithms for achieving parabolic fairness in wireless channel allocation. Wireless channel allocation (WCA) is a basic problem of fair distribution of indivisible goods, in this case the allocation of channels to users in a wireless schedule. Parabolic fairness represents a state that coincides with maxmin fairness in fair end-to-end user traffic rate allocation. This state can also be represented by maximization of a special case of the ordered weighted averaging operator. The related task then is to find an algorithm to approximate that maximum value for the WCA as close as possible. Here, several heuristic approaches are taken into account: a simple annealing heuristic, its integration into an Iterated Local Search (ILS) and also the integration of ILS as local search of a memetic algorithm. The comparison gives that best results can be achieved by the ILS, with up to 2-3 times improved performance compared to other algorithms and for practically relevant problem dimensions.