Genetic algorithm to solve optimum TDMA transmission schedule in broadcast packet radio networks

The problem of finding an optimum conflict-free transmission schedule for a broadcast packet radio network (PRNET) is NP-complete. In addition to a host of heuristic algorithms, recently, neural network and simulated annealing approaches to solve this problem were reported. We show that the standard genetic algorithm, though able to solve small problems, performed poorly for large networks. This is because classical crossover and mutation operations create invalid members, which flood the whole population, hindering the progress of the search for valid solutions. In this paper, special crossover and mutation operations are defined, such that the members of the population always remain valid solutions of the problem. Excellent results were obtained in a few generations, even for very large networks with 400 nodes. The results were compared with recently reported neural network and mean field annealing approaches.

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