A Greedy Genetic Algorithm for the TDMA Broadcast Scheduling Problem

The broadcast scheduling problem (BSP) in wireless adhoc networks is a well-known NP-complete combinatorial optimization problem. The BSP aims at finding a transmission schedule whose time slots are collision free in a wireless ad-hoc network with time-division multiple access (TDMA). The transmission schedule is optimized for minimizing the frame length of the node transmissions and maximizing the utilization of the shared channel. Recently, many metaheuristics can optimally solve smaller problem instances of the BSP. However, for complex problem instances, the computation of metaheuristics can be quite time and memory consuming. In this work, we propose a greedy genetic algorithm for solving the BSP with a large number of nodes. We present three heuristic genetic operators, including a greedy crossover and two greedy mutation operators, to optimize both objectives of the BSP. These heuristic genetic operators can generate good solutions. Our experiments use both benchmark data sets and randomly generated problem instances. The experimental results show that our genetic algorithm is effective in solving the BSP problem instances of large-scale networks with 2,500 nodes.