The protection of defended assets such as military bases and population centers against hostile targets (e.g., aircrafts, missiles, and rockets) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We review algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The results show that many of the tested algorithms produce high quality solutions for small-scale problems. A novel variant of particle swarm optimization seeded with an enhanced greedy algorithm is described and is shown to perform best for large instances of the real-time allocation problem.