Nature inspired solutions to the distributed detection system optimization problem

In this study two nature inspired optimization methods, namely particle swarm optimization and differential evolution, are proposed for the distributed detection problem. Performances of the methods are investigated through simulations where the detection capability of a parallel distributed topology consisting of N local sensors and a central processor is optimized. Simulation results have revealed that the performance of the differential evolution, in terms of convergence, is considerably better than that of the particle swarm optimization.