A new hybrid swarm optimization algorithm for power system vulnerability analysis and sensor network deployment

In this paper, a new particle swarm optimization (PSO) inspired swarm intelligence optimization algorithm, so called hybrid multiagent swarm optimization (HMSO) algorithm, is proposed and investigated for mixed-binary nonlinear programming (MBNLP) problems. This new HMSO algorithm, including a continuous optimizer and a binary optimizer, is motivated by the recently developed results about multiagent coordination for network systems. Specifically, the new HMSO algorithm not only shares the global optimal solution among the particles like the PSO algorithm, but also shares the neighboring particle´s velocity and position information for each individual particle underlying a communication topology. In this paper, we present some applications of our new HMSO algorithm for solving recently raised power system vulnerability analysis and sensor network deployment problems. The simulation illustrations are provided and compared with an improved PSO from the literature, while the numerical results reveal the high accuracy of the new HMSO when solving MBNLP problems.