Sensor scheduling using ant colony optimization
Dan Schrage, Paul Gonsalves · 2003
The basic problem of collection management is to schedule a group of sensor assets over a series of mission objectives in a way that minimizes resource usage and maximizes the likelihood that all the mission objectives will be completed. We present an approach to collection management, specifically sensor scheduling, that relies on Ant Colony Optimization (ACO), a biologically-inspired search algorithm. This approach offers agent-based modeling of the search resources and environment to ensure realism. We extend the traditional ACO algorithm, which relies on a single agent for search, to accommodate coordinated multi-sensor search teams made up of heterogeneous sensor assets.