Resource planning and scheduling of payload for satellite with genetic particles swarm optimization

The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite´s functions and the payload´s resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces, where then the planning and scheduling is modeled as a combinatorial optimization. The genetic particle swarm optimization algorithm (GPSO) is proposed to address the problem, which was derived from the original continuous particle swarm optimization (PSO) and incorporated with the genetic reproduction mechanisms, namely crossover and mutation. The simulation results have shown that GPSO significantly improved the search efficacy of PSO for the combinatorial optimizations.