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. A particle swarm optimization algorithm is proposed to address the problem with an adaptive
mutation operator selection, where the swarm is divided into groups with different probabilities to employ various
mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted
adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown
the feasibility and effectiveness of the method.
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