This study investigates the issue of multi-objective mission planning for multi-payload satellite constellations via the nondominated sorting carnivorous plant algorithm (NSCPA).Observation time windows are generated, and a constraint satisfaction model work top saver is established based on multiple regional targets, satellite orbits, and characteristics of the synthetic aperture radar (SAR) payload and optical payload.A task conflict detection and resolution method is proposed to handle the task assignment among multiple satellites.
Based on the existing single objective-based CPAs, a modified multi-objective NSCPA is first developed for multi-objective planning optimization using the non-dominated sorting algorithm.The effectiveness and superiority of the NSCPA are verified by a series of simulation experiments and comparisons with the Power Wire traditional non-dominated sorting genetic algorithms-II (NSGA-II) and particle swarm optimization (PSO).