A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

Abstract

This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.

Keywords

• earth observation satellite
• task clustering
• mission planning
• ga-sa hybrid algorithm