Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents new approach design multiple multi-objective genetic algorithm (MOGA). is proposed perform multi-missions that should satisfy global and regional disaster monitoring missions. A computational class of MOGA named non-dominated sorting II implemented optimize the system. Pareto optimal solutions are found minimize number satellites average revisit time (ART) both coverage while maximizing percentage coverage. As result, study validates feasibility implementing with an acceptable level performance in terms ART Moreover, demonstrates CubeSat’s ability multi-missions.