Evolutionary Optimization for Active Debris Removal Mission Planning

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region space. Such problem is known to be computationally demanding and present paper provides technique preliminary based on novel evolutionary optimization algorithm, which identifies best sequence captured and/or deorbited. An original archipelago structure adopted improving algorithm capabilities explore search Several crossover mutation operators migration schemes are also tested order identify set parameters considered class problems. The numerically fictitious cloud neighbourhood Sun-synchronous orbit, including cases with multiple chasers.