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