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With the increase of space debris, space debris removal has gradually become a major issue to address by worldwide space agencies. Multiple debris removal missions, in which multiple debris objects are removed in a single mission, are an economical approach to purify the space environment. Such missions can be considered typical time-dependent traveling salesman problems (TDTSPs). In this study, an intelligent global optimization algorithm called Timeline Club Optimization (TCO) is proposed to solve multiple debris removal missions of the TDTSP model. TCO adopts the traditional ant colony optimization (ACO) framework and replaces the pheromone matrix of the ACO with a new structure called the Timeline Club. The Timeline Club records which debris object to be removed next at a certain moment from elitist solutions and decides the probability criterion to generate debris sequences in new solutions. Two hypothetical scenarios, the Iridium-33 mission and the GTOC9 mission, are considered in this study. Simulation results show that TCO offers better performance than those of beam search, ant colony optimization, and the genetic algorithm in multiple debris removal missions of the TDTSP model.
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