Task Allocation Method for Emergency Active Debris Removal Based on the Fast Elitist Non-Dominated Sorting Genetic Algorithm

Active space debris removal is now integral to modern space exploration. In order to address the problem of a heterogeneous satellite swarm with different payloads carrying out the emergency active removal of space debris, this paper proposes a Multi-type Chromosome Fast Elitist Non-Dominated Sorting Genetic Algorithm (MC-NSGA-II). The algorithm is designed to enable the satellite swarm to execute multiple coupled tasks in succession with improved optimization efficiency. An arbitrary execution order may result in deadlock, where one or more satellites become trapped in an infinite waiting loop. In order to address the heterogeneous problem of satellites and task coupling constraints, a multi-type chromosome coding strategy is developed. To evaluate different allocation strategies, three optimization objectives—time consumption, fuel consumption, and task balance—are introduced. To align with the multi-type chromosome coding strategy, two distinct sorting methods are developed for crossover and mutation operations, ensuring that all offspring individuals meet the constraints. Additionally, the algorithm incorporates a dynamic parameter-setting strategy to enhance solution efficiency. Finally, comparative simulations validate the effectiveness and superiority of the proposed method. The results show that the high-quality solution search ability of the MC-NSGA-II algorithm is 23.07% higher than that of the standard NSGA-II algorithm.