Task Allocation for Space Debris Removal based on Improved Particle Swarm Optimization Algorithm

Space debris removal task allocation refers to the assignment of different tasks to different spacecraft, in order to achieve optimal utilization and combination of the spacecraft. To address the issue of traditional optimization algorithms easily falling into local optima in solving task allocation problems, this paper proposes a space debris removal task allocation method based on an improved particle swarm optimization (PSO) algorithm. In this method, the adaptability of inertia weight, learning factor, and social factor are used to reduce the randomness of particle position updates, thereby further improving the algorithm’s solving ability. Additionally, in order to deal with task allocation problems under multiple constraints, a penalty factor is introduced into the objective function to strengthen the punishment for the constraint conditions. Validation through simulation, the proposed method in solving the constrained space debris removal task allocation problem is 22.8% faster than the traditional particle swarm optimization algorithm, with a cost value reduction of 13.36%, and 40.07% faster and 5.64% lower cost value than the genetic algorithm. This method can effectively improve the efficiency and accuracy of task allocation and has excellent stability. Therefore, this method has certain application prospects and promotion value in the task allocation problems in the field of aerospace.