A Task Allocation Algorithm of Spacecraft Cluster Space Game

Task planning in spacecraft cluster game confrontations presents a highly complex optimization problem characterized by multi-layered constraints and objectives, including optimal fuel consumption, time cost, and dynamic environmental adaptability. To address these challenges, this paper proposes an improved discrete particle swarm optimization (DPSO) algorithm for spacecraft cluster adversarial task allocation. First, a task allocation model is established, integrating key variables such as fuel consumption, maneuver duration, game success probability, and target value, while incorporating constraints like single-task assignment, target coverage, and maximum fuel capacity. Building on this, the proposed algorithm introduces random perturbations during velocity updates to escape local optima and accelerate convergence, enhancing traditional DPSO by balancing exploration and exploitation. Simulation experiments demonstrate the algorithm's efficacy: in a scenario involving 12 spacecraft and 3 adversarial targets, the improved DPSO converges to the optimal solution within 100 iterations, outperforming the standard DPSO within 750 iterations. Compared to the standard DPSO, the approach of this paper uniquely addresses dynamic constraints through real-time adaptive adjustments and achieves a 7.5 speedup in convergence. The results validate the algorithm's ability to handle complex spatial game dynamics, including orbital maneuvering uncertainties and time-varying combat capabilities, while ensuring multi-objective optimization. This work advances spacecraft cluster autonomy in adversarial environments, offering a scalable and efficient solution for real-time task allocation.