A Multi-Stage Optimization Approach for Satellite Orbit Pursuit–Evasion Games Based on a Coevolutionary Mechanism

For the satellite orbit pursuit–evasion game problem, this paper proposes a multi-stage optimization-based solution aimed at improving the confrontation strategies between task satellites and target satellites in complex space environments. The approach divides the satellite pursuit–evasion game into two phases: the “approach phase” and the “sustained phase”. It dynamically optimizes the trajectories and strategies of the task and target satellites to achieve adaptive orbit control and behavior optimization. To enhance the global search capability and local convergence of the algorithm, this paper employs the Zebra Optimization Algorithm, introducing a multi-population cooperative evolution mechanism, and integrates differential game theory to improve the stability and reliability of the game strategies. Simulation results demonstrate that the proposed method effectively enhances task efficiency under multiple constraints, dynamically adjusts the strategies of both the pursuer and the evader, and provides an efficient, scalable solution applicable to satellite pursuit–evasion games in complex space environments.