Cooperative Mission Planning of Multiple Spacecrafts Using a Multiobjective Optimization Algorithm Based on Reinforcement Learning

Mission planning of spacecrafts is critical to their successful execution. This article will focus on cooperative mission planning of multiple spacecrafts problem (CMP-MSP). To tackle this challenge, an efficient multiobjective snake optimizer (MOSO) based on reinforcement learning (MOSORL) is developed. The algorithm integrates an adaptive MOSO with a population classification and bidirectional search mechanism to improve the performance. In addition, a repair strategy is introduced to prevent the generation of suboptimal individuals. To improve the efficiency and accuracy of solutions, reinforcement learning (RL) is introduced to update the iterative strategy of MOSO. A dynamic learning rate approach is implemented to enhance the algorithm's learning speed. The effectiveness of the proposed algorithm is validated through numerical simulation experiments on 16 sets of instances with varying mission scales. The experimental results confirm that MOSORL outperforms competing methods significantly in solving CMP-MSP.