A hybrid grey wolf optimizer using opposition-based learning, sine cosine algorithm and reinforcement learning for reliable scheduling and resource allocation

As the number of space debris in geosynchronous Earth orbits continues to grow, the threat posed by space debris to satellites surveillance is increasing, and the available orbital resources are also decreasing. Thus, reasonably scheduling and allocating the resources for space object tracking has become vital. This paper establishes an optimization model for the resource allocation and scheduling problem for space debris tracking. A fusion algorithm that combines the grey wolf optimizer, opposition-based learning, sine cosine search strategy, and reinforcement learning was proposed and used to solve the problem. Six groups of realistic data were selected based on the relevant background information of space debris tracking to test the validity and effectiveness of the proposed algorithm. The performance of the state-of-the-art optimization algorithms was compared with that of the proposed algorithms. The result of the experiment indicates that the proposed algorithm effectively solves the resource allocation and scheduling problem for space debris tracking.