A heuristic ant colony optimization approach for allocating and packing multi-module satellite payloads

It is effective to pack multi-module satellite payloads for existing heuristic and evolutionary approaches. But for them, the multi-module payload allocation optimization is not considered before packing. In addition, an evolutionary approach based on a random initial scheme needs to spend more time on interference calculation. For this reason, a knowledge-based heuristic ant colony optimization approach is proposed for allocating and packing multi-module satellite payloads (MSAPHAA). Firstly, related knowledge is obtained from the principle of dynamics and the definition of moment of inertia. Then by combining it with heuristic and ant colony optimization, it will be realized to both the multi-module payload allocation optimization and packing optimization without interference computation， and in the process of iteration payload migration can be accomplished by analyzing the packing position of payloads. Introducing the knowledge-based swap intelligent optimization and avoiding interference computation improves the performance of the proposed approach. Numerical experiments show that all the solution accuracy, computation efficiency, and stability of the proposed MSAPHAA approach are higher than those of existing approaches.