基于蚁群的天基预警多目标优化算法

In this paper, a continuous search space multi-objective ant colony optimization algorithm based on the ant colony optimization algorithm and R₂ sorting algorithm is proposed to solve the scheduling problem of space-based early-warning sensors. In the algorithm, the task constraints of dynamic changes in the number of sensor coverage, the resource constraints of the number of satellites and sensors, and environmental constraints, such as earth occlusion, edge observation, and observation distance, are considered. To solve the poor multi-objective trade-off ability and low computational efficiency of continuous search spaces existing in the traditional ant colony algorithm in space-based early warning task planning, a sensor scheduling scheme is sorted, and multi-objective optimization is calculated. Optimization objectives, such as target switching times, sensor fatigue, and target observation time, are weighed. Then, the algorithm is compared with the metaheuristic algorithm and dynamic ant colony algorithm in three states: sufficient observation resources, shortage of observation resources, and a serious shortage of observation resources. The results show that the algorithm can optimize the sensor switching times, single satellite observation time, and total observation time under the task, resource, and environmental constraints and is suitable for the optimization of the tracking scheme of space-based early warning constellation systems for ballistic missile and other moving targets with infrared characteristics.