基于分层控制免疫遗传算法的多卫星联合任务规划方法

 研究面向复杂任务需求的多卫星联合下的任务规划技术，对于提高对地观测系统的保障能力具有重要意义。各种类型的复杂任务需求由于需求特点的差异，使得相对于针对点观测任务的规划更加复杂。在提炼问题特点的基础上，建立了整数规划问题模型，并提出了分层控制免疫遗传算法。该算法采用双层操作，在父层采用遗传操作算子，在子层采用免疫操作算子，并引入基因重组、自适应免疫更新、小生境等机制改进搜索过程。仿真实验表明，分层控制免疫算法在解决复杂任务的任务规划中是可行有效的。

Multiple Satellite Scheduling Based on Hierarchical and Immune Genetic Algorithm

The study of multiple satellite scheduling that satisfies complicated satellite observation requirements is of great importance in improving the supporting capability of an earth observation system. The scheduling is made more complex by the diversity of various satellite observing requirements and the differences of their characteristics as compared with scheduling of small target observation. Based on an analysis of the characteristics of the issue, the article establishes an integer programming model, and proposes a new algorithm named hierarchical immune genetic algorithm (HIGA) to solve it. The algorithm adopts a two-layer operation, with a genetic operation in the parent layer, and an immunity operation in the child layer. Moreover, it incorporates gene recombination, adaptive immunity updating and niche mechanism to improve the performance of the searching process. Experimental results show that the algorithm is both feasible and efficient in multiple satellite scheduling.