通过分析区域目标多星成像任务规划的约束条件，建立相应的约束满足模型，并分析模型的数学复杂度。为改善遗传算法应用于多星成像任务规划问题时，全局搜索能力较弱的缺点，首次提出使用基因表达式编程求解此问题。在算法实现的过程中，设计出倒置遗传算子增强最优解的搜索，并引入知识库保留迭代过程中的精英个体。结果表明，基因表达式编程不仅有效和合理地解决了多星成像规划问题，而且极大地提高了解的精度。
The constraint-satisfaction model is established by analyzing the constraints of multi-satellite imaging mission planning for regional targets, and the mathematical complexity of the model is analyzed. In order to improve the weak global searching ability of the genetic algorithm in multi-satellite imaging mission planning, the gene expression programming (GEP) is first proposed in this work to solve the problem. In the process of algorithm implementation, the inverted genetic operator is designed to enhance the search ability for the optimal solution, and the repository is introduced to preserve elite individuals in the iteration process. The results show that the gene expression programming (GEP) is effective and reasonable in solving multi-satellite imaging planning problems and greatly improves the accuracy of the solution.
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