Abstract: Addressing the problem of aircraft mission instruction sequence generation and optimization, we propose a constraint-hierarchical mission instruction sequence processing framework and further design a sequence orchestration method that integrates topological optimization and priority-encoded genetic algorithm. First, instructions and their constraints are modeled as a directed graph, and virtual nodes are introduced to replace strongly connected components (SCCs) in the graph, achieving cycle elimination. Then, for the generated directed acyclic graph (DAG), a basic initial frame-work of the instruction sequence is constructed through topological optimization. For the extracted strongly connected components, the priorities of their nodes are encoded and used as the gene indexes of crossover objects, thereby iter-atively generating optimized instruction sequence snippets. Finally, the snippets are integrated into the initial framework to achieve the generation and optimization of the mission instruction sequence. Simulation results display that, in sce-narios with instruction sets of varying scales and complexities, the proposed method significantly reduces the genera-tion time and compresses the length of the instruction sequences compared to other encoding methods.

Key words: Sequence generation, Instruction arrangement, Genetic algorithm, Mission planning, Instruction release