A Two-Stage Co-Evolutionary Algorithm Enhanced by Reinforcement Learning for Efficient Aircraft Test Task Allocation

Efficient long-term flight test task allocation is crucial for aircraft development, yet it poses significant challenges due to the complex, extended timelines and numerous interdependent tasks. Current methods often rely on expert judgment, leading to inefficiencies, delays, and suboptimal planning. This paper addresses these challenges by introducing a reinforcement learning-enhanced two-stage evolutionary algorithm. The algorithm incorporates a mixed-integer nonlinear programming model aimed at minimizing the maximum flight test duration while adhering to multiple constraints. Key innovations include a two-stage evolutionary framework with optimized encoding schemes and crossover operators, and dynamic adjustment of crossover and mutation probabilities using Q-learning. These features enable the algorithm to handle the complexity and unpredictability of long-term flight test planning. Extensive experiments validate the approach, demonstrating its ability to generate high-quality, adaptive flight test plans in real-world, complex scenarios.