The Task Scheduling of IMA based on The Multi-stage Q-learning Differential Evolution

With the increasing complexity of the integrated modular avionics IMA and the growing demand for efficient operation in multi-task environments, IMA systems must not only utilize various resources efficiently but also consider communica-tion latency, system safety and real-time responsiveness during task execution. Because of the number and complexity of tasks increasing, the system faces dual challenges: real-time task scheduling and resource utilization optimization. There-fore, we propose a task scheduling method based on a multi-stage Q-learning dif-ferential evolution algorithm. First, a bilevel scheduling model for IMA systems is constructed, which comprehensively considering key factors such as resource utilization, communication latency and safety. Second, an enhanced differential evolution algorithm is employed to optimize the model. Specifically, in the popu-lation initialization stage, the proposed algorithm uses a chaotic logistic map to ensure a uniform distribution of the initial population in the solution space. Dur-ing the population evolution process, the fitness of each infeasible individual is corrected through the penalty function. Meanwhile, the proposed algorithm utiliz-es a Q-learning mechanism to dynamically adjust the evolutionary operators to improve their adaptability and employs a multi-stage constraint addition strategy to expand the search space of the algorithm. Finally, experimental results compar-ing the proposed algorithm with other algorithms demonstrate its superior per-formance, which indicates its effectiveness in solving task scheduling problems in integrated avionics systems.