Optimization of flight test tasks allocation and sequencing using genetic algorithm

Flight test tasks arrangement is one of the most significant problems in the development of new civil aircraft. Normally, there are many factors restraining flight test tasks arrangement, including characteristics of experimental aircraft, requirements of tasks themselves, and logical relationships among them, leading to increased development period and costs. Hence, flight test tasks arrangement is generally viewed as a multi-constraint nonlinear optimization problem. To improve flight test efficiency, a multi-level optimization model of flight test tasks allocation and sequencing is introduced in this paper, where flight test period is the main optimization objective, and a penalty function evaluating tasks testing dates is the minor optimization objective. A flight test tasks sequence oriented improved genetic algorithm (FTTSOIGA) is proposed to solve the model. Firstly, a tasks allocation algorithm is designed to establish the mapping between tasks sequence and tasks arrangement result, which is independent of feasible sequence. Then, the arrangement result is optimized by optimizing the tasks sequence using the genetic algorithm. Furthermore, a tasks sequence adjustment strategy is applied to accelerate algorithm convergence. Simulation cases of 3 experimental aircraft and 80 flight test tasks demonstrate the efficiency of FTTSOIGA. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Flight test tasks arrangement is a significant issue in the development of new civil aircraft, constrained by various factors leading to increased development period and costs. A multi-level optimization model and an improved genetic algorithm have been introduced to enhance the efficiency of flight test tasks.