Optimization of flight test tasks allocation and sequencing using genetic algorithm
Optimization of flight test tasks allocation and sequencing using genetic algorithm
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使用遗传算法优化飞行测试任务分配和排序
DOI：
10.1016/j.asoc.2021.108241
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发表时间：
2021-12
影响因子：
8.7
通讯作者：
Zeming Mao
中科院分区：
文献类型：
--
作者：
Shuangfei Xu;Wenhao Bi;An Zhang;Zeming Mao
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. • An improved genetic algorithm is used to optimize flight test tasks arrangement. • Flight efficiency is evaluated from two levels. • Fairly realistic flight test constraints are considered in the optimization model. • A tasks allocation algorithm independent of feasible sequence is proposed. • A sequence adjustment strategy is applied to accelerate algorithm convergence.
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影响因子：
8.3
作者：
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通讯作者：
Yuliang Su;Xuening Chu;Dongping Chen;Xiwu Sun
DOI：
10.2514/6.2008-2580
发表时间：
2008-04
期刊：
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影响因子：
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DOI：
10.1016/j.tcs.2020.09.004
发表时间：
2020-08
期刊：
Theor. Comput. Sci.
影响因子：
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作者：
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通讯作者：
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影响因子：
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