基于动态指标的飞机方案多目标优化方法

Dynamic index based method for multi-objective optimization in aircraft conceptual design

为提高飞机方案多目标优化过程中最优解的搜索效率,对多目标方案的比较评价方法及其在优化中的应用进行了研究.提出了可用于多目标方案对比评价的基准指标,并建立了利用新生成方案的目标值对基准指标进行动态更新的动态指标.通过采用动态指标构造适应度函数改进了多目标遗传算法,进行的双目标优化算例表明,改进的算法能够获得更优的Pareto前沿.采用改进的多目标优化方法对一种轻型战斗机概念方案进行了优化设计,设置了重量、气动、隐身等4个优化目标,优化结果验证了基于动态指标改进的多目标遗传算法在飞机概念方案设计优化中的有效性.

To increase the search efficiency of optimal solution in aircraft conceptual design multi-objective optimization,comparison evaluation technique for multi-objective model was studied along with the implementation. Index benchmark（ IB） was proposed for multi-objective model comparison,and dynamic index（DI） was conducted by updating the IB with the objective value of new solutions. Then multi-objective genetic algorithm（MOGA） was improved through conducting the fitness model with DI. The results of some optimization with two objectives indicate that better Pareto front can be obtained through the improved algorithm. Finally,the improved MOGA with DI（ DIMOGA） was used in the multi-objective optimization of a light fighter conceptual design,including four optimal objectives in discipline on weight,aerodynamic,and stealth. The optimization results validate the effectiveness of DIMOGA method in the aircraft conceptual design optimization.