Application of improved genetic algorithms in aircraft conceptual parameter optimization design
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摘要: 基于飞机总体参数设计中的多目标优化问题,提出了改进的多目标遗传算法.算法围绕Pareto最优解的概念,利用遗传算法的内在并行性,设法求取多目标优化问题的"Pareto前沿".将不同的改进遗传算法应用于同一干线客机总体参数优化设计中,要求巡航升阻比和有效载荷系数两个目标达到最大,并对各种算法所得的结果进行综合分析与比较,结果显示:基于Pareto排序的多目标优化算法(NSGA,Non-dominated Sorting Genetic Algorithm)的Pareto解最优,可以支配改进的向量评价遗传算法(VEGA,Vector-Evaluated Genetic Algorithm)和随机权重遗传算法(RWGA,Random-Weight Genetic Algorithm)的结果;而VEGA和RWGA的结果互有优劣.Abstract: Based on the multi-objective optimization problems in the aircraft conceptual parameter design, improved multi-objective genetic algorithms were proposed. The "Pareto front "of Multi-objective optimization (MO) was tried to seek by using inherent parallelism of genetic algorithms (GA) while emphasizing the Pareto optimal conception. Different improved genetic algorithms were applied to a same two-objective optimization arterial airliner conceptual parameter optimization design, where both the ratio of lift to drag in cruise and the useful load fraction were asked to be maximized. Through general analysis and comparison to solutions of different algorithms, it can conclude that the Pareto optimal results of the non-dominated sorting genetic algorithm(NSGA) are better than the improved vector-evaluated genetic algorithm(VEGA) and the random-weight genetic algorithm(RWGA), while the results of VEGA and RWGA are equal.
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Key words:
- multiple objective optimization /
- aircraft design /
- Pareto optimal /
- genetic algorithms
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