采用改进非支配近邻免疫算法的低轨混合星座设计优化

针对低轨同构星座覆盖资源在纬度上分布不均匀的不足,提出采用低轨混合星座提升覆盖均匀性的设计方案,并推导了满足全球任意点平均每天覆盖一定次数的最小卫星规模估算公式。针对非支配近邻免疫算法（NNIA）约束处理方面的不足,提出基于约束支配的改进非支配近邻免疫算法（Modified NNIA）,并以此设计了一种低轨混合星座优化平台来优化带约束的星座设计问题。仿真结果表明,改进的NNIA算法在收敛速度和多样性上均优于非支配分层遗传算法（NSGA II）和多目标粒子群算法（MOPSO）,可大大提高星座设计的效率。同时优化结果也表明低轨混合星座可提高覆盖的均匀性和大部分区域的覆盖次数,进而减少特定覆盖要求所需的卫星数目。     

Aerodynamic multi-objective integrated optimization based on principal component analysis

Based on improved multi-objective particle swarm optimization (MOPSO) algorithm with principal component analysis (PCA) methodology,an efficient high-dimension multiobjective optimization method is proposed,which,as the purpose of this paper,aims to improve the convergence of Pareto front in multi-objective optimization design.The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign) platform,which contains aerodynamics,stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft,in which the redundant objectives identified by PCA are transformed to optimization constraints,and several design methods are compared.The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached.The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.     

基于模型管理框架的机翼结构多目标优化设计

采用演化算法对某高空长航时无人机机翼结构进行多目标优化设计时,由于需要大量的演化迭代和很多次的有限元分析计算,使演化算法相当耗时.为了提高效率,采用模型管理框架对该机翼结构进行多目标优化设计.采用模型管理框架可以建立满足精度要求的目标及约束的近似模型,使演化算法不仅避免了大量的有限元分析计算,而且获得了满意的该高空长航时无人机机翼结构的多目标优化设计结果.     

LEO navigation augmentation constellation design with the multi-objective optimization approaches

Low Earth Orbit (LEO) satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently. A few LEO Navigation Augmentation (LEO-NA) constellations have been proposed, while corresponding constellation design methodologies have not been systematically studied. The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes. It is essentially different from the communication constellation or earth observing constellation design problem. In this study, we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency, namely the Position Dilution of Precision (PDOP), visible LEO satellites and the orbit altitude. A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm. To evaluate the performance of the optimized constellation, we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes: the polar constellation, the single-layer constellation and the two-layer constellation. The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation. The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.