基于NSGA-Ⅱ多目标优化的C2组织设计

把NSGA-Ⅱ算法用于求解C2组织设计问题.分析了C2组织设计常见处理算法在优化目标处理和算法流程两方面存在的问题,给出用NSGA-Ⅱ算法求解C2组织设计问题的算法设置.把NSGA-Ⅱ这样一种多目标优化算法引入C2组织设计问题,改变了以往研究此类问题时只能定义单个指标的情况,使领域专家能定义和研究新的优化目标.针对C2组织设计问题的特性做了调整后,实验结果数据表明NSGA-Ⅱ可以迅速地同时得到高质量和富有启发性的一群优化结果.     

一种基于Taguchi方法的混合NSGA-Ⅱ算法

提出一种基于Taguchi方法的混合NSGA-Ⅱ算法,即用Taguchi方法来改造NSGA-Ⅱ算法的交叉操作和变异操作,目的是提升NSGA-Ⅱ算法的优化能力.针对多目标优化测试问题的实验表明该方法能够显著提高NSGA-Ⅱ算法的优化效果,而且该方法不改变NSGA-Ⅱ的算法框架,易于实现.     

基于改进NSGA-II算法的多目标无人机路径规划

路径规划是无人机自主智能飞行的关键技术之一。以路径长度和受威胁程度为优化指标,提出无人机路径规划的多目标优化模型。为找出一组分布多样化的最优路径,提出改进的NSGA-Ⅱ算法,该方法在经典智能多目标优化算法NSGA-Ⅱ的基础上,引入增加、删除算子使规划的路径能避开威胁区、引入最大拐弯角约束缓解变异操作导致的航路突变、引入混合目标空间和决策空间信息的新型拥挤距离算子提高路径的多样性。仿真实验表明,对比NSGA-Ⅱ和传统的GA算法,改进的NSGA-Ⅱ算法能够有效找到一组收敛性好且分布多样化的路径。     

基于NSGA-Ⅱ算法的轨道转移优化方法研究

在普适变量法求解Lambert转移问题基础上,提出基于NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ)算法的轨道转移时间-能量优化问题解决方法:通过修改非优超排序方法和采用空间扩张策略对NSGA-Ⅱ算法进行了改进,运用约束支配的概念解决了约束条件下Pareto最优集分层困难的问题。仿真实验表明:改进的NSGA-Ⅱ算法能有效求解轨道转移时间-能量优化问题,且比原算法提高了Pareto前沿散布性能。     

有源干扰条件下基于NSGA-Ⅱ的雷达网优化部署方法

在有源干扰条件下,雷达网部署直接影响着防区内指挥信息系统的预警监测能力。由于防区内由分散于不同位置,且重要度不同的责任区组成的,那么实现全方位全纵深的预警能力,将是雷达网部署的重要方面。根据覆盖系数和重叠系数为主要优化目标,基于NSGA-Ⅱ算法进行多目标优化。首先定义了覆盖系数和全局重叠系数两个指标,尤其是全局重叠系数打破了以往重叠系数的概念,从全局出发引导雷达网优化部署;同时,提出基于NSGA-Ⅱ的多目标优化部署算法,采用诱导跳跃、基因到位、诱导交叉等候选解生成方式,保持种群多样性,提高算法收敛性。实验表明,部署优化算法耗时较低,不同干扰源部署态势使网络节点部署产生较大差异,多样的候选解生成方法明显提高了算法的收敛速度。     

基于NSGA-Ⅱ的防空部署优化方法

针对防空部署的特点,根据Markov状态转移链建立了防空武器的防御效能模型,并在考虑了防空武器平均防御总成本的基础上结合防御效能模型建立了多目标的防空部署优化模型,进而设计了求解防空部署优化模型的NSGA-Ⅱ多目标优化算法,并通过仿真得到Pareto最优解集,从中选取了相对最优解作为防空部署优化的最终结果.仿真结果表明...     

基于排挤机制改进的多目标进化算法

进化算法是求解多目标优化问题(MOP)重要而有效的方法。为加快收敛速度,提高收敛精度,在已有算法(NSGA-Ⅱ)的基础上,引进小生境思想,提出了更为合理的排挤机制。通过典型应用函数的计算测试,结果表明:上述改进不仅具有较高的计算效率,而且能够得到分布更为合理的解,且能保持解的多样性分布。     

基于NSGA-Ⅱ的WTA多目标优化(英文)

将多目标遗传算法NSGA-(改进的非支配排序遗传算法)应用于求解武器-目标分配(WTA)问题。首先,针对以往在建立防空型WTA问题的优化模型上的片面性,把WTA问题看做多目标优化问题,建立了综合考虑作战效能和防御效能的WTA双目标优化模型。然后在此基础上,研究和应用了NSGA-来求解WTA问题。最后由仿真算例验证了NSGA-在WTA问题中的应用可行性,表明了NSGA-可以快速地搜索到WTA多目标优化的Pareto最优解集,从而为求解WTA问题提供了一条有效途径。     

改进MOPSO的联合火力打击目标分配

在舰载联合火力打击的武器-目标分配问题中,针对失败概率最小和使用武器最少原则,设计了一种改进的多目标粒子群优化算法(MOPSO)来进行分配的优化。将自适应变异方法用于更新外部档案集;随机挑选外部档案集内依据拥挤距离由大到小排在前5%的解当作全局最优值;通过利用改进的学习因子和惯性权重来更新粒子。仿真结果表明,设计的算法比带精英机制的非支配排序遗传算法(NSGA-Ⅱ)运行速度更快、求得的Pareto前沿解的精度更高,能有效地解决舰载联合火力打击目标分配问题。     

多平台空间目标观测协同调度问题

针对空间目标观测的平台多样性、单次观测不确定性、任务层次优先性等现实问题,从多平台任务协同分配的角度,统一考虑夭基、地基固定、地基移动观测平台,建立面向空间目标应急观测异构平台预调度模型,基于STK和改进NSGA-Ⅱ算法进行了仿真验证,为构建天地一体空间目标观测体系建立提供有益借鉴.     

Optimal design of a novel cylindrical sandwich panel with double arrow auxetic core under air blast loading

The increasing threat of explosions on the battle field and the terrorist action requires the development of more effective blast resistance materials and structures. Curved structure can support the external loads effectively by virtue of their spatial curvature. In review of the excellent energy absorption property of auxetic structure, employing auxetic structure as core material in curved sandwich shows the potential to improve the protection performance. In this study, a novel cylindrical sandwich panel with double arrow auxetic (DAA) core was designed and the numerical model was built by ABAQUS. Due to the complexity of the structure, systematic parameter study and optimal design are conducted. Two cases of optimal design were considered, case1 focuses on reducing the deflection and mass of the structure, while case2 focuses on reducing the deflection and increasing the energy absorption per unit mass. Parameter study and optimal design were conducted based on Latin Hypercube Sampling (LHD) method, artificial neural networks (ANN) metamodel and the nondominated sorting genetic algorithm (NSGA-Ⅱ). The Pareto front was obtained and the cylindrical DAA structure performed much better than its equal solid panel in both blast resistance and energy absorption capacity. Optimization results can be used as a reference for different applications.     

多目标的分布式协同进化MDO算法

通过引入非优超排序和排挤的多目标处理机制 ,将分布式协同进化MDO算法的能力扩展到多目标的多学科设计优化问题。多目标的分布式协同进化MDO算法在保持各学科充分自治和各学科并行设计优化协同的基础上 ,通过一次运行即可获得具有良好分布的多个Pareto最优解 ,逼近整个Pareto最优前沿。应用于导弹气动 /发动机 /控制三学科两目标设计优化问题 ,与约束法计算结果的对比表明算法能够有效逼近该问题的Pareto最优前沿 ,为设计决策提供了丰富的信息     

An interactive branch-and-bound algorithm for bicriterion nonconvex/mixed integer programming

Although there has been extensive research on interactive multiple objective decision making in the last two decades, there is still a need for specialized interactive algorithms that exploit the relatively simple structure of bicriterion programming problems. This article develops an interactive branch-and-bound algorithm for bicriterion nonconvex programming problems. The algorithm searches among only the set of nondominated solutions since one of them is a most preferred solution that maximizes the overall value function of the decision maker over the set of achievable solutions. The interactive branch-and-bound algorithm requires only pairwise preference comparisons from the decision maker. Based on the decision maker's responses, the algorithm reduces the set of nondominated solutions and terminates with his most preferred nondominated solution. Branching corresponds to dividing the subset of nondominated solutions considered at a node into two subsets. The incumbent solution is updated based on the preference of the decision maker between two nondominated solutions. Fathoming decisions are based on the decision maker's preference between the incumbent solution and the ideal solution of the node in consideration.     

应用单亲遗传算法进行大规模UCAVs任务分配

在应用GA求解大规模无人作战飞机(UCAVs)任务分配这个典型组合优化问题时,需要使用描述问题直观的序号编码方式,但由于传统的交叉、变异算子操作复杂,因而进化效率不高.针对上述的不足,提出了一种单亲遗传算法,采用序号编码,使用基因换位等遗传算子,简化了遗传操作.通过对单亲遗传算法、传统遗传算法求解该问题所得的结果作了详细的比较,证明了单亲遗传算法在寻优效率上的优越性.     

Approximating nondominated sets in continuous multiobjective optimization problems

Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or ε‐constrained problems, or discretizations‐based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation‐based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

独立分量分析在雷达盲信号处理上的应用

独立分量分析(ICA)是近年来发展起来的一种有效的雷达盲信源分离方法。目前新体制雷达的大量涌现,运用原有的雷达分选方法是无法分选的,这已经无法适应现代复杂的电磁环境。在深入分析FastICA算法的基础上,将其应用于连续波雷达信号分选中。计算机仿真表明,这种算法应用于连续波雷达盲信号分选时,不仅提高了信号的分选速度和信号分离的准确率,而且可以有效地提高信号分选的效能,也为盲信号处理提供了一种新的思路。     

基于区域定位重要度指标的GPS失效性分析

空间几何构型是决定GPS定位精度的关键因素之一。采用概率统计的思想,基于区域定位重要度指标算法,通过分析计算不同空间几何构型的几何精度系数,寻求对区域定位精度影响最大的卫星构型。在此基础上对GPS卫星进行重要度排序,并对GPS星座失效性进行分析。结果表明:此方法能有效找出对区域定位精度影响最大的卫星构型,并且避免了大规模仿真计算,快捷有效。     

Approximating the nondominated frontiers of multi‐objective combinatorial optimization problems

Finding all nondominated vectors for multi‐objective combinatorial optimization (MOCO) problems is computationally very hard in general. We approximate the nondominated frontiers of MOCO problems by fitting smooth hypersurfaces. For a given problem, we fit the hypersurface using a single nondominated reference vector. We experiment with different types of MOCO problems and demonstrate that in all cases the fitted hypersurfaces approximate all nondominated vectors well. We discuss that such an approximation is useful to find the neighborhood of preferred regions of the nondominated vectors with very little computational effort. Further computational effort can then be spent in the identified region to find the actual nondominated vectors the decision maker will prefer. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

基于GA-PSO的目标分配问题研究

防空作战中的目标分配问题属于NP完全问题,在综合考虑火力单元作战效能和防御效能的基础上,引入火力单元综合有利度,建立了一种用于大规模多火力单元对抗多轮次目标的静态目标分配模型,给出了基于GA因子的改进PSO算法,并将算法应用于目标分配模型的求解,通过VC 6.0编程和计算机仿真验证了模型的可行性和算法的有效性.     

各向异性介质体目标电磁散射分析的快速算法

利用体积分方程矩量法(MoM)及其多层快速多级子算法(MLFMA)研究了各向异性的电介质体或/和磁介质体目标的电磁散射问题,将多层快速多级子算法的计算结果与解析方法(Mie)或矩量法结果进行了对比分析.不但验证了MLFMA算法的高效性和精确性,同时该算法还适合于分析模拟任意复杂介质体(如各向异性磁化等离子体)目标的电磁散射问题,为进一步实现电大尺寸的任意复杂各向异性介质体/金属组合目标电磁散射分析的精确高效算法奠定了基础.