面向武器投放规划的自适应混合响应面优化方法研究
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摘要
武器投放作为战术飞机任务规划系统中的关键环节,其规划问题是一个多变量、高度非线性及不连续的约束优化问题。这类问题的最大特点是基于实际系统的决策指标与约束条件的评估过程需要的代价昂贵。论文以降低规划过程中的评估代价为目标,采用响应面近似模型作为代理模型参与规划的方式,主要从基于响应面的优化框架和混合响应面模型展开了研究,设计响应面优化方法及其在武器投放规划上的应用。论文的主要研究内容如下:
1.建立了基于响应面模型的优化框架。利用响应面方法可以根据样本快速建立一组独立变量与系统响应之间关系的近似模型这个优点,结合基于代理模型的优化技术,建立了一个基于响应面模型的优化框架,并对试验设计方法、建立优化问题及求解,响应面更新等四方面进行了研究,为设计响应面优化方法提供了可行的基本框架。
2.建立了混合响应面模型。响应面近似精度是影响优化效率的重要因素之一,将径向基函数和多项式混合作为响应面的近似函数,建立了混合响应面模型,提高了模型的近似精度,加速了整个优化过程。同时,避免了求解系数时可能出现奇异矩阵的情形,保证了模型的惟一性。
3.提出了自适应混合响应面优化方法。在混合响应面模型的基础上,结合对称拉丁超立方设计方法和周期动态约束策略,提出了自适应混合响应面优化方法,为防止优化过程陷入局部搜索加入重启动模式进行改进,进一步提高方法的优化性能。实验数据表明方法具有良好的的全局优化能力,且是一种稳定的响应面优化方法。
4.提出了基于响应面优化的武器投放规划问题求解方法。以典型侧跃升-俯冲攻击战术为对象,分析武器投放过程涉及的关键点,将关键点的参数作为决策变量,结合规划过程需要考虑的指标及约束条件,建立了武器投放规划的一般参数优化模型。在优化模型的基础上,提出了一种基于自适应响应面优化的武器投放规划问题求解方法。规划结果表明方法相比原先规划方法能够在较少的评估次数内得到的规划结果具有较优的指标并满足同样的约束条件。
This thesis focuses on weapon delivery planning that is an important aspect in the tactical aircraft mission planning. The weapon delivery planning can be regarded as a constraint optimization problem with multi-variables, highly nonlinear and discontinuous. The decision-goals and constraints based on actual systems are costly to be evaluated in these problems. To reduce mentioned evaluation cost, response surface model is proposed and developed as a surrogate-model for planning. A response surfaces based optimization framework is constructed. Afterwards, hybrid response surface models are designed. Furthermore, a response surface optimization method is proposed and applied into applications in weapon delivery planning. The main achievements and progress are as follows:
Firstly, an optimization framework based on response surface model is established. Response surface technique can quickly create an approximation between a set of independent variables and the system’s response. Therefore, an optimization framework is constructed by integrating the merits of response surfaces and the surrogate-model optimization. The design of experiments, establishment and solving of optimization problems, and updating of response surface are then discussed, respectively. This provides a feasible fundamental framework for further design of response surface optimization method.
Secondly, a hybrid response surface (HRS) is presented. The polynomial and radial basis functions are used to estimate the response surface approximation accuracy that may affect the optimization efficiency specifically. And the HRS is modeled to speed up the whole optimization process. It is significant that the HRS model can be free of the coefficient matrix that may appear singular to ensure the uniqueness of the model.
Thirdly, an adaptive hybrid response surface optimization (AHRSO) method is proposed. The AHRSO method is put forward on the basis of symmetric Latin hypercube design (SyLHD) and circle dynamic constrained (CDC) strategy. A restart mode is added to prevent the optimization process dropping into a local search, which improves the performance of the proposed method. Experimental results indicate that AHRSO is competitive for global optimization, and stable for global optimization.
Finally, an AHRSO based problem-solving method is presented for the weapon delivery planning. The key points of a typical Pop-up attack in the weapon delivery process are analyzed. Thus, the process parameters are regarded as decision variables. Weapon delivery planning problems are modeled as a standard optimization model with goals and constraints in the planning. A weapon delivery planning problems-solving method is then presented by using the response surface optimization. Compared with the original planning approaches, the presented method can get better planning results with less evaluation cost.
1.建立了基于响应面模型的优化框架。利用响应面方法可以根据样本快速建立一组独立变量与系统响应之间关系的近似模型这个优点,结合基于代理模型的优化技术,建立了一个基于响应面模型的优化框架,并对试验设计方法、建立优化问题及求解,响应面更新等四方面进行了研究,为设计响应面优化方法提供了可行的基本框架。
2.建立了混合响应面模型。响应面近似精度是影响优化效率的重要因素之一,将径向基函数和多项式混合作为响应面的近似函数,建立了混合响应面模型,提高了模型的近似精度,加速了整个优化过程。同时,避免了求解系数时可能出现奇异矩阵的情形,保证了模型的惟一性。
3.提出了自适应混合响应面优化方法。在混合响应面模型的基础上,结合对称拉丁超立方设计方法和周期动态约束策略,提出了自适应混合响应面优化方法,为防止优化过程陷入局部搜索加入重启动模式进行改进,进一步提高方法的优化性能。实验数据表明方法具有良好的的全局优化能力,且是一种稳定的响应面优化方法。
4.提出了基于响应面优化的武器投放规划问题求解方法。以典型侧跃升-俯冲攻击战术为对象,分析武器投放过程涉及的关键点,将关键点的参数作为决策变量,结合规划过程需要考虑的指标及约束条件,建立了武器投放规划的一般参数优化模型。在优化模型的基础上,提出了一种基于自适应响应面优化的武器投放规划问题求解方法。规划结果表明方法相比原先规划方法能够在较少的评估次数内得到的规划结果具有较优的指标并满足同样的约束条件。
This thesis focuses on weapon delivery planning that is an important aspect in the tactical aircraft mission planning. The weapon delivery planning can be regarded as a constraint optimization problem with multi-variables, highly nonlinear and discontinuous. The decision-goals and constraints based on actual systems are costly to be evaluated in these problems. To reduce mentioned evaluation cost, response surface model is proposed and developed as a surrogate-model for planning. A response surfaces based optimization framework is constructed. Afterwards, hybrid response surface models are designed. Furthermore, a response surface optimization method is proposed and applied into applications in weapon delivery planning. The main achievements and progress are as follows:
Firstly, an optimization framework based on response surface model is established. Response surface technique can quickly create an approximation between a set of independent variables and the system’s response. Therefore, an optimization framework is constructed by integrating the merits of response surfaces and the surrogate-model optimization. The design of experiments, establishment and solving of optimization problems, and updating of response surface are then discussed, respectively. This provides a feasible fundamental framework for further design of response surface optimization method.
Secondly, a hybrid response surface (HRS) is presented. The polynomial and radial basis functions are used to estimate the response surface approximation accuracy that may affect the optimization efficiency specifically. And the HRS is modeled to speed up the whole optimization process. It is significant that the HRS model can be free of the coefficient matrix that may appear singular to ensure the uniqueness of the model.
Thirdly, an adaptive hybrid response surface optimization (AHRSO) method is proposed. The AHRSO method is put forward on the basis of symmetric Latin hypercube design (SyLHD) and circle dynamic constrained (CDC) strategy. A restart mode is added to prevent the optimization process dropping into a local search, which improves the performance of the proposed method. Experimental results indicate that AHRSO is competitive for global optimization, and stable for global optimization.
Finally, an AHRSO based problem-solving method is presented for the weapon delivery planning. The key points of a typical Pop-up attack in the weapon delivery process are analyzed. Thus, the process parameters are regarded as decision variables. Weapon delivery planning problems are modeled as a standard optimization model with goals and constraints in the planning. A weapon delivery planning problems-solving method is then presented by using the response surface optimization. Compared with the original planning approaches, the presented method can get better planning results with less evaluation cost.
引文
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