Multi-objective optimization of inverse planning for accurate radiotherapy

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly,the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ)was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dosevolume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.     

目标函数设置对放疗逆向计划多目标优化过程的影响

目前放疗逆向计划中常用的目标函数有两种:基于剂量分布的目标函数和基于剂量．体积直方图（DVH）的目标函数。实际系统都基于单目标优化算法进行,不考虑逆向计划的多目标性。在Pareto多目标优化理论的基础上,研究逆向计划的目标函数设置问题,比较基于剂量分布的目标函数和基于DVH的目标函数对多目标优化过程的影响,包括优化时间、收敛性和存在的问题等,为逆向计划过程中多目标优化目标函数的设置提供依据。There are two kinds of objective functions in radiotherapy inverse planning: dose distribution-based and Dose-Volume Histogram （DVH）-based functions. The treatment planning in our days is still a trial and error process because the multi-objective problem is solved by transforming it into a single objective problem using a specific set of weights for each object. This work investigates the problem of objective function setting based on Pareto multi-optimization theory, and compares the effect on multi-objective inverse planning of those two kinds of objective functions including calculation time, converge speed, etc. The basis of objective function setting on inverse planning is discussed.     

A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation

In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.     

基于遗传算法的输电线路无人机巡检路径规划

针对无人机有效、安全巡检输电线路的路径问题,提出了一种基于遗传算法的输电线路无人机巡检路径规划方法,采用极坐标编码方式对无人机巡检路径构造染色体。结合实际情况中的无人机巡检各种约束问题,设计了适合于无人机巡检路径规划的遗传算子。实验结果证明算法能综合考虑各种因素,提高了全局寻优能力,是解决实际输电线路无人机巡检路径规划问题的较好办法。     

混沌免疫多目标算法求解认知引擎参数优化问题

合理的认知引擎参数设置可以提高频谱的使用性能. 通过分析认知无线网络中的认知引擎参数配置, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌免疫多目标优化的求解方法. 算法使用Logistic混沌映射初始化种群, 并在每一代将混沌特性用于最优解集的搜索; 设计了适合此问题的免疫克隆算子和抗体群更新算子, 保证了Pateto最优解集分布的多样性和均匀性. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 算法可以根据信道条件和用户服务的动态变化, 自适应调整各个子载波的发射功率和调制方式, 可以求出更多满足偏好需求的解, 满足认知引擎参数优化要求.     

Genetic algorithms for the QoS based multicast routing and wavelength allocation problem in WDM network

To tackle the QoS based multicast routing and wavelength allocation problem (MRWA), three multi-objective genetic algorithms are proposed, which are based on the ideas of Non-dominated Sorting, Strength Pareto and Decomposition, respectively. The chromosome coding scheme, crossover and mutation operators are redefined. To ensure the generated offspring being a connected light-tree, a light-path repair process and a loop eliminating process are designed. The proposed algorithms were evaluated on a set of different scale test problems and compared with the recently proposed GA based multi-objective optimization algorithm for this problem. The experimental results reveal very encouraging results in terms of the solution quality.     

Multi-objective strategy to optimize dithering technique for high-quality three-dimensional shape measurement

Dithering optimization techniques can be divided into the phase-optimized technique and the intensity-optimized technique. The problem with the former is the poor sensitivity to various defocusing amounts, and the problem with the latter is that it cannot enhance phase quality directly nor efficiently. In this paper, we present a multi-objective optimization framework for three-dimensional(3D) measurement by utilizing binary defocusing technique. Moreover, a binary patch optimization technique is used to solve the time-consuming issue of genetic algorithm. It is demonstrated that the presented technique consistently obtains significant phase performance improvement under various defocusing amounts.     

Optimization analysis of the thermal cloaking problem for a cylindrical body

The inverse heat transfer problem associated with constructing multilayer material shells cloaking the presence of a cylindrical body in an externally applied temperature field is studied. As the original mathematical model, the steady-state heat equation for an anisotropic shell is used. With the help of the optimization method, this inverse problem is reduced to the corresponding control problem. A numerical algorithm of its solution based on the particle swarm optimization is proposed, and the results of numerical experiments are discussed.     

集中供热系统“管-站”布局及管网优化设计方法研究

对大型集中供热系统而言,设计过程包括"管-站"布局设计和管网系统设计。根据最优化理论,提出了集中供热系统的两级优化设计模型,即第一级的"管站"布局优化和第二级的管网系统多目标优化设计模型。由于"管-站"布局优化模型属NP-hard问题(不确定问题),为寻求有效解法,对"管站"布局优化模型提出两步分级法求解,即优化过程分两步进行,第一步为分区优化,第二步为选址优化。运用本文所述方法对辽河油田振兴区集中供热改造工程,进行了优化设计,取得了较好效果。     

基因算法在光阴极注入器优化设计中的应用

基于正在建设的大连自由电子激光装置,研究了基因算法在注入器优化设计中的系统应用。与传统的注入器优化方法相比较,基因算法在多变量、多目标注入器优化问题中具有优化效率高、优化结果精确的优势;通过采用权重因子的方法将多目标问题转化为单目标优化问题,大大简化了基因算法优化流程;光阴极注入器对束流稳定性要求严格,通过基因算法优化使束流的到达时间抖动控制到150fs,满足束流稳定性的要求。     

自然循环流动不稳定性的多目标优化极限学习机预测方法

极限学习机是近年来提出的一种前向单隐层神经网络训练算法,具有训练速度快、不会陷入局部最优等优点,但其性能会受到随机选取的输入权值和阈值的影响.针对这一问题,提出一种基于多目标优化的改进极限学习机,将训练误差和输出层权值的均方最小化同时作为优化目标,采用带精英策略的快速非支配排序遗传算法对极限学习机的输入层到隐层的权值和阈值进行优化.将该算法应用于摇摆工况下自然循环系统不规则复合型流量脉动的多步滚动预测,分析了训练误差和输出层权值对不同步长预测效果的影响.仿真结果表明,优化极限学习机预测误差可以用较小的网络规模获得很好的泛化能力.为流动不稳定性的实时预测提供了一种准确度较高的途径,其预测结果可以作为核动力系统操作员的参考.     

基于分解策略的飞行器气动隐身优化设计研究

为了综合提高飞行器的气动性能与隐身性能,文章利用基于分解策略的梯度优化方法,对某跨声速机翼进行气动隐身综合优化设计.采用Tchebycheff方法,将气动隐身多学科多目标优化问题分解为多个单目标优化子问题,再对每个单目标子问题进行梯度优化.通过求解离散伴随方程获得气动目标对设计变量的梯度,采用自动微分方法对物理光学法（physical optics,PO）程序进行微分,即可得到雷达散射截面（radar cross section,RCS）对设计变量的梯度.经过综合优化,获得优化解集中各给定权重系数对应下的分支解,相比初始机翼,优化机翼的阻力系数减小,升阻比提高,重点方位的雷达散射截面均值减小,验证了该优化设计方法具有较好的实用性.      

三维跨音速压气机叶栅多目标气动优化设计

提出了一种多目标差分进化算法(MDE),典型函数试验结果表明该算法具有优秀的多日标寻优能力,满足多目标气动优化设计的要求.耦合并行多目标差分进化算法、三次B样条曲面造型方法和CFD求解技术,提出了新的轴流式叶轮机械叶栅三维多目标气动优化设计方法.选择等熵效率和总压比为目标函数,完成了NASA Rotor37转子叶栅的多目标气动优化设计.优化后叶栅的气动性能明显提高,表明该方法具有良好的优化性能和应用前景.     

超-超引射器多目标优化设计

推导出了超-超引射器性能计算和优化设计模型,借助Pareto优胜、Pareto最优解和Pareto前端等概念,采用基于多目标进化/分解算法（MOEA/D）的多目标优化方法,计算得到超-超引射器多目标优化问题的Pareto前端,解决了超-超引射器多目标优化设计问题,并与常规参数分析方法进行了比较。结果表明:超-超引射器性能影响参数相互关系复杂,增压比和引射系数作为引射器主要性能参数相互冲突,通过常规分析难以得到较清晰的设计准则,利用多目标优化设计方法可有效地辅助多属性决策和系统优化设计。     

叶轮机械叶栅多目标多学科设计优化算法

提出了基于多目标寻优的叶轮机械叶栅多学科设计优化算法,方法包括:采用并行多目标差分进化算法作为优化求解器来搜寻叶栅多学科设计优化问题的Paxeto解集,采用非均匀B样条方法对叶片型面进行参数化处理,通过求解Reynolds-Avergaed Navier-Stokes方程评估叶片的气动性能,耦合气动计算得到的叶片表面压力,应用有限元分析方法预测叶片的强度性能.为证明本文方法的实用性,选择叶片的等熵效率和叶片应力为目标函数,完成了NASA Rotor 37转子叶栅的多学科设计优化,结果表明本文提出的多学科设计优化算法具有良好的优化性能.     

The optimization method in design problems of spherical layered thermal shells

The inverse problem of designing multilayered spherical shells, intended for thermal cloaking a spherical body or concentrating heat in it, has been analyzed. The stationary heat conduction equation for an anisotropic medium is applied as an original mathematical model. The optimization method is used to reduce this inverse problem to an extreme problem, where the role of controls is played by the thermal conductivities of shell layers. A numerical algorithm for solving the problem is proposed, and the results of computational experiments are discussed.     

阻抗障碍物声散射的反问题

研究了从声散射场的远场分布的信息来再现声阻抗障碍物形状的反问题,建立了求解这类反问题的一种非线性最优化模型,并提出了数值实现该非线性最优化模型的一种两步调整迭代算法.两步过程的应用使在确定未知障碍物形状的非线性最优化步中未知函数的个数达到了最少,而在调整迭代过程中,通过利用前一迭代步所得重构信息,使重构精度得到了相当大的改进.所建立的反演算法的一个特别吸引人的性质是,只需要远场分布的一个Fourier系数即可对未知声阻抗障碍物作几何物形的设别.对大量具有各种几何形状的二维障碍物的数值算例保证了本算法是实用和有效的.     

基于无导数优化方法的数值模式误差估计

初始场误差和模式误差是制约数值预报准确率的两个关键因素,本文主要考虑利用历史观测资料实现时空演变的模式误差的估计问题.通过把模式误差综合考虑成为准确模式中的未知项,把历史资料看作是带有未知项的准确模式的特解,构造了求解时空演变的模式误差项的反问题及其最优控制问题.给出了一个解决最优控制问题的无导数优化方法,该方法的优点是不需要建立原数值模式的切线性模式与伴随模式,它只需在增加一个外强迫项的基础上运行原数值模式即可实现模式误差项的最优估计.关于Burgers方程的算例表明,无论模式的初始状态是否准确已知,无导数优化方法都能有效解决时空演变的模式误差的最优估计问题,它为实际业务模式利用历史数据提取模式误差信息并显著地改进预报效果提供了一种方便可行的数值方法与理论依据.     

Inversion of Robin coefficient by a spectral stochastic finite element approach

This paper investigates a variational approach to the nonlinear stochastic inverse problem of probabilistically calibrating the Robin coefficient from boundary measurements for the steady-state heat conduction. The problem is formulated into an optimization problem, and mathematical properties relevant to its numerical computations are investigated. The spectral stochastic finite element method using polynomial chaos is utilized for the discretization of the optimization problem, and its convergence is analyzed. The nonlinear conjugate gradient method is derived for the optimization system. Numerical results for several two-dimensional problems are presented to illustrate the accuracy and efficiency of the stochastic finite element method.