遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法，如单纯形法、Powell法等，易陷入局部最优解，而遗传算法是一种新兴的直接搜索最优化算法，它模拟达尔遗传选择与自然进化的理论，根据“适生存”和“优胜劣汰”的原则，借助“复制”、“交换”、“突变”等操作可以得到全局最优解，本将遗传算法运用于电子枪发射系统的最优化设计，得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量。     

桁架结构优化设计的遗传算法

本文提出了桁架结构系统优化设计的新方法遗传算法，它不同于常规优化算法的特点在于，从多个初始点开始寻优，并采用交迭和变异算子避免过早地收敛到局部最优解，可获得全局最优解，且不受初始值影响。     

基于神经网络的结构模型修改

结构模型修改已经演化为一个多学科的研究课题，并且可以在最优化框架内进行解决。考虑到模型修改是一个典型的反问题，全局最优解并不一定是真止实际需要的解，提出了一种两步的结构模型修改方法，首先利用神经网络代替模型分析过程，然后根据实际测量的模态数据结合梯度下降算法得到修改结果，与利用遗传算法直接搜索最优模型修改结果相比，该方法不但可以减少模型分析次数，而且可以提供多种可能的修改结果供进一步参考。     

遗传算法中交叉算法的改进

遗传算法是模拟达尔文的自然选择学说和自然界的生物进化过程的一种计算模型。它采用简单的编码技术来表示各种复杂的结构,并通过对一组编码表示进行简单的遗传操作和优胜劣汰的自然选择来指导学习和确定搜索的方向。遗传算法的操作对象是一群二进制串（称为染色体、个体）,即种群。这里每一个染色体都对应问题的一个解。从初始种群出发,采用基于适应值比例的选择策略在当前种群中选择个体,使用杂交和变异来产生下一代种群。如此模仿生命的进化一代代演化下去,直到满足期望的终止条件为止。一般应用于在一个问题的解集中查找最优解情况,如是一个问题有多个答案,但是想查找一个最优答案的话,那么使用遗传算法可以达到更快更好的效果。本文就遗传算法中的交叉算法的改进进行讨论与研究。     

基于蛙跳思想的量子编码遗传算法

量子门旋转相位、变异概率大小的确定,是目前制约量子遗传算法效率的两个主要问题。本文提出一种基于蛙跳思想的量子编码遗传算法(QRGA),该算法采用自适应的方式对量子旋转门旋转角进行调整,并基于模糊逻辑将蛙跳的步长进行量化以指导变异概率调整,保证进化的方向性和提高算法效率,对比实验结果表明算法可以避免陷入局部最优解,并能快速收敛到全局最优解,在运行时间和解的性能上都取得了较好的效果。     

基于Pareto排序遗传算法的改进型扩张室压力脉动衰减器多目标优化

针对传统扩张室式压力脉动衰减器低、高频脉动衰减明显的不足,提出了两种改进结构。在衰减器外形尺寸不变的前提下,以最大化的回冲频率和固有频率处的滤波性能为目标,利用实数编码的标准遗传算法与Pareto排序的遗传算法分别对这两种改进结构进行参数优化,并结合一维平面波理论计算了其插入损失;在运用遗传算法进行优化的过程中,对影响优化的算法主要驱动算子、Pareto最优解、Pareto前沿的选取进行了讨论。结果表明,二维判据空间取得Pareto最优时,得到滤波器的各个结构参数非劣解,优化后的改进型扩张室式压力脉动衰减器在基频及其一次谐频处具有最优的滤波性能。     

遗传算法在桁架结构优化设计中的应用

本文提出桁架结构系统优化设计的新方法—遗传算法。它与常规化算法的不同之处在于从多个初始点开始寻优，并采用交迭和变异算子避免过早地收敛到局部最优解，可获得全局最优解，且不受初始值影响。该算法不必求导计算，编程简单、快捷，尤其适用于具有离散变量的结构优化设计问题。     

遗传算法在物流配送路径优化问题中的应用

遗传算法是一种基于自然进化原理的全局搜索随机算法。遗传算法在物流管理的运输问题、布局问题、选址问题、配送问题、调度问题等方面应用非常广泛。首先建立物流配送路径优化问题数学模型,在此基础上构造求解物流配送路径优化问题的遗传算法。用此遗传算法进行物流配送路径优化,可以方便有效地求得问题的最优解或近似最优解。     

遗传算法与惩罚函数法在机械优化设计中的应用

提出了应用于机械优化设计的"遗传算法+惩罚函数法"的通用算法.它非常适合求解复杂的非线性约束优化问题.本通用算法既克服了传统优化方法的缺点,得到了一个较为理想的全域最优解;同时也改善了遗传算法的局限性.     

Topology optimization of double- and triple-layer grids using a hybrid methodology

In this article, a hybrid methodology combining evolutionary structural optimization (ESO) and gravitational particle swarm (GPS) methods is proposed for topology optimization of double- and triple-layer grids. In the present methodology, which is called the ESO-GPS method, the size optimization of double- and triple-layer grids is first performed by ESO. Then, the outcomes of the ESO are used to improve the GPS through four modifications. Structural weight is minimized against constraints on the displacements of nodes, internal stresses and element slenderness ratio. The GPS is used to investigate the optimum topology of large-scale skeletal structures with discrete variables whose agents update their respective positions by the particle swarm optimization velocity and the acceleration of the gravitational search algorithm. The numerical results show that the proposed algorithm, the ESO-GPS, performs better than the GPS and the other methods presented in the literature.     

Bi-directional evolutionary topology optimization of continuum structures with one or multiple materials

There are several well-established techniques for the generation of solid-void optimal topologies such as solid isotropic material with penalization (SIMP) method and evolutionary structural optimization (ESO) and its later version bi-directional ESO (BESO) methods. Utilizing the material interpolation scheme, a new BESO method with a penalization parameter is developed in this paper. A number of examples are presented to demonstrate the capabilities of the proposed method for achieving convergent optimal solutions for structures with one or multiple materials. The results show that the optimal designs from the present BESO method are independent on the degree of penalization. The resulted optimal topologies and values of the objective function compare well with those of SIMP method.     

适用于支护拓扑优化的双向渐进优化方法

利用近年来发展的渐进结构优化方法(ESO)的概念,发展了适用于支护拓扑优化问题的双向渐进结构优化方法(BESO)。本文方法能在优化过程中控制开孔数,有效地避免了棋盘格式的出现,提出了两个解决振荡问题的技巧。将加固后的岩体看成人工支护材料,可以把加固优化的问题转化为人工支护材料在原岩中的分布的拓扑优化问题。建立了防治底臌帮臌的准则及其敏感度,研究了均匀地基不同地应力条件给定加固量时使得底臌帮臌最小的洞室开挖支护的最优拓扑。最优设计的底臌量明显小于经验设计的底臌量,证明了方法的有效性。     

叠层复合材料方板多孔形状优化

采用一种无梯度仿生技术——基于等限制Tsai-Hill值准则的固定网格渐进优化方法(FGESO),研究了叠层复合材料方板在拉剪荷载时不同孔数、不同叠层构造条件下的最优孔形问题。在孔的周围不断把限制Tsai-Hill值小于删除标准的材料删除,直到稳定状态达到,然后提高删除标准继续迭代,直到达到指定的开孔面积。与传统渐进优化方法(ESO)的不同之处在于利用节点而不是单元的限制Tsai-Hill值来确定需要删除的材料,因此得到了比ESO更光滑的结果。例子证明了方法的普适性和有效性。还研究了两孔方板最优孔形的优化历程,结果反映了相邻开孔相互影响的一些规律。     

Evolutionary topology optimization using the extended finite element method and isolines

This study presents a new algorithm for structural topological optimization of two-dimensional continuum structures by combining the extended finite element method (X-FEM) with an evolutionary optimization algorithm. Taking advantage of an isoline design approach for boundary representation in a fixed grid domain, X-FEM can be implemented to improve the accuracy of finite element solutions on the boundary during the optimization process. Although this approach does not use any remeshing or moving mesh algorithms, final topologies have smooth and clearly defined boundaries which need no further interpretation. Numerical comparisons of the converged solutions with standard bi-directional evolutionary structural optimization solutions show the efficiency of the proposed method, and comparison with the converged solutions using MSC NASTRAN confirms the high accuracy of this method.     

基于渐进结构优化的取料梁腹板拓扑优化

为了实现圆形堆取料机取料梁的轻量化设计,采用渐进结构优化方法(ESO)研究取料梁腹板的拓扑优化问题.在有限元软件ANSYS平台上利用其APDL语言进行二次开发实现了基于应力水平的渐进结构优化法,并结合使用灵敏度再分配技术控制棋盘格式,使各单元对结构性能的贡献或影响实现平滑过渡.最后获得了较清晰的取料梁腹板拓扑优化构形,为实现取料梁下一步设计及优化提供参考模型.     

A boundary density evolutionary topology optimization of continuum structures with smooth boundaries

Based on the variable density method, this article proposes a boundary density evolutionary topology optimization method. The method uses a material interpolation model without penalization. Combined with the density grading filtering method, an optimal topology with only 0/1 cells can be obtained. Compared with the solid isotropic microstructures with penalization method (SIMP), no penalty factor is required in the material interpolation model; compared with the evolutionary structural optimization method (ESO), intermediate-density elements are allowed in the optimization process, but the concept of gradually removing the low-utilization materials near the boundary in the ESO method is retained. After the optimal result is obtained, the structural boundary element is processed by the level set of nodal strain energy, and the optimization result with smooth boundaries similar to the level set method (LSM) can be obtained. The proposed method has the superiority of the variable density method, and it also combines the advantages of the evolutionary method and the level set method, so which is named as boundary density evolution (BDE) method. The four static and one dynamic optimization examples illustrate the stability and efficiency of the proposed method.     

约束阻尼板结构振动声辐射优化

根据经典薄板理论,建立约束阻尼板有限元模型,将其视作镶嵌于无限大刚性障板,利用Rayleigh积分法推导结构的辐射声功率及灵敏度表达式。以一阶峰值频率或频带激励下的声功率最小化为目标,约束阻尼材料体积分数为约束条件,建立拓扑优化模型,采用渐进优化算法,编制了优化计算程序,获得了约束阻尼材料的最优拓扑构型,并与全覆盖板及基板的辐射声功率进行了对比。研究表明：以声功率最小化为目标,对约束阻尼材料布局进行拓扑优化,能有效抑制结构的振动声辐射,为结构低噪声设计提供了重要的理论参考和技术手段。     

A strength-based multiple cutout optimization in composite plates using fixed grid finite element method

The design of interior cutouts in laminated composite panels is of great importance in aerospace, automobile and structural engineering. Based on the Tsai–Hill failure criterion of the first ply, this paper presents a newly developed Fixed (FG) Grid Evolutionary Structural Optimization (ESO) method to explore shape optimization of multiple cutouts in composite structures. Different design cases with varying number of cutouts, ply orientations and lay-up configurations are taken into account in this study. The examples demonstrate that the optimal boundaries produced by FG ESO are much smoother than those by traditional ESO. The results show the remarkable effects of different opening numbers and various lay-up configurations on resulting optimal shapes. The paper also provides an in-depth observation in the interactive influence of the adjacent cutouts on the optimal shapes.     

A hybrid cellular automaton–bi-directional evolutionary optimization algorithm for topological optimization of crashworthiness

This article proposes a new algorithm for topological optimization under dynamic loading which combines cellular automata with bi-directional evolutionary structural optimization (BESO). The local rules of cellular automata are used to update the design variables, which avoids the difficulty of obtaining gradient information under nonlinear collision conditions. The intermediate-density design problem of hybrid cellular automata is solved using the BESO concept of 0–1 binary discrete variables. Some improvement strategies are also proposed for the hybrid algorithm to solve certain problems in nonlinear topological optimization, e.g. numerical oscillation. Some typical examples of crashworthiness problems are provided to illustrate the efficiency of the proposed method and its ability to find the final optimal solution. Finally, numerical results obtained using the proposed algorithms are compared with reference examples taken from the literature. The results show that the hybrid method is computationally efficient and stable.