自动排料中关键算法的研究

通过基于演化计算的全局搜索,寻找零件在排料时的最优次序及各自的最佳旋转角度,并采用最低最左原则(BL,Bottom-Left)实现自动排料.文中通过对自动排料中排料件图形的表示及数据结构描述,以及关键算法的实现讨论,将在处理不规则多边形的排料优化问题中判断点是否在多边形内部的通用方法射线法与多边形的单调性相结合,提出一种改进算法,并通过实验获得了较好的结果和性能.     

全景视频最小变形双极方形映射研究

球面全景视频映射为矩形视频后才能使用现有编码标准进行编码.针对映射过程中存在内容变形和数据冗余的问题,提出一种最小变形双极方形映射(Minimize Deformation Bipolar Square Projection,MDBSP)算法.算法分三步:将球面按纬度展开成一个等面积的、边界与纬度呈余弦关系的平面图形;用两个三角形和一个矩形组成的多边形近似该平面图形,并使变形度最小;将多边形内像素重排列成一个矩形.实验结果表明,MDBSP算法能有效解决内容变形和数据冗余的问题,压缩效率比经纬图映射提高17.48％.     

排料算法优化

用计算机进行计算、优化排料是剪、冲裁等材料加工过程中非常值得关注的问题。对贵金属、特种材料和高档装饰材料等,提高它们的利用率具有明显的经济效益。 排料优化问题依照算法和材料的不同可分为矩形板排料和连续带排料两大类。在剪裁加工中材料大多为矩形板材,常采用矩形板材优化排料算法。有时还要把冲剪顺序、冲剪方式等作为附加条件考虑在优化过程中。这类优化过程中按工件形状又分为两类：复杂工件排料、矩形工件排料。矩形板材排料过程中工件种类多,尺寸各异。 在连续冲裁加工中材料大多为宽度给定的连续带,工件种类单一。按照…     

基于遗传模拟退火混合算法的矩形包络求解

介绍了一种利用遗传模拟退火混合算法求解矩形包络的方法,该算法在优化搜索中能自适应地控制变异率,使得优化高效地逼近全局最优.矩形包络是将不规则多边形用它们的最小外接矩形来代替,就是求解该多边形的最佳包络矩形.实验结果表明,在解决矩形包络问题中,遗传模拟退火混合算法是一种高效的,可靠的优化算法,具有一定的实用价值.     

多边形中心点向量的二次插值变形算法

多不动点约束下的网格变形算法需要用户确定不动点和操作点,针对该问题,提出多边形中心点向量的二次插值变形算法。该算法根据源、目标多边形中心点向量间旋转经过的面积与2个向量间的差值建立相似度函数,在变形过程中采用二次贝塞尔插值方法,在对应过程中利用改进的动态规划算法。实验结果表明,该算法可减少变形过程中多边形内部扭曲的程度,且计算量小、对应时间短、变形效果自然。     

基于曲率匹配和递归排序的自适应排样算法

为提高盘状毛坯的使用率,提出一种口腔修复加工中模型边界在盘状毛坯中的排样算法,主要包括边界匹配、多边形定位及递归排序.首先基于协方差矩阵及矩阵SVD分解算法对模型多边形进行分段,并采用等弧长曲线采样曲率匹配确定待匹配模型边界;然后依毛坯边界角度分布及沿圆周排样的思想确定模型轮廓的旋转和平移定位;最后提出一种基于包络率的递归排序算法,对在排样过程中发现的大孔洞可动态地调整排样顺序.实验结果表明,该算法可以处理不规则模型边界在任意形状毛坯中的排样,能有效地降低加工成本.     

基于动作空间的三维装箱问题的确定性高效率求解算法

三维装箱问题要求将有限个三维矩形物体尽可能多地装入到一个三维矩形箱子中,使得箱子的填充率即体积利用率最大.在求解三维装箱问题的穴度算法的基础之上,进一步做了以下改进:(1)将当前剩余空间中可能放入的每个体积最大的三维矩形虚拟物体所对应的空间定义为动作空间,在动作空间内放入物体并使穴度的定义体现放入物体与动作空间的吻合程度;(2)在物体放入位置的选择上直接体现"金角银边草肚皮"的思想,每一步只选择最靠近箱子边缘的一个动作空间来装载物体;(3)结合捆绑策略,将形状大小相同的物体捆绑为一个较大的矩形块进行放入,对捆绑块形状大小的选择为在不超出动作空间的前提下尽量用物体填满该空间的两至三个维度.实验结果表明,改进后的穴度算法在付出很少的开销代价的情况下显著地提高了箱子的填充率.     

满足工艺品剪切要求的二维不规则排料算法

主要研究在矩形铁皮原材料上排放若干种二维不规则工艺品零件.对零件轮廓矢量图进行分类,采用形心重合定位填充算法对不规则图形进行组合,然后使用对排、单排、改进型的最低水平线排料策略,在满足"一刀切"的工艺品剪切要求和同种规格零件尽量放在一起的前提下,尽量提高原材料的利用率.实验结果表明排料算法的原材料利用率较高,同时满足剪切工艺要求.     

改进的蚂蚁算法在矩形优化排料中的应用

矩形件优化排料问题是一类具有NP完全难度的组合优化问题.将改进的蚂蚁算法应用到矩形件优化排料问题求解中,优化试验结果表明可获得比基本蚂蚁算法更好的效果,为矩形件优化排料这类NP完全问题提供了新的思路和方法.     

带预选搜索步深的二维一刀切矩形优化排料

排料问题是一种总体资源分配问题，其目标是将定量的资源划分为若干指定的份额。使剩余量极小。本文提出了一种新的二维一刀切矩形优化排料算法。实验结果表明，该算法效率高，灵活性强，可被广泛应用于许多相关排料领域。     

求解单位等边三角形Packing问题的近似算法

多边形Packing问题不仅具有重要的理论意义，而且也有广阔的应用前景，由于该问题具有NP难度，且具有连续的性质，一般要事先对多边形的放置方位进行限制，例如不允许多边形旋转，然后再进行优化求得近似解，该文采用一种新的思路对多边形Packing问题的一个特例－单位等边三角形Packing问题进行了研究，提出了零自由度动作和零自由度放置策略的概念，并设计了一个近似求解算法－最小损伤法，复杂性分析和计算结果表明该算法是高效的，以此为基础，可能为多边形Packing问题找到类似的求解算法。     

A Basic Algorithm for Computer-Aided Design of Material Arrangement

An algorithm has been obtained for solving the packing problem of placing convex polygons with different shapes and sizes into a rectangular vessel by simulating the elastic mechanics proscess.It is pointed out that,based on this alorithm,a sysem of computer-aided design can be developed for arranging two-dimensional materials.     

基于十字模板的特征点匹配方法

针对传统模板匹配中矩形模板计算量大、容易造成误匹配的问题,对矩形模板进行改进,提出了十字模板匹配算法;对待匹配的两幅图像进行边缘提取,结合两幅图像的灰度图像获得灰度边缘图像,并利用十字模板匹配方法匹配选定的模板区域;最终在连续性约束的条件下,利用欧氏距离完成特征点匹配。对比实验结果表明十字模板匹配速度快,准确度高;基于十字模板的特征点匹配方法简单可靠,具有一定的实用性。     

Solving the irregular strip packing problem via guided local search for overlap minimization

The irregular strip-packing problem (ISP) requires a given set of non-convex polygons to be placed without overlap within a rectangular container having a fixed width and a variable length, which is to be minimized. As a core sub-problem to solve ISP, we consider an overlap minimization problem (OMP) whose objective is to place all polygons into a container with given width and length so that the total amount of overlap between polygons is made as small as possible. We propose to use directional penetration depths to measure the amount of overlap between a pair of polygons, and present an efficient algorithm to find a position with the minimum overlap for each polygon when it is translated in a specified direction. Based on this, we develop a local search algorithm for OMP that translates a polygon in horizontal and vertical directions alternately. Then we incorporate it in our algorithm for OMP, which is a variant of the guided local search algorithm. Computational results show that our algorithm improves the best-known values of some well-known benchmark instances.     

基于离散粒子群优化算法求解矩形件排样问题

改进了一种近似排样算法,并将改进的近似排样算法与离散粒子群优化算法结合求解矩形件排样问题.设计了应用离散粒子群优化算法求解矩形件排样问题的相关操作和定义,给出了离散粒子群优化算法求解矩形件排样问题的详细步骤,最后通过实验测试,验证了算法的有效性.     

基于遗传模拟退火算法的矩形件优化排样

为了探索更高效的矩形件优化排样方法,提出了一种改进的自适应遗传模拟退火算法。设计了基于矩形件的排样次序及旋转变量的两层染色体编码方法,并采用基于临界多边形的BL定位策略实现矩形件的布局;通过构造启发式算法生成排样初始种群,然后各个种群之间通过相互竞争实现优秀个体的迁移与共享,最终搜索到最优解。标准测试问题的实验结果验证了所提算法的可行性与有效性。     

Heuristic for the rectangular strip packing problem with rotation of items

This paper presents a heuristic algorithm for the rectangular strip packing problem, where a set of rectangular items are packed orthogonally into a strip of definite width and infinite height, so as to minimize the required height. The items cannot overlap and rotation by 90 degrees is allowed. The solution contains several sections. The algorithm is based on a sequential grouping and value correction procedure that considers multiple candidate solutions. It generates each next section using a subset of the remaining items and then corrects the values of the included items. The algorithm is used to solve 13 groups of benchmark instances. It is able to improve the solution quality for all groups.     

New approaches to nesting rectangular patterns

In this study, two approaches are explored for the solution of the rectangular stock cutting problem: neuro-optimization, which integrates artificial neural networks and optimization methods; and genetic neuro-nesting, which combines artificial neural networks and genetic algorithms. In the first approach, an artificial neural network architecture is used to generate rectangular pattern configurations, to be used by the optimization model, with an acceptable scrap. Rectangular patterns of different sizes are selected as input to the network to generate the location and rotation of each pattern after they are combined. A mathematical programming model is used to determine the nesting of different sizes of rectangular patterns to meet the demand for rectangular blanks for a given planning horizon. The test data used in this study is generated randomly from a specific normal distribution. The average scrap percentage obtained is within acceptable limits. In the second approach, a genetic algorithm is used to generate sequences of the input patterns to be allocated on a finite width with infinite-length material. Each gene represents the sequence in which the patterns are to be allocated using the allocation algorithm developed. The scrap percentage of each allocation is used as an evaluation criterion for each gene for determining the best allocation while considering successive generations. The allocation algorithm uses the sliding method integrated with an artificial neural network based on the adaptive resonance theory (ART1) paradigm to allocate the patterns according to the sequence generated by the genetic algorithm. It slides an incoming pattern next to the allocated ones and keeps all scrap areas produced, which can be utilized in allocating a new pattern through the ART1 network. If there is a possible match with an incoming pattern and one of the scrap areas, the neural network selects the best match area and assigns the pattern. Both approaches gave satisfactory results. The second approach generated nests having packing densities in the range 95–97%. Improvement in packing densities was possible at the expense of excessive computational time. Parallel implementation of this unconventional approach could well bring a quick and satisfactory solution to this classical problem.     

A simple randomized algorithm for two-dimensional strip packing

Two-dimensional strip packing problem is to pack given rectangular pieces on a strip of stock sheet having fixed width and infinite height. Its aim is to minimize the height of the strip such that non-guillotinable and fix orientation constraints are meet. In this paper, an improved scoring rule is developed and the least waste priority strategy is introduced, and a randomized algorithm is presented for solving this problem. This algorithm is very simple and does not need to set any parameters. Computational results on a wide range of benchmark problem instances show that the proposed algorithm obtains a better or matching performance as compared to the most of the previously published meta-heuristics.