一种基于蜜蜂进化选择算子的布局遗传算法

三维矩形布局问题属于NP 难问题,对于三维矩形布局问题的求解大多依赖于各 种启发式算法。该文以布局物体体积递减为定序规则,结合布局物体在布局空间中的几何可行 域,以吸引子法为定位规则,利用蜜蜂进化型遗传算法优化吸引子函数中的参数来求解三维矩 形布局问题(BEGA),得到新型布局遗传算法。最后对不同的算例进行了计算,并与以标准比 例选择作为选择算子的传统布局遗传算法(SPGA)等对比证明了该算法的有效性。     

模拟退火遗传算法的轮廓线拼接研究

基于断层数据的三维重构是三维数据场可视化技术的主要研究内容,重构物体的三维模型也是分析、仿真的前提.应用模拟退火遗传算法设计和实现了三维物体表面重建算法.通过在遗传算法中融人退火处理操作,提高了种群的多样性,避免了遗传算法中存在的早熟收敛问题,有效地增强了算法的全局寻优能力.此外,提出了一种基于边的最小交叉多边形算法,提高了交叉操作效率.根据轮廓线拼接问题的特点,设计了针对性强的交叉、变异操作.实验结果表明,算法有效地提高了传统的全局法轮廓线拼接算法的效率.     

手机动画空间布局规划的设计与实现

手机3D 动画自动生成系统是要实现从用户发送信息给服务器,经过信息抽取、情节规划、场景规划等一系列的处理,最终生成与短信内容相关的视频动画并发送给接收方这一过程。其中场景规划模块是在情节定性规划的基础上确定情节的各个细节,并将其量化到三维动画场景文件中。在动画情节规划的基础上,对动画场景规划模块中的三维场景空间布局问题进行研究,将三维场景可用空间根据物体的语义信息进行布局,基于语义网技术设计和实现三维场景的布局知识库,最终实现了三维物体的合理摆放,系统不仅保证了物体的无遮挡、无碰撞摆放,也实现了同一物体添加多个的情况,使物体的摆放具有多样性同时也体现了物体的语义信息。     

BBL布局的均场退火方法

均场退火方法既可以看作是一种新的神经网络计算模型,又可视为是对模拟退火的重大改进,提出了一个基于均场退火方法的任意单元布局算法,用一个三维二值换位矩阵将问题映射了为神经网络,建立包含重叠约束和优化目标的能量函数,再用均场退火方程迭代求解,每个单元只能放置在布局平面一个位置上的约束,用神经元归一化的方法解决。     

用神经网络求解时间驱动的宏单元布局问题

文中提出了一个宏单元布局的均场退火网络求解方法,算法用一个三维二值换位矩阵将问题映射为神经网络,建立包含时延约束,重叠约束和优化目标的能量函数,再用均场退火方程迭代求解。每个单元只能放置在布局平面一个位置上的约束用神经元归一化的方法解决。     

虚拟家居设计系统中基于约束的三维布局

在三维场景中对物体进行布局是一项费时而单调的工作。本文以虚拟家居设计系统为应用背景,分析了虚拟家居设计系统中三维布局操作的需求和特点以及系统中的约束类型,提出了一种基于约束的三维布局算法,旨在提高三维操作的便捷性,减少不合理设计结果。应用实例表明,该算法在虚拟家居设计系统的实际应用中取得了较好的效果,对于设计过程中的虚拟布局有重要借鉴意义。     

基于多实体的混合设计方法应用研究

目前通常有两种可用的设计方法：自顶向下和自底向上。骨架式自顶向下设计得 到了广泛的研究,但过于抽象不易于掌握,且忽略现有资源的应用及零部件间的位置及特征对 关联零部件设计的作用;另外自底向上设计脱离设计布局,设计效率低下。为解决两种设计存 在的问题,提出了基于三维多实体的模块化混合设计法。首先根据不同产品的结构特点,划分 了三维多实体布局类型,并简要介绍了布局方法。然后结合各种情况详细地阐述了混合设计中 最重要的参数关联问题,其中重点探讨了如何将设计加速器的设计结果嵌入到智能布局零件中, 以此实现末端需求控制前端设计的反求设计思想。最后结合典型设计案例阐述了混合设计法的 应用,表明混合设计法是可行且有效的。     

一种基于八叉树结构表达的三维实体布局启发式算法

本文在利用八叉树结构表达三维实体布局块及布局空间的基础上,根据八叉树同构节点匹配的思想设计了一个三维实体布局的启发式算法,并提出了三环图方法,解决了八叉树节点的同构识别问题．理论分析及计算的结果表明了该算法对于具有任意形状和大小的布局对象的三维布局问题来说效果理想．     

基于三维布局优化的实验课程安排算法

通过把实验课程安排表构造为一个三维空间，把被安排实验课程的班级人数构造为物体，这样实验课程安排问题就转化为三维布局问题。通过三维布局优化思想，采用八叉树算法分解布局空间，得到最优布局结果。将布局问题优化方法应用于实验课程的安排，使其被安排的时间和地点合理，且所占用的实验室空闲座位最少，从而提高了实验室利用率，缓解目前高校实验课程安排中容易出现的冲突和不平衡。     

遗传算法在钟表机芯设计中的应用

在钟表机芯设计中，齿轮参数的优化设计是一个组合优化问题，很难用传统优化方法解决.遗传算法是一种基于生物进化原理的启发式搜索方法，近年来，它成功地解决了许多计算难题.使用该算法的难点是如何将具体问题映射成适于该算法的编码以及根据编码进行各种操作.该文对传动系统各齿轮参数序号进行编码，成功地解决了齿轮参数的优化设计问题，也为一般机械设计中传动系统参数的优化提供了经验.通过比较，利用遗传算法得出的参数比用专家系统得出的参数更优.     

Optimal Packing Configuration Design with Finite-Circle Method

The Finite-circle Method (FCM) is further developed to solve 2D and 3D packing optimization problems with system compactness and moment of inertia constraints here. Instead of using the real geometrical shape as in existing solutions, we approximate the components and the design domain with circles of variant radii. Such approximation makes it possible to transform the original problem into a basic packing problem of FCM approximated components. Meanwhile, the overlapping between different components can be easily avoided by limiting the distance between corresponding circles in terms of their radii. With this formulation, the FCM provides a general and systematic approach and makes gradient-based optimization algorithms applicable. Furthermore, FCM has been extended to 3D packing problems by simply replacing circles with spheres in this paper. Several examples designing the compactness and moment of inertia of the component systems are presented to show the effect of FCM.     

Computation of interferences between three-dimensional objects and the optimal packing problem

The related problems of computing the separation/interference distance between two three-dimensional objects and clearance/protrusion distance between an object and a container wall occur frequently in industry. A method for computing these distances by solving appropriate optimization problems is presented. The method can treat three-dimensional objects with curved sides. The problem of optimally packing objects into a container is also discussed. The packing problem is also solved as an optimization problem The separation/interference and clearance/protrusion problems become subproblems to the packing problem. Examples are presented in the paper.     

Filter design: a finite dimensional convex optimization approach

We consider the design of transfer functions (filters) satisfying upper and lower bounds on the frequency response magnitude or on phase response, in the continuous and discrete time domains. The paper contribution is to prove that such problems are equivalent to finite dimensional convex optimization problems involving linear matrix inequality constraints. At now, such optimization problems can be efficiently solved. Note that this filter design problem is usually reduced to a semi infinite dimensional linear programming optimization problem under the additional assumption that the filter poles are fixed (for instance, when considering FIR design). Furthermore, the semi infinite dimensional optimization is practically solved, using a gridding approach on the frequency. In addition to be finite dimensional, our formulation allows to set or not the filter poles. These problems were mainly considered in signal processing. Our interest is to propose an approach dedicated to automatic control problems. In this paper, we focus on the following problems: design of weighting transfers for H_∞ control and design of lead‐lag networks for control. Numerical applications emphasize the interest of the proposed results. Copyright © 2003 John Wiley & Sons, Ltd.     

A Combinatorial Benders׳ decomposition for the lock scheduling problem

The Lock Scheduling Problem (LSP) is a combinatorial optimization problem that represents a real challenge for many harbours and waterway operators. The LSP consists of three strongly interconnected subproblems: scheduling lockages, assigning ships to chambers, and positioning the ships inside the chambers. These should be interpreted respectively as a scheduling, an assignment, and a packing problem. By combining the first two problems into a master problem and using the packing problem as a subproblem, a decomposition is achieved that can be solved efficiently by a Combinatorial Benders׳ approach. The master problem is solved first, thereby sequencing the ships into a number of lockages. Next, for each lockage, a packing subproblem is checked for feasibility, possibly returning a number of combinatorial inequalities (cuts) to the master problem. The result is an exact approach to the LSP. Experiments are conducted on a set of instances that were generated in correspondence with real world data. The results indicate that the decomposition approach significantly outperforms other exact approaches presented in the literature, in terms of solution quality and computation time.     

Reliability evaluation and optimal design in heterogeneous multi-state series-parallel systems

This paper addresses the heterogeneous redundancy allocation problem in multi-state series-parallel reliability structures with the objective to minimize the total cost of system design satisfying the given reliability constraint and the consumer load demand. The demand distribution is presented as a piecewise cumulative load curve and each subsystem is allowed to consist of parallel redundant components of not more than three types. The system uses binary capacitated components chosen from a list of available products to provide redundancy so as to increase system performance and reliability. The components are characterized by their feeding capacity, reliability and cost. A system that consists of elements with different reliability and productivity parameters has the capacity strongly dependent upon the selection of constituent components. A binomial probability based method to compute exact system reliability index is suggested. To analyze the problem and suggest an optimal/near-optimal system structure, an ant colony optimization algorithm has been presented. The solution approach consists of a series of simple steps as used in early ant colony optimization algorithms dealing with other optimization problems and offers straightforward analysis. Four multi-state system design problems have been solved for illustration. Two problems are taken from the literature and solved to compare the algorithm with the other existing methods. The other two problems are based upon randomly generated data. The results show that the method can be appealing to many researchers with regard to the time efficiency and yet without compromising over the solution quality.     

Approximate algorithms for the container loading problem

In this paper we develop several algorithms for solving three–dimensional cutting/packing problems. We begin by proposing an adaptation of the approach proposed in Hifi and Ouafi (1997) for solving two–staged unconstrained two–dimensional cutting problems. We show how the algorithm can be polynomially solved for producing a constant approximation ratio. We then extend this algorithm for developing better approximate algorithms. By using hill–climbing strategies, we construct some heuristics which produce a good trade–off between the computational time and the solution quality. The performance of the proposed algorithms is evaluated on different problem instances of the literature, with different sizes and densities (a total of 144 problem instances).     

函数优化异步并行演化算法

提出了一种新型、高效的函数优化异步并行演化算法,利用这个算法,在巨型并行计算机上解决了一些高难度的大型优化问题,其中包括一个超高维的非线性规划问题－BUMP问题。由于BUMP问题的强非线性和超我峰特性,目前还未见有超过50维的BUMP问题的结果发表。而在此不仅仅得到了从2维到50维迄今最好的解,而且一直计算到了1000000维,并得到了满意的结果。数值实验表明,新算法是鲁棒和高效的。     

基于启发式动态分解算法的矩形件优化排样

针对二维矩形件优化排样问题,提出了一种启发式动态分解算法,其可扩展用于三维及多容器全局排样求解。根据排放矩形件对容器进行正交动态分解,计算放置耦合度选择最佳子容器,通过干涉关系实现所有容器状态更新,实现大规模复杂排样问题的快速高效求解。对国际上公认Bench-mark多个问题例的计算结果表明,所提算法与同类算法相比优势明显,布局利用率提高达9.4%,计算效率提升达95.7%,并且已在商业化排样软件AutoCUT中应用,应用前景良好。     

基于改进初始解的遗传算法的布局设计方法

带性能约束的三维布局问题属于具有很强应用背景的组合优化问题,进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性,使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解,对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景,建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较,表明该算法具有较好的求解效率。