Scatter search and star-paths: beyond the genetic metaphor

Scatter search and genetic algorithms have originated from somewhat different traditions and perspectives, yet exhibit features that are strongly complementary. Links between the approaches have increased in recent years as variants of genetic algorithms have been introduced that embody themes in closer harmony with those of scatter search. Some researchers are now beginning to take advantage of these connections by identifying additional ways to incorporate elements of scatter search into genetic algorithm approaches. There remain aspects of the scatter approach that have not been exploited in conjunction with genetic algorithms, yet that provide ways to achieve goals that are basic to the genetic algorithm design. Part of the gap in implementing hybrids of these procedures may derive from relying too literally on the genetic metaphor, which in its narrower interpretation does not readily accommodate the strategic elements underlying scatter search. The theme of this paper is to show there are benefits to be gained by going beyond a perspective constrained too tightly by the connotations of the term genetic. We show that the scatter search framework directly leads to processes for combining solutions that exhibit special properties for exploiting combinatorial optimization problems. In the setting of zero-one integer programming, we identify a mapping that gives new ways to create combined solutions, producing constructions calledstar-paths for exploring the zero-one solution space. Star-path trajectories have the special property of lying within regions assured to include optimal solutions. They also can be exploited in association with both cutting plane and extreme point solution approaches. These outcomes motivate a deeper look into current conceptions of appropriate ways to combine solutions, and disclose there are more powerful methods to derive information from these combinations than those traditionally applied.This research is supported in part by the Joint Air Force Office of Scientific Research and Office of Naval Research Contract No. F49620-90-C-0033 at the University of Colorado     

考虑循环寿命折损的主动配电网仿射可调鲁棒优化方法

针对主动配电网在源荷储协同下的不确定性优化问题,提出了基于仿射可调鲁棒优化的主动配电网运行策略。首先,考虑分布式光伏的可控模型、循环寿命折损的电池储能模型和ZIP负荷模型,构建了主动配电网优化运行模型。然后,采用椭球集合来描述分布式光伏出力的不确定性,并结合仿射策略形成优化模型中决策变量与分布式光伏出力不确定性变量之间的线性决策机制。最后,通过对偶变换将主动配电网鲁棒优化模型转化为一个确定性的混合整数二阶锥规划模型进行求解。在改进的IEEE33节点算例系统中对比验证了所提出的仿射可调鲁棒优化方法的有效性和优点。     

考虑电池储能老化和需求侧响应的交直流混合配电网动态重构方法

现有的电池储能模型一方面认为电池的充放电功率和效率为固定不变的,另一方面有关电池储能的充放电过程对循环老化的影响缺少考虑,这与电池单元本体的非线性等效电路模型相违背。因此,考虑电池储能的循环老化成本、需求侧响应,提出了交直流混合配电网动态重构方法。通过对实际电池储能的充放电功率的运行区域进行采样,在凸包络的采样点集合中将非理想状态下的电池储能模型重构为线性模型。在此基础上,分别建立了基于累积吞吐电量和放电深度的循环老化成本模型,构建了综合成本最小化的交直流混合配电网动态重构模型。并采用混合整数二阶锥规划方法求解。在改进的33节点算例上仿真验证了所提基于放电深度的循环老化成本模型的经济性,分析了交直流混合配电网重构方法经济性的影响因素。     

分支定价方法求解带二维装箱约束的车辆路径问题EI北大核心CSCD

面向家具、电器等货物的物流配送场景,研究带二维装箱约束的车辆路径问题(2L–CVRP),构建了2L–CVRP的混合整数线性规划模型.为求解大规模2L–CVRP,构建了该问题集合划分模型,提出基于分支定价的方法.针对分支节点的松弛模型,基于列生成策略将其分解为线性规划主问题、带资源和二维装箱约束的最短路径子问题,并提出基于ng-route松弛策略的标签算法和基于禁忌搜索的装箱算法有效求解复杂子问题.仿真结果表明,提出的方法可高效求解大规模2L–CVRP,其中ng-route松弛策略能有效提升算法求解效率,研究成果为装箱约束下大规模车辆路径问题的高效求解提供了有效途径.     

基于高速环形振荡器的皮秒量级事件计时测量

研究设计基于高速环形振荡器的皮秒量级事件计时器。利用代表事件的信号上升沿去触发高速环形振荡器,产生与事件同步的时钟脉冲信号,对正弦参考信号采样,再通过全相位FFT算法处理,大幅提高事件计时测量的精度。实验结果表明,在正弦参考信号中心频率f₀=10 MHz,全相位FFT运算点数N=8 192,ADC的量化位数b=14 bits,采样频率f_s=140 MHz的情况下,事件计时器能够获得约3.16 ps rms的单次测量精度,时间稳定性优于±0.31 ps/h,实验结果与基于理论分析的误差范围一致,达到皮秒量级事件计时测量。     

快速扫描机载气象雷达多普勒解模糊算法

本文针对机载气象雷达前视快速扫描下的多普勒模糊问题,提出了两种多普勒解模糊算法：基于改进方位向波束形成算法和基于阻塞矩阵算法。基于改进方位向波束形成算法在传统的Capon波束形成的基础上,利用空间谱积分的方法对非期望信号协方差矩阵进行重构和对导向矢量进行估计,进一步提高了算法的鲁棒性,降低方位角估计误差;基于阻塞矩阵算法需要估计导向矢量,并利用导向矢量计算阻塞矩阵,将接收信号进行多普勒模糊相消预处理,阻塞每个多普勒频点的模糊多普勒分量,以达到解模糊的目的。通过仿真实验证明,两种方法均可以有效地恢复信号的多普勒信息,其中基于阻塞矩阵算法与基于改进方位向波束形成算法相比,有更优良的性能,并有更小的运算量。     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

The assembly line worker assignment and balancing problem with stochastic worker availability

Assembly lines can be employed successfully in sheltered work centres to better include persons with disabilities in the labour market as well as to improve production efficiency. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). These assembly lines are characterised not only by a heterogeneous workforce, but also by high levels of absenteeism, which makes it more difficult to obtain stable and efficient line balancing solutions. In this paper, an extension of the ALWABP to minimise the expected cycle time under uncertain worker availability is proposed. We model this problem as a two-stage mixed integer program, and propose local search heuristics for solving it. Computational experiments show that stochastic modelling can help to improve the line’s efficiency and that the proposed heuristics produce good results for instances of practical size.     

一种新的大整数乘法算法

为解决超出计算机系统基本整数类型表达能力的整数(大整数)算术运算问题,以基础算法--大整数乘法为研究对象,根据大整数的表示形式与多项式表示形式上的相似性,结合大整数乘法进位与取模的特点,给出了一种关于大整数乘法的多项式算法.其方法与别的方法最大的不同是,虽然是求两个大整数乘法,但整个算法没有使用乘法,只是用加法运算而已...     

奇异值分解视频水印嵌入与检测及安全性测试

文章针对目前现有的基于小波变换和奇异值分解相结合的数字水印算法存在的缺陷,提出了一种新的视频水印算法:以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法,重点论述了奇异值分解的视频水印嵌入与检测以及安全性的测试.实验表明,提出的新算法具有较好的高透明性和鲁棒性.