Novel streak-resolving algorithm for particle streak velocimetry

In this paper, a novel streak-resolving algorithm is proposed for particle streak velocimetry (PSV) to resolve velocity fields for a single image frame with multiple particle streaks. A model streak function, which was based on the temporal integral of the particle image intensity moving along its trajectory during the exposure period, was approximated using a multivariable least-square fit procedure to reconstruct the streak information and the corresponding particle trajectory. Lagrangian tracking was achieved, and the velocities were evaluated by differentiating the resolved particle trajectory with respect to time. Two types of flows, accelerating parallel flow and the rotational flow of Hill’s vortex, were used to generate synthetic streaks for the performance tests. Three types of relative error were defined and used to evaluate the performance of the algorithm in terms of statistical mean and standard deviation (SD) errors. The accuracy of the fitted streak parameters, such as particle image intensity and diameter, were also evaluated and compared. The results reveal that the error and SD were low if the image noise is below 1.0%; for noise levels of 5.0%, the error was up to 10% with an SD of up to 12%. The error and SD of the particle image intensity and particle image diameter for both flow types were also 0%–7% for clean and up to 12% for noisy images. The processing results for experimental streak images of flow past a cylinder reveal that these images can be resolved using the proposed algorithm with a residual mean error of 4.38 and an SD of 9.48. These results suggest that the proposed novel approach can be used to resolve velocity fields with only a single image frame and without expensive hardware for high-speed imaging and thus is suitable for diverse applications.     

基于蚁群算法的车间配送路径优化的仿真研究

介绍了蚁群算法的基本原理及算法的实现,并用蚁群算法来解决车间配送系统中的路径优化问题.通过VC6.0进行实例仿真,取得了很好的搜索效果,表明该方法能有效的发现最优解.     

An efficient ant colony optimization system for the manufacturing cells formation problem

An ant colony optimization (ACO) scheme for the manufacturing cells design problem is proposed, which uses a tight eigenvalue-based bound to guide and accelerate the search. This feature is combined with a good initialization procedure and with ideas from successful ACO implementations in other areas, to achieve efficiency and reliability with the minimum structure and set of parameters. The resulting algorithm produces most promising results for medium to large size problems, with negligible computational effort.     

Improving the Delaunay tessellation particle tracking algorithm in the three-dimensional field

Particle tracking velocimetry (PTV) algorithms match particles across two consecutive frames corresponding to a certain flow pattern. PTV based on Delaunay tessellation (DT-PTV) has a succinct structure and depends minimally on algorithmic assumptions. This study proposes several methods for improving DT-PTV performance in the three-dimensional field. The improved version, called 3DT-PTV, is tested using synthetic flows with various parameters and under difficult circumstances. Results show that 3DT-PTV performs better than the classical version in addressing flows with a noticeable ratio of particles without match and those with an excessive ratio of the inter-frame particle displacement to the inter-particle distance.     

Industrial applications of the ant colony optimization algorithm

The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to be found and is useful in industrial environments where computational resources and time are limited. A hybridization using iterated local search (ILS) is made in this work to the existing heuristic to refine the optimality of the solution. Applications of the ACO algorithm also involve numerous traveling salesperson problem (TSP) instances and benchmark job shop scheduling problems (JSSPs), where the latter employs a simplified ant graph-construction model to minimize the number of edges for which pheromone update should occur, so as to reduce the spatial complexity in problem computation.     

基于自适应蚁群算法的矩形件排样优化

蚁群算法是一种新型的进化算法,但它与其他进化算法一样也存在容易陷入局部搜索空间等缺陷。将蚁群算法引入到矩形零件的排样优化问题中,根据待排矩形零件面积和长宽比对每个蚂蚁节点赋予一定的初始信息量,同时对基本蚁群系统中的状态转移概率与信息素挥发因子做了适当改进,使其拥有自适应调节作用,从而有效的避免了蚂蚁长时间陷入局部搜索空间,使算法能在较短的时间内收敛到全局最优或近似最优解。最后两个实例表明,此算法用于求解矩形件排样问题是有效的。     

A cone-beam compensated back-projection algorithm for X-ray particle tracking velocimetry

Characterizing multiphase or granular flows is difficult due to the opaque nature of the system. While invasive measurement techniques provide detailed information about a single point, assessing the entire system is a laborious task due to the large number of samples required. Therefore, significant work has gone into developing noninvasive methods of measuring these flow systems. In this study, identical pairs of X-ray source/detector systems are used to provide two simultaneous but independent X-ray radiographic projections, which are then coupled together to perform X-ray stereographic imaging of a granular flow. A cone-beam compensated back-projection algorithm is developed for X-ray particle tracking velocimetry (XPTV). This method accurately corrects for the X-ray׳s cone-beam geometry, which is ignored in parallel-beam back-projection methods. To demonstrate the need for the cone-beam compensation, a direct comparison between the cone-beam and parallel-beam back-projection algorithms is used, and significant differences are presented. These methods are then used to perform XPTV in a double screw mixer, allowing the position and velocity of individual tracer particles to be characterized.     

基于蚁群算法的电控助力转向系统的集成优化设计

介绍了蚁群算法的基本知识,将蚁群算法引入电控助力转向系统(EPS)中,对EPS结构参数和控制参数进行集成优化,并用MATLAB进行了仿真.结果表明采用集成优化系统的转向轻便性和灵敏性都有了提高,改善了助力特性,从而使设计的结果达到了全局最优.     

基于蚁群算法的选择装配

选择装配是一种由低加工精度零件获得高精度装配件的方法,可归纳为一个组合优化问题,蚁群算法是解决这类问题的有效方法.综合考虑选择装配中的匹配率和匹配精度,提出以综合装配质量指标为选择装配的目标函数.为了求解选择装配的组合优化问题,在蚁群算法的框架内提出一个考虑信息素分布为节点模式的蚁群算法解构造图模型,并详细讨论蚁群算法的实现过程.通过对实例的仿真计算,考证该方法的实效性.     

基于自适应蚁群优化算法的数据挖掘模型研究

在Ant-Miner算法框架中通过对启发式因子、信息素更新及路径选择概率等策略的改进,提出基于自适应蚁群优化算法的分类挖掘模型,并进行实验分析,与基本Ant-Miner算法相比较,表明这一算法在规则质量及运行时间上都有很大的优势.     

基于蚁群算法的供应链滚动优化决策方法

针对钢铁供应链最终客户的需求不确定性,提出了一种将蚁群算法与滚动优化算法相结合的供应链优化决策方法,旨在满足最终客户不确定性需求的同时降低成本。通过实施滚动优化策略,来减少需求不确定性导致的决策失误。在优化模型中考虑了采购规模和生产规模对单位成本的非线性影响。为求解滚动优化中的非线性优化问题,通过将成本等效为路径的长度,将决策变量的候选解等效为城市,从而把决策优化问题转化为蚁群路径寻优问题。在每次静态优化中,优化算法根据历史数据和反馈信息来确定优化决策变量。针对一个包括供应商、生产商、零售商和最终客户的供应链对象进行了仿真研究,结果表明了所提方法在克服需求不确定和模型非线性方面的有效性。     

Concept optimization for mechanical product by using ant colony system

The aim of conceptual design is to generate the best design candidate. Concept solving in conceptual design can be viewed as a problem of combinatorial optimization, in which there exists a “combinational explosion” phenomenon when using the traditional morphological matrix method to tackle it. In this research, a concept optimization problem is studied based on an Ant Colony System (ACS). By analyzing the similarity between concept solving and Traveling Salesman Problem (TSP), concept solving is transformed into a problem of optimal path in combinatorial optimization, where the dynamic programming based solution space model and the longest path based optimization model are developed. Then, the ant algorithm to resolve TSP is adopted to implement concept optimization according to the positive feedback searching mechanism of ACS, and some improvements are made incorporating crossover and mutation operators of a genetic algorithm (GA), to obtain the optimal scheme rapidly and effectively. Finally, a conceptual design case of press is given to demonstrate the feasibility and rationality of this proposed approach. The employment of ACS enables concept solving to be implemented with an algorithm and thus possesses better operability, which offers a promising way to solve the “combinatorial explosion” problem in conceptual design.     

Minimizing makespan during environmental stress screening using a genetic algorithm and an ant colony optimization

The environmental stress screening (ESS) chamber employed in a typical electronics manufacturing service (EMS) provider’s facility is used for screening products, attempting to expose defects that cannot be detected by visual inspection or electrical testing. The ESS chambers are bottleneck work centers in most of the EMS facilities. This research uses a genetic algorithm (GA) and an ant colony optimization (ACO) to solve the job-scheduling problem at the ESS chamber where the makespan of the process is minimized. The performances of the two approaches were compared against some of the first-fit (FF) techniques from the literature. Both the GA and ACO techniques produced satisfactory results up to a job size of 40. Furthermore, ACO generally requires a longer computational time, while offering more consistent solution quality.     

基于Pareto蚁群算法的拆卸线平衡多目标优化

为提高产晶拆卸效率,针对拆卸线平衡问题建立了数学模型.该模型以最小拆卸线闲置率、负荷均衡和最小拆卸成本为优化目标.结合拆卸线平衡问题的具体特点,提出了一种改进的基于Pareto解集的多目标蚁群优化算法.算法采用小生境技术,引导蚂蚁搜索到分布良好的Pareto最优解集,并以被支配度和分散度为个体评价规则.实验测试结果表明了该算法的可行性.最后,结合企业生产实际,给出了所提模型与算法的具体应用.     

Using ant colony optimization to solve hybrid flow shop scheduling problems

In recent years, most researchers have focused on methods which mimic natural processes in problem solving. These methods are most commonly termed “nature-inspired” methods. Ant colony optimization (ACO) is a new and encouraging group of these algorithms. The ant system (AS) is the first algorithm of ACO. In this study, an improved ACO method is used to solve hybrid flow shop (HFS) problems. The n-job and k-stage HFS problem is one of the general production scheduling problems. HFS problems are NP-hard when the objective is to minimize the makespan [1]. This research deals with the criterion of makespan minimization for HFS scheduling problems. The operating parameters of AS have an important role on the quality of the solution. In order to achieve better results, a parameter optimization study is conducted in this paper. The improved ACO method is tested with benchmark problems. The test problems are the same as those used by Carlier and Neron (RAIRO-RO 34(1):1–25, 2000), Neron et al. (Omega 29(6):501–511, 2001), and Engin and Döyen (Future Gener Comput Syst 20(6):1083–1095, 2004). At the end of this study, there will be a comparison of the performance of the proposed method presented in this paper and the branch and bound (B&;B) method presented by Neron et al. (Omega 29(6):501–511, 2001). The results show that the improved ACO method is an effective and efficient method for solving HFS problems.     

Multi-heuristic desirability ant colony system heuristic for non-permutation flowshop scheduling problems

Ant colony optimization (ACO) is a novel intelligent meta-heuristic originating from the foraging behavior of ants. An efficient heuristic of ACO is the ant colony system (ACS). This study presents a multi-heuristic desirability ACS heuristic for the non-permutation flowshop scheduling problem, and verifies the effectiveness of the proposed heuristic by performing computational experiments on a well-known non-permutation flowshop benchmark problem set. Over three-quarters of the solutions to these experiments are superior to the current best solutions in relevant literature. Since the proposed heuristic is comprehensible and effective, this study successfully explores the excellent potential of ACO for solving non-permutation flowshop scheduling problems.     

基于自适应蚁群算法的协同制造项目资源优化配置

为探索更高效的协同制造资源优化配置算法,提出了基于自适应蚁群算法的求解方法。针对零件的特定工艺线路,建立了以时间、成本和质量为目标的制造资源优化配置模型,设计了模型求解的自适应蚁群算法,并给出具体算例,验证了算法的可行性和有效性。     

一种用于优化PID参数的改进蚁群算法

PID控制由于算法简单,鲁棒性好在工业的过程控制中应用很广泛,但是传统的Z-N算法整定的PID参数却并不是最佳的。蚁群算法作为一种新型的仿生优化算法。该算法具有很多优良的性质,近年来在优化领域中得到了广泛应用。本文提出了一种改进的蚁群算法。通过增加高斯变异这一环节来增强算法的智能性。最后通过仿真实验获得了较好的控制效果。     

一种面向室内 AGV 路径规划的改进蚁群算法

针对传统蚁群算法在大规模和复杂环境中,全局搜索效率差,收敛速度慢,路径转弯次数过多且不够平滑等问题,本文提出一种改进蚁群算法。该方法通过动态更新不同等级蚂蚁路径上的信息素,加快算法的收敛速度;通过引入距离函数和方向函数作为启发因子,改善路径搜索质量;采用一种改进自适应伪随机转移策略,减小陷入局部最优解的概率;在最优路径的基础上引入三次均匀B样条曲线进行优化,提高路径的平滑性。通过在2种不同规模环境下的路径规划实验表明,本文算法相比传统算法在分别减少55.6%和59.4%转弯次数的基础上,提升87.5%和100%的收敛速度,验证了本文算法的优越性。最后,以QBot2e为平台,将本算法应用到室内自动导引车(AGV)路径规划中,进一步验证了算法的实用性。