A multi-objective optimization and decision algorithm for locator layout continuous searching in checking fixture design

Locator layout is a key concern in checking fixture design (CFD) to hold a checked part accurately and stably. Since the localization accuracy evaluation only considered the positions and directions of locators, ignoring the whole layout of them which is related with localization stability, in this paper, a multi-objective continuous searching optimization and decision algorithm is proposed to obtain an accurate, stable, and detachable locator layout for CFD. The algorithm searches a trade-off between two goals, location accuracy and stability, and constrains the layout with the 3-2-1 location principle to assure the detachability. In order to remove the discretization error in traditional optimization methods, the algorithm searches locator points on the surfaces of the checked part model continuously instead of the discretized points. At last, the algorithm is realized and embedded in Siemens NX by application program interface and two case studies are presented to verify its feasibility and practicability.     

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。     

基于改进蚁群算法的PID参数优化

一般传统PID控制器参数优化都采用Ziegler-Nichols法,由于其无法获得满意的动态指标,因此本文提出一种基于改进蚁群算法的PID控制器参数优化方法。蚁群算法是一种仿生进化算法,其采用分布计算机制,具有较强鲁棒性。本文提出的改进蚁群算法,可自适应调整路经上的信息素,并将各路径上的信息素强度限制在某个区域内,以避免搜索停滞。仿真实验表明该方案可行。     

Identification of fuzzy systems using a continuous ant colony algorithm

A continuous ant colony algorithm is first proposed to identify fuzzy systems. It is used to construct five fuzzy systems to approximate nonlinear functions of one, two, and three variables. The effect of algorithm parameters on the approximation error is studied. A comparative analysis with other identification algorithms showed an advantage of the proposed algorithm.     

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.     

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。     

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.     

基于蚁群算法的工艺路线生成及优化

针对计算机辅助工艺规划中的工艺路线的决策问题,提出了一种基于蚁群算法的工艺路线生成及优化算法.该算法将被加工零件划分为若干特征元,并由各个特征元的加工链得到该零件的加工元;根据加工元的属性,用加权海明距离表示加工元之间的相似度;根据加工元之间的约束条件,确定其优先加工关系并得到各自的前趋加工元;对基本蚁群算法进行改进,在禁忌准则和约束条件的限制下对解空间进行搜索,从而得到优化的零件加工工艺路线.通过实例验证了该算法的可行性和有效性.     

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。     

基于蚁群算法的单克隆菌落挑选仪路径规划

单克隆菌落挑选仪是集光学成像、图像识别和自动控制等技术于一身,应用于生物工程领域的一种高端仪器。对12×8阵列挑选针和无序排列的菌落目标,只有对挑选路径和顺序进行优化,才能有效提高挑选通量。针对这一需求,利用蚁群算法的基本原理,对单克隆菌落挑选仪挑选路径进行了优化。仿真实验结果表明,该算法可以有效提高挑选效率。     

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.     

Deformation control through fixture layout design and clamping force optimization

Workpiece deformation must be controlled in the numerical control machining process. Fixture layout and clamping force are two main aspects that influence the degree and distribution of machining deformation. In this paper, a multi-objective model was established to reduce the degree of deformation and to increase the distributing uniformity of deformation. The finite element method was employed to analyze the deformation. A genetic algorithm was developed to solve the optimization model. Finally, an example illustrated that a satisfactory result was obtained, which is far superior to the experiential one. The multi-objective model can reduce the machining deformation effectively and improve the distribution condition.     

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.     

Multi-objective optimization in single-row layout design using a genetic algorithm

This paper presents the development of a genetic algorithm for determining a common linear machine sequence for multi-products with different operation sequences and facilities with a limited number of duplicate machine types available for a job. This work aims to minimize the total flow distance traveled by products, reduce the number of machines arranged in the final linear sequence, and decrease the total investment cost of the machines used in the final sequence. We assume that product flow runs only in the forward direction, either via in-sequence or bypass movement. We demonstrate the effectiveness of the proposed algorithm by solving a typical layout design problem taken from literature, and several randomly generated problems. Results indicate that the proposed algorithm serves as a practical decision support tool for resolving layout problems in manufacturing facilities.     

Recent developments on machining fixture layout design, analysis, and optimization using finite element method and evolutionary techniques

A review of the recent development of the machining fixture configuration/layout is presented in this paper. The literature review is mainly focused on the recently developed optimal fixture configuration under the dynamic conditions of the workpiece. In this review paper, the fixture design, fixture analysis, fixture synthesis, fixture layout design, optimization of fixture layout design, various optimization algorithms, and case studies of two- and three-dimensional workpiece geometries under dynamic conditions have been emphasized specially. The further scopes of the research are finally summarized.     

基于自适应蚁群算法的盘式制动器优化设计

以某MPV车型的制动盘为研究对象,以增大该制动盘的制动力矩为主要优化目标,同时兼顾降低制动盘厚度和控制温升,建立了该制动盘的数学模型,并运用自适应蚁群算法对该数学模型进行了优化计算.优化结果表明,该优化方案不仅增大了该制动盘的制动力矩,同时还降低了制动盘的厚度,且制动盘温升不大.     

多种群蚁群算法解机组组合优化

电力系统机组组合问题是一个大规模混合整数规划问题,具有高维、离散、非线性等特点,在数学上被称为NP-hard问题。为解决蚁群算法在解决机组组合问题中遇到的计算速度慢、易陷入局部最优等问题,将多种群蚁群算法应用到解决机组组合的问题中。开展了多种群蚁群算法在机组组合问题中的应用分析,新建了除搜索蚁之外的侦察蚁和工蚁,设定了3种蚁群之间的信息交互原理,提出了各蚁群的信息素更新方法。在修正后的IEEE30节点系统对算法可行性作了验证,并对算法的合理性和有效性进行了分析。研究结果表明,所提出的多种群蚁群算法是合理、有效的。     

大规模定制板材排样的多种群蚁群优化算法

为解决定制家具企业的大规模多品种小批量的矩形件排样问题,针对其一刀切的特殊生产工艺要求,通过对待切板自动生成两个不同的编号:横切编号和纵切编号的方法,采用面积,长度,宽度来启发的多种群蚁群算法,避免了单因素启发的不合理布局,做到了"生成即可行",经多个企业的实际使用后,发现与单因素蚁群启发相比,多种群算法寻优能力强,主要表现为:最大利用率最高;多次寻优的最大利用率变化区间小.