一种求解车间作业调度问题的混合微粒群算法

在对车间作业调度问题优化目标与约束条件分析的基础上，以最小化机器完工时间为目标构建优化模型。通过将遗传算法和模拟退火算法的优点与标准微粒群算法相结合，提出一种求解该问题的混合微粒群算法，并结合实例验证了该算法的有效性与收敛性。     

基于改进混合作用力微粒群算法的液压阀块加工车间调度优化

针对微粒群算法作用力规则的不足，提出改进混合作用力微粒群(IHFPSO)算法。采用阶段性搜索策略，将算法的搜索过程分为前期和后期2个搜索阶段：在前期搜索阶段，微粒在其他微粒的引斥力作用下进行最优搜索，以保持种群多样性；在后期搜索阶段，微粒在双引力及引力提供的加速度的共同作用下向最优解收敛，以提高局部搜索能力。将所提出的IHFPSO算法应用于液压阀块加工车间调度问题，利用矩阵变量来处理约束条件，给出一种基于矩阵的微粒编码、解码方法。通过液压阀块加工车间调度优化实例，将IHFPSO算法与微粒群算法、中值导向微粒群算法、扩展微粒群算法、多作用力微粒群算法进行对比，验证提出的IHFPSO算法结果最优，实现液压阀块加工车间调度优化。     

求解液压阀块加工车间调度的多作用力微粒群算法

为有效地解决液压阀块加工车间调度问题,考虑工序间和机器间的约束关系,以最大完成时间最小为目标,给出了液压阀块加工车间调度优化模型。为平衡算法的全局和局部搜索能力,提出了多作用力微粒群(MFPSO)算法,采用多作用力阶段性搜索策略,将搜索过程划分为前期、中期、后期3个阶段,并对应构造单一斥力、平衡引斥力、单一引力3种作用力规则,在不同搜索阶段采用不同的作用力规则,提高了算法的搜索机制和寻优性能。将MFPSO算法用于求解液压阀块加工车间调度问题,利用矩阵变量来处理约束条件,给出了一种基于矩阵的微粒编码、解码方法。通过液压阀块加工车间调度优化实例,将MFPSO算法与微粒群算法、中值导向微粒群算法、扩展微粒群算法、蚁群算法进行了对比,结果表明,提出的MFPSO算法结果最优,从而验证了该算法的有效性。     

高维多目标优化设计的灰色微粒群算法

提出了一种基于混合离散变量多目标优化设计求解方法,该方法应用灰色相对关联度将多目标优化问题转化为单目标优化问题,再用改进微粒群算法求解.开发了混合离散变量优化的灰色改进微粒群法程序.该方法能合理地处理优化设计中混合离散变量的取值问题,通过对标准微粒群算法改进,引入动态罚函数,算法具有很强的全局寻优能力.优化设计实例表明,该算法对优化设计问题的特性无特殊要求,具有较好的普适性,而且程序运行可靠,全局收敛能力强.     

基于线搜索的微粒群算法在机械设计中的应用

为了避免微粒群优化算法早熟,利用算法不需求导的特点,提出了基于线性搜索的改进微粒群算法及实现过程,并把该算法应用于机械优化问题.实例表明,该方法全局收敛性好,收敛速度快且精度高,是解决工程设计中复杂线性优化问题的可行方法.     

基于微粒群算法的机械优化设计

介绍了微粒群优化算法的原理、改进及算法实现过程.用数值实验验证了该算法在解决复杂机械优化问题方面的可行性与有效性.     

基于Petri网和混合算法的作业车间优化

采用赋时变迁Petri网,建立了一种作业车间调度模型.通过为机器分配工序来消解因机器库所共享而引起的冲突,得到了表示调度方案的标志图,给出了一种生成可行调度标志图的方法.同时,提出了一种变迁激发序列编码的离散版粒子群算法,并将模拟退火算法嵌入到该粒子群算法中,以提高算法的优化性能.仿真结果验证了混合算法的可行性和有效性.     

萤火虫群优化算法在公差分配优化的应用

萤火虫群优化算法是一种新颖的群体智能优化算法,目前在优化领域中的应用比较少。分析了萤火虫算法的仿生原理和数学模型。提出了基于萤火虫群优化算法的公差分配优化方法。以公差分配的优化目标函数作为评价函数。在基本GSO算法的基础上,使用罚函数,把它加到目标函数中,不断地寻找更优可行解,逐渐达到搜索全局最优解。通过一个具体实例测试,并将萤火虫算法和微粒群算法相比较,仿真实验结果表明萤火虫群优化算法在公差分配优化问题中有效可行,且具有较高的计算效率。     

基于改进微粒群算法的模具多项目动态调度

针对模具多项目执行过程中任务拖期导致的调度计划变更,提出了一种启发式动态调度算法.利用改进的微粒群算法构建一个加权工期之和最小的初始调度计划,并基于关键链管理方法对初始调度计划进行合理地缓冲设置.建立了以调度计划变更费用最小为优化目标的启发式动态调度模型,并用改进的微粒群算法进行求解.通过仿真计算分析了算法的町行性与可靠性,并与标准的微粒群算法进行了比较.     

基于微粒群的约束优化

针对微粒群算法在处理约束优化问题时,难以兼顾约束与优化之间关系的问题,提出了一种泛学习微粒群算法(ULPSO),通过引入微粒不可行历史最优,使得微粒的学习更具多样性和有效性,增强了算法的搜索智能.通过对常用的13个基准函敷的测试对比分析,表明该算法求解约束优化问题的计算具有快速性、稳定性和有效性.     

基于禁忌搜索和遗传算法的云仓储分配优化

针对商家企业选择的云仓储分布过于密集,导致其服务覆盖范围有较多重叠的问题,提出了仓储间因缺货调货而产生的物流成本费用,以及商家企业的服务辐射范围两个关键因素.首先,以物流成本总费用最小为目标,建立云仓储分配与优化模型.然后,通过设计两种优化的禁忌搜索算法和遗传算法,对其进行求解.最后,与现有算法从收敛性、最优解以及云仓...     

Enhanced characterization of an accelerometer-based fall detection algorithm using a repository

Falling is a common accident that can lead to serious injury among the elderly. To reduce injuries resulting from falls, automatic detection allows immediate medical assistance. Hence, various fall detectors, including body sensors and smartphones, have been developed in recent decades. In our previous study, an accelerometer-based fall detector was designed with high accuracy for simulated falls performed by young volunteers. However, there are significant differences between the acceleration signals generated by simulated and real falls. Simulated devices do not accurately assess the sensitivity and specificity of falls. Hence, the goal of this study is to access the accuracy of our designed accelerometer-based fall detection algorithm using a real-world repository. The results showed that the algorithm accurately characterizes real falls. Differences between our approach and previously published algorithms are discussed. This study is expected to assist in the design of more effective practical fall detection algorithms.     

Modified A-star algorithm for modular plant land transportation

Many common path optimization algorithms are available. However, problems arise when a general route optimization algorithm is applied to land transportation of large cargo, such as a modular plant. The large and heavy structure of a modular plant can lead to a loss of time depending on the curve of the road. This problem is more critical when traveling through large turns, which may also cause mechanical problems. Therefore, curves are essential parameters for modular plant land transportation. In this research, we show the importance of angles in the path via multi-body dynamic simulations and finite element analysis. Based on these results, we constructed a pathfinding algorithm that considers the importance of angles. The traditional A-star algorithm considers only distance as a cost, whereas our modified A-star algorithm considers both distance and angle as costs. Our goal is to improve traditional A-star algorithms and optimize them for modular plant land transportation.     

Design, optimization, and deployment of a waterworks pumping station control system

This article presents a summary of the development and realization of a custom control and monitoring system for a water supply facility consisting of fixed-capacity intake pumps, a reservoir tank, and variable-speed outtake pumps. Project realization included the design and building of control hardware, as well as the design and deployment of the intake pump switching algorithm. Details of the control system design with an emphasis on the pump switching algorithm are given. The stages of the system development, including process modeling, design goal formulation, optimization of control algorithm using genetic algorithms, simulation, and implementation, are presented. Finally, deployment and real-life results are shown.     

Compression of fluorescence microscopy images based on the signal-to-noise estimation

Modern microscopic techniques like high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging may involve collection of thousands of images per experiment. Efficient image-compression techniques are indispensable to manage these vast amounts of data. This goal is frequently achieved using lossy compression algorithms such as JPEG and JPEG2000. However, these algorithms are optimized to preserve visual quality but not necessarily the integrity of the scientific data, which are often analyzed in an automated manner. Here, we propose three observer-independent compression algorithms, designed to preserve information contained in the images. These algorithms were constructed using signal-to-noise ratio (SNR) computed from a single image as a quality measure to establish which image components may be discarded. The compression efficiency was measured as a function of image brightness and SNR. The alterations introduced by compression in biological images were estimated using brightness histograms (earth's mover distance (EMD) algorithm) and textures (Haralick parameters). Furthermore, a microscope test pattern was used to assess the effect of compression on the effective resolution of microscope images.     

A unified localization approach for machining allowance optimization of complex curved surfaces

The goal of workpiece localization is of interest to find the optimal Euclidean transformation that aligns the sampled points to the nominal CAD model to ensure sufficient stock allowance during the machining process. In this paper, a unified localization technique is developed for sculptured surface machining. This technique concerns an alignment process to satisfy a user-defined set of constraints for some specific surfaces where the machining allowance is preferentially guaranteed. The mathematical model of the constrained optimization alignment is firstly established, and is efficiently solved by a combination of the multipliers method and the BFGS algorithm to handle the large number of constraints in allowance optimization. To efficiently calculate the Euclidean oriented distance, a novel approach, which combines the robust arithmetic for multivariate Bernstein-form polynomials and Bezier surface segmentation algorithm, is presented based on recursive quadtree decomposition. Two typical sculptured surfaces are used to test the developed algorithm and comparisons between the proposed algorithm and the existing algorithms are given. Experiment results show that the proposed method is appropriate and feasible to distribute the stock allowance for proper sculptured surface machining.     

遗传模拟退火混合算法在钣金自动排样中的研究

在钣金制造中,如何进行钣金件的排样,使得钣金达到最大的使用效率是目前机械设计工程迫切需要解决的方法。将人工智能领域中的遗传算法和模拟退火算法相结合,在前人排放算法的基础上,提出改进,利用遗传算法产生排样序列,利用模拟退火算法抑制遗传算法的早熟现象,进行排样处理,取得了理想的结果,实例测试表明,算法性能稳定。能为钣金排样系统的开发提供实用的、较优的排料算法。     

快速成型技术中分层算法的研究综述

作为快速成型技术中必不可少的环节,根据对零件制造精度和装配要求及效率的侧重不同,多年来多种分层算法已被国内外学者开发出来。在同等加工条件下,根据加工精度要求和层厚变化的不同,将分层算法大致分为等层厚分层算法和适应性分层算法两类。从常用的立体光刻(STL)模型、原始计算机辅助设计(CAD)模型和点云数据3种数据模型入手,简述了两类分层算法的研究和发展;介绍了采用斜边的分层算法、基于区域划分的混合算法、曲面分层算法等先进分层算法;讨论了分层算法中待解决的问题:直接分层算法的文件格式标准和轮廓的精确拟合等问题。最后,总结得出了分层算法未来的研究方向和趋势。     

Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms

In this paper, the algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with; the algorithm verifying the trace of the covariance matrix of the innovation sequence, the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence, the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence, and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft to detect sensor faults, and some suggestions are given on the use of the algorithms in flight control systems.     

基于模糊基函数网络和自适应最小二乘算法的外圆纵向磨削表面粗糙度的预测

建立了外圆纵向磨削表面粗糙度的模糊基函数网络（FBFN）预测模型，网络的训练采用自适应最小二乘算法（ALS）。ALS将最小二乘算法和遗传算法相结合，能够自主学习，不用人为干预，FBFN和粗糙度的分析模型相结合，只需少量实验数据便可完成网络的训练，自动产生模糊规则，确定隐含层的节点数。仿真和实验结果表明，FBFN网络结构非常适合粗糙度的预测和控制，采用ALS学习方法比BP算法、传统的遗传算法和正交二乘法等能产生更好的结果。