A new iterative mutually coupled hybrid GA–PSO approach for curve fitting in manufacturing

Fitting data points to curves (usually referred to as curve reconstruction) is a major issue in computer-aided design/manufacturing (CAD/CAM). This problem appears recurrently in reverse engineering, where a set of (possibly massive and noisy) data points obtained by 3D laser scanning have to be fitted to a free-form parametric curve (typically a B-spline). Despite the large number of methods available to tackle this issue, the problem is still challenging and elusive. In fact, no satisfactory solution to the general problem has been achieved so far. In this paper we present a novel hybrid evolutionary approach (called IMCH-GAPSO) for B-spline curve reconstruction comprised of two classical bio-inspired techniques: genetic algorithms (GA) and particle swarm optimization (PSO), accounting for data parameterization and knot placement, respectively. In our setting, GA and PSO are mutually coupled in the sense that the output of one system is used as the input of the other and vice versa. This coupling is then repeated iteratively until a termination criterion (such as a prescribed error threshold or a fixed number of iterations) is attained. To evaluate the performance of our approach, it has been applied to several illustrative examples of data points from real-world applications in manufacturing. Our experimental results show that our approach performs very well, being able to reconstruct with very high accuracy extremely complicated shapes, unfeasible for reconstruction with current methods.     

基于Chambolle-Pock算法框架的高阶TV图像重建算法

传统的总变差（TV）最小算法是一种基于压缩感知(CS)的经典迭代重建算法，可以从稀疏数据或含噪数据中高精度地重建图像。然而，TV算法在重建分段常数特征不明显的图像时可能会引入块状伪影，通过研究得出，在图像去噪中使用高阶总变差（HOTV）能有效压制TV模型引入的块状伪影。鉴于此，提出了一种HOTV图像重建模型及其Chambolle-Pock（CP）求解算法。具体来说，以二阶梯度构建二阶TV范数，进而设计了一种数据保真约束的二阶TV最小重建模型，并推导出了相应的CP算法。在理想数据投影和含噪数据投影条件下，分别采用基于波浪背景的Shepp-Logan模体、灰度渐变模体以及真实CT图像模体进行重建实验，并进行定性和定量分析。理想数据投影的重建结果表明，和传统TV算法相比，HOTV算法能有效压制块状伪影并提高重建精度。含噪数据投影的重建结果表明，HOTV算法和TV算法均有良好的抗噪能力，但HOTV算法的保边性能更好且抗噪性更强。在重建分段常数特征不明显而灰度波动特征明显的图像时，HOTV算法是一种比TV算法更优的重建算法。所提HOTV算法可以被推广到各种扫描模式下的CT重建及其他成像模态中。     

CT有限角度算法的比较研究

在医学临床中,C形臂X射线机的应用愈来愈广泛。由于C臂机自身的特点,且所检测物体的密度及形状存在较大差异,使得其在有限角度下所获得的投影图像断截,投影数据不完全。本文主要分析在有限角度下CT图像最优准则重建的算法。通过对迭代重建算法ART、SART以及TV-SART的比较,得知在有限角为90°的情况下,TV-SART的重建图像更为清晰,更接近原始图像,从而为基于C形臂CT的成像系统的实现提供可靠的理论支撑。     

基于Spark的CT图像FBP重建算法程序并行设计

将常用于CT图像重建的滤波反投影算法程序设计成能够运行在大数据框架Spark中的并行模式,以此来提高计算效率并实现批量图像的重建,缩短图像重建时间。基于分布式计算框架Spark,利用其图像处理工具Thunder,将滤波反投影算法在图像重建过程中设计成并行程序模式,实现图像的片间并行重建。实验结果表明,随着Spark集群规模的不断扩大,在确保重建图像质量的前提下,重建一定数量的CT图像相比单机模式下时间显著缩短,并行滤波反投影算法具有完全加速比,并行效率趋近于1。基于Spark集群实现的滤波反投影算法能够显著提升CT图像重建速度,并实现大量图像并行重建,可扩展其他的CT图像重建算法,对远程医学图像重建平台的建设具有重要参考意义。     

A geometric partitioning method for distributed tomographic reconstruction

Tomography is a powerful technique for 3D imaging of the interior of an object. With the growing sizes of typical tomographic data sets, the computational requirements for algorithms in tomography are rapidly increasing. Parallel and distributed-memory methods for tomographic reconstruction are therefore becoming increasingly common. An underexposed aspect is the effect of the data distribution on the performance of distributed-memory reconstruction algorithms. In this work, we introduce a geometric partitioning method, which takes into account the acquisition geometry and aims to minimize the necessary communication between nodes for distributed-memory forward projection and back projection operations. These operations are crucial subroutines for an important class of reconstruction methods. We show that the choice of data distribution has a significant impact on the runtime of these methods. With our novel partitioning method we reduce the communication volume drastically compared to straightforward distributions, by up to 90% for a number of cases, and furthermore we guarantee a specified load balance.     

Evolving cellular automata for location management in mobile computing networks

Location management is a very important and complex problem in mobile computing. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. The paper investigates the use of cellular automata (CA) combined with genetic algorithms to create an evolving parallel reporting cells planning algorithm. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such an evolving CA system, cells in the network are mapped to cellular units of the CA and neighborhoods for the CA is selected. GA is then used to discover efficient CA transition rules. The effectiveness of the GA and of the discovered CA rules is shown for a number of test problems.     

鲁棒的单类协同排序算法

单类协同过滤(One-class collaborative filtering, OCCF)问题是当前的一大研究热点.之前的研究所提出的算法对噪声数据很敏感,因为训练数据中的噪声数据将给训练过程带来巨大影响,从而导致算法的不准确性.文中引入了Sigmoid成对损失函数和Fidelity成对损失函数,这两个函数具有很好的灵活性,能够和当前最流行的基于矩阵分解(Matrix factorization, MF)的协同过滤算法和基于最近邻(K-nearest neighbor, KNN)的协同过滤算法很好地融合在一起,进而提出了两个鲁棒的单类协同排序算法,解决了之前此类算法对噪声数据的敏感性问题.基于Bootstrap抽样的随机梯度下降法用于优化学习过程.在包含有大量噪声数据点的实际数据集上实验验证,本文提出的算法在各个评价指标下均优于当前最新的单类协同排序算法.     

Simulation optimisation using a genetic algorithm

The paper presents an attempt to apply genetic algorithms (GAs) to the problem of optimising an existing simulation model. A simple real-coded GA is presented and used to change the simulation model parameters. With each new parameter set proposed, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed that measures the quality of these parameters. Because of its nature and the stochastic and unpredictable behaviour of the complex simulation model, the goal function used leads to a highly non-linear, noisy and mixed (discrete and continuous) programming optimisation problem. A GA successfully works on it, and as a result gives a parameter set that measures substantially better than the nominal one. This demonstrates the capability of GAs to solve hard inverse problems even in the area of complex simulation model optimisation.     

A (/spl mu/ + /spl lambda/) evolutionary algorithm for reconstruction of SPECT data

Algorithms used to reconstruct single photon emission computed tomography (SPECT) data are based on one of two principles: filtered back projection or iterative methods. In this paper, an evolution strategy (ES) was applied to reconstruct transaxial slices of SPECT data. Evolutionary algorithms are stochastic global search methods that have been used successfully for many kinds of optimization problems. The newly developed reconstruction algorithm consisting of /spl mu/ parents and /spl lambda/ children uses a random principle to readjust the voxel values, whereas other iterative reconstruction methods use the difference between measured and simulated projection data. The (/spl mu/ + /spl lambda/)-ES was validated against a test image, a heart, and a Jaszczak phantom. The resulting transaxial slices show an improvement in image quality, in comparison to both the filtered back projection method and a standard iterative reconstruction algorithm.     

A comparison of three artificial life techniques for reporting cell planning in mobile computing

Location management is a very important and complex problem in today's mobile computing environments. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper compares several well-known artificial life techniques to gauge their suitability for solving location management problems. Due to their popularity and robustness, a genetic algorithm (GA), tabu search (TS), and ant colony algorithm (ACA) are used to solve the reporting cells planning problem. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such a planner, a GA, TS, as well as several different AC algorithms are implemented. The effectiveness of each algorithm is shown for a number of test problems.     

An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models

The use of genetic algorithms (GA) for optimization problems offers an alternative approach to the traditional solution methods. GA follow the concept of solution evolution, by stochastically developing generations of solution populations using a given fitness statistic, for example the achievement function in goal programs. They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Owing to the probabilistic development of populations, GA do not distinguish solutions, e.g. local optima from other solutions, and therefore cannot guarantee optimality even though a global optimum may be reached. In this paper, a non-linear goal program of the North Sea demersal fisheries is used to develop a genetic algorithm for optimization. Comparisons between the GA approach and traditional solution methods are made, in order to measure the relative effectiveness. General observations of the use of GA in multi-objective fisheries bioeconomic models, and other similar models, are discussed.     

A hybrid computational strategy for identification of structural parameters

By identifying parameters such as stiffness values of a structural system, the numerical model can be updated to give more accurate response prediction or to monitor the state of the structure. Considerable progress has been made in this subject area, but most research works have considered only small systems. A major challenge lies in obtaining good identification results for systems with many unknown parameters. In this study, a non-classical approach is adopted involving the use of genetic algorithms (GA). Nevertheless, direct application of GA does not necessarily work, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. A hybrid computational strategy is thus proposed, combining GA with a compatible local search operator. Two hybrid methods are formulated and illustrated by numerical simulation studies to perform significantly better than the GA method without local search. A fairly large structural system with 52 unknown parameters is identified with good results, taking into consideration the effects of incomplete measurement and noisy data.     

Scattered data interpolation of Radon data

Linear combinations of translates of a given basis function have long been successfully used to solve scattered data interpolation and approximation problems. We demonstrate how the classical basis function approach can be transferred to the projective space ℙ ^d−1. To be precise, we use concepts from harmonic analysis to identify positive definite and strictly positive definite zonal functions on ℙ ^d−1. These can then be applied to solve problems arising in tomography since the data given there consists of integrals over lines. Here, enhancing known reconstruction techniques with the use of a scattered data interpolant in the “space of lines”, naturally leads to reconstruction algorithms well suited to limited angle and limited range tomography. In the medical setting algorithms for such incomplete data problems are desirable as using them can limit radiation dosage.     

Iterative improvement to solve the parcel hub scheduling problem

This paper presents iterative improvement algorithms to solve the parcel hub scheduling problem (PHSP). The PHSP is combinatorial optimization problem that consists of scheduling a set of inbound trailers to a small number of unload docks. At the unload docks, the inbound trailers must be unloaded and the parcel sorted and loaded onto outbound trailers. Because the transfer operation is labor intensive, the transfer of parcels must be done in such a way as to minimize the timespan of the transfer operation. Local search (LS) and simulated annealing (SA) algorithms are developed and evaluated to solve the problem. The performances of the algorithms are compared to the performance of an existing genetic algorithm (GA). The computational results show that the LS and SA algorithms offer solutions that are superior to those offered by the GA.     

Genetic Algorithms in Noisy Environments

Genetic algorithms are adaptive search techniques which have been used to learn high-performance knowledge structures in reactive environments that provide information in the form of payoff. In general, payoff can be viewed as a noisy function of the structure being evaluated, and the learning task can be viewed as an optimization problem in a noisy environment. Previous studies have shown that genetic algorithms can perform effectively in the presence of noise. This work explores in detail the tradeoffs between the amount of effort spent on evaluating each structure and the number of structures evaluated during a given iteration of the genetic algorithm. Theoretical analysis shows that, in some cases, more efficient search results from less accurate evaluations. Further evidence is provided by a case study in which genetic algorithms are used to obtain good registrations of digital images.     

基于自适应权值的点云三维物体重建算法研究

基于三维扫描点云数据的三维物体重建是计算机图形学中非常重要的课题,在计 算机动画、医学图像处理等多方面都有应用。其中基于最小二乘问题的Levenberg-Marquart 算 法和基于极大似然估计的M-Estimator 算法都是不错的方案。但是当点的数量过多过少或者点 云中有噪声时,这些方案产生的结果都会有较大的误差,影响重建的效果。为了解决这两个问 题,结合Levenberg-Marquart 算法和M-Estimator 算法,提出了一种新的算法。该算法结合 Levenberg-Marquart 算法较快的收敛性和M-Estimator 算法的抗噪性,能很好地解决点数量较多 和噪声点影响结果的问题。通过在M-Estimator 的权重函数上进行改进,提出自适应的权值函 数,用灵活变动和自适应的值代替原来的固定值,使算法在噪声等级较高时也能表现良好。最 后将算法应用在球体和圆柱上,并和最新的研究成果进行对比,数据说明算法无论是在点云数 量较多还是在噪声等级较高的情况下都明显优于其他已知算法。     

A case-based classifier for hypertension detection

The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.     

Higher-order nonlinear priors for surface reconstruction

For surface reconstruction problems with noisy and incomplete range data, a Bayesian estimation approach can improve the overall quality of the surfaces. The Bayesian approach to surface estimation relies on a likelihood term, which ties the surface estimate to the input data, and the prior, which ensures surface smoothness or continuity. This paper introduces a new high-order, nonlinear prior for surface reconstruction. The proposed prior can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion. An exact solution would require solving a fourth-order partial differential equation (PDE), which can be difficult with conventional numerical techniques. Our approach is to solve a cascade system of two second-order PDEs, which resembles the original fourth-order system. This strategy is based on the observation that the generalization of image processing to surfaces entails filtering the surface normals. We solve one PDE for processing the normals and one for refitting the surface to the normals. Furthermore, we implement the associated surface deformations using level sets. Hence, the algorithm can accommodate very complex shapes with arbitrary and changing topologies. This paper gives the mathematical formulation and describes the numerical algorithms. We also show results using range and medical data.     

Adding learning to cellular genetic algorithms for trainingrecurrent neural networks

This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA) for training recurrent neural nets (RNN). Each RNN weight is encoded as a floating point number, and a concatenation of numbers forms a chromosome. Reproduction takes place locally in a square grid, each grid point representing a chromosome. Lamarckian and Baldwinian (1896) mechanisms for combining cellular GA and learning are compared. Different hill-climbing algorithms are incorporated into the cellular GA. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. RTRL has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, the simplest form of learning, has been implemented by considering the RNN as feedforward networks. The hybrid algorithms are used to train the RNN to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA is the fastest method. Learning should not be too extensive.