A hybrid method for inversion of 3D DC resistivity logging measurements

This paper focuses on the application of hp hierarchic genetic strategy (hp–HGS) for solution of a challenging problem, the inversion of 3D direct current (DC) resistivity logging measurements. The problem under consideration has been formulated as the global optimization one, for which the objective function (misfit between computed and reference data) exhibits multiple minima. In this paper, we consider the extension of the hp–HGS strategy, namely we couple the hp–HGS algorithm with a gradient based optimization method for a local search. Forward simulations are performed with a self-adaptive hp finite element method, hp–FEM. The computational cost of misfit evaluation by hp–FEM depends strongly on the assumed accuracy. This accuracy is adapted to the tree of populations generated by the hp–HGS algorithm, which makes the global phase significantly cheaper. Moreover, tree structure of demes as well as branch reduction and conditional sprouting mechanism reduces the number of expensive local searches up to the number of minima to be recognized. The common (direct and inverse) accuracy control, crucial for the hp–HGS efficiency, has been motivated by precise mathematical considerations. Numerical results demonstrate the suitability of the proposed method for the inversion of 3D DC resistivity logging measurements.     

A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy

Cell formation problem attempts to group machines and part families in dedicated manufacturing cells such that the number of voids and exceptional elements in cells are minimized. In this paper, we presented a linear fractional programming model with the objective of maximizing the grouping efficacy while the number of cells is unknown. To show the effectiveness of the proposed model, two test problems were applied. Then, to solve the model for real-sized applications, a hybrid meta-heuristic algorithm in which genetic algorithm and variable neighborhood search are combined. Using the grouping efficacy measure, we have also compared the performance of the proposed algorithm on a set of 35 test problems from the literature. The results show that the proposed GA-VNS method outperforms the state-of-the-art algorithms.     

Reconstruction of the perfusion coefficient from temperature measurements using the conjugate gradient method

We consider the inverse bio-heat transfer problem to determine the space- and time-dependent perfusion coefficient from temperature measurements. In this formulation, the problem is fully determined and the coefficient is identifiable if and only if the temperature has dense support. However, the problem is still ill-posed since small errors in the measured temperature cause large errors in the output perfusion coefficient due to the numerical differentiation of noisy data involved which represents an unstable procedure. In order to overcome this difficulty and restore stability, we employ for the first time the conjugate gradient method (CGM) for solving the inverse problem under investigation. Regularization is achieved by stopping the iteration process at an appropriate threshold dictated by the discrepancy principle. Numerical results show that the CGM is accurate and reasonably stable in retrieving the perfusion coefficient. Moreover, comparison with other methods shows improved efficiency and stability in inverting noisy data.     

脑电产生源三维空间分布的仿真研究

对于脑电信号源的分布模型，在基于加权广义逆矩阵的最小范数解的框架下，进行了源分布图象的计算机仿真计算，主要包括两部分内容：通过求解脑电正问题的边界元算法获取导联场矩阵；以脑电生理学上的特性，即源活动的高度协同性、聚集性及稀疏性为先验前提，借鉴LORETA算法中有关拉普拉斯平滑算子，以及FOCUSS算法中有关重加权的竞争机制等，通过分阶段地构造加权矩阵的方法逐步对逆解做出约束，实现具有较高分辨率的脑内各处电活动源的强度与方向的三维图象重建。计算机仿真研究结果证实了此方法的可行性。     

A hybrid steepest descent method for constrained convex optimization

This paper describes a hybrid steepest descent method to decrease over time any given convex cost function while keeping the optimization variables in any given convex set. The method takes advantage of the properties of hybrid systems to avoid the computation of projections or of a dual optimum. The convergence to a global optimum is analyzed using Lyapunov stability arguments. A discretized implementation and simulation results are presented and analyzed. This method is of practical interest to integrate real-time convex optimization into embedded controllers thanks to its implementation as a dynamical system, its simplicity, and its low computation cost.     

A hybrid method for performance analysis of G/G/m queueing networks

Open queueing networks are useful for the performance analysis of numerous real systems. Since exact results exist only for a limited class of networks, decomposition methods have been extensively used for approximate analysis of general networks. This procedure is based on several approximation steps. Successive approximations made in this approach can lead to a considerable error in the output. In particular, there are no general accurate formulas for computing the mean waiting time and the inter-departure variance in general multiple-server queues. This causes the results from decomposition methods when applied to G/G/m queueing networks to be very approximative and to significantly deviate from actual performance values. We suggest substituting some approximate formulae by low-cost simulation estimates in order to obtain more accurate results when benefiting from the speed of an analytical method. Numerical experiments are presented to show that the proposed approach provides improved performance.     

A rapid inversion and resolution analysis of magnetic microscope data by the subtractive optimally localized averages method

Modern scanning magnetic microscopes have the potential for fine-scale magnetic investigations of rocks. Observations at high spatial resolution produce large volumes of data, and the interpretation of these data is a nontrivial task. We have developed software using an efficient magnetic inversion technique that explicitly constructs the spatially localized Backus-Gilbert averaging kernel. Our approach, using the subtractive optimally localized averages (SOLA) method (Pijpers, R.P., Thompson, M.J., 1992. Faster formulations of the optimally localized averages method for helioseismic inversions. Astronomy and Astrophysics 262, L33-L36), yield a unidirectional magnetization. The averaging kernel expresses the spatial resolution of the inversion and is valuable for paleomagnetic application of the scanning magnetic microscope. Inversion examples for numerical magnetization patterns are provided to exhibit the performance of the method. Examples of actual magnetic field data collected from thin sections of natural rocks measured with a magnetoimpedance (MI) magnetic microscope are also provided. Numerical tests suggest that the data-independent averaging kernel is desirable for a point-to-point comparison among multiple data. Contamination by vector magnetization components can be estimated by the averaging kernel. We conclude that the SOLA method is a useful technique for paleomagnetic and rock magnetic investigations using scanning magnetic microscopy.     

A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids

This paper presents a hybrid classification method that utilizes genetic algorithms (GAs) and adaptive operations of ellipsoidal regions for multidimensional pattern classification problems with continuous features. The classification method fits a finite number of the ellipsoidal regions to data pattern by using hybrid GAs, the combination of local improvement procedures and GAs. The local improvement method adaptively expands, rotates, shrinks, and/or moves the ellipsoids while each ellipsoid is separately handled with a fitness value assigned during the GA operations. A set of significant features for the ellipsoids are automatically determined in the hybrid GA procedure by introducing “don’t care” bits to encode the chromosomes. The performance of the method is evaluated on well-known data sets and a real field classification problem originated from a deflection yoke production line. The evaluation results show that the proposed method can exert superior performance to other classification methods such as k nearest neighbor, decision trees, or neural networks. Ki K. Lee received the B.S. degree from Han Yang University, Seoul, Korea in 1994, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1996 and 2005, respectively. From 2001 to 2004, he was a senior research engineer in telecommunication systems laboratory of LG Electronics Inc. Since 2005, he has been an assistant professor in the School of Management at Inje University, Kimhae, Korea. His research interests include intelligent decision support systems, soft computing, and pattern recognition. Wan C. Yoon received the B.S. degree from Seoul National University, Korea in 1977, the M.S. degree from KAIST, Korea in 1979, and the Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology in 1987. He is professor of the Department of Industrial Engineering at KAIST, Korea. His research interests include application of artificial intelligence, human decision-making and aiding, information systems, and joint intelligent systems. Dong H. Baek received the B.S. degree from Han Yang University, Seoul, Korea in 1992, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1994 and 1999, respectively. He is an assistant professor in management information systems at department of business administration, Hanyang University, Korea. His research interests include management information systems, system engineering, and machine learning.     

A hybrid graph–neural method for domain decomposition

A hybrid method is presented for domain decomposition employing a graph theoretical algorithm and a neural network optimization model. The method uses graph theory and neural networks for decomposition of structured finite element meshes. Examples are presented to illustrate the efficiency of the developed method.     

A fuzzy clustering-based hybrid method for a multi-facility location problem

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, customers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facilities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The center of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to compare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.     

A hybrid solution method for fuzzy train formation planning

The train formation plan (TFP) determines the routing and frequency of trains, and assigns the demands to trains. In this paper, in order to consider the real-life condition of railways, a mathematical model with fuzzy costs is proposed for train formation planning in Iranian railway. In this fuzzy model, the costs are considered in three scenarios, namely optimistic, normal and pessimistic. The model is formulated based on the fixed-charge capacitated multicommodity network design problem. Since the TFP problem is NP-hard, an efficient hybrid algorithm combining local branching and relaxation induced neighborhood search methods is presented. A three-step method is applied for parameter tuning using design of experiments approach. To evaluate the efficiency and effectiveness of the proposed algorithm, the results are compared with those of the state-of-the-art optimization software.     

A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem

Job shop scheduling problem (JSP) which is widespread in the real-world production system is one of the most general and important problems in various scheduling problems. Nowadays, the effective method for JSP is a hot topic in research area of manufacturing system. JSP is a typical NP-hard combinatorial optimization problem and has a broad engineering application background. Due to the large and complicated solution space and process constraints, JSP is very difficult to find an optimal solution within a reasonable time even for small instances. In this paper, a hybrid particle swarm optimization algorithm (PSO) based on variable neighborhood search (VNS) has been proposed to solve this problem. In order to overcome the blind selection of neighborhood structures during the hybrid algorithm design, a new neighborhood structure evaluation method based on logistic model has been developed to guide the neighborhood structures selection. This method is utilized to evaluate the performance of different neighborhood structures. Then the neighborhood structures which have good performance are selected as the main neighborhood structures in VNS. Finally, a set of benchmark instances have been conducted to evaluate the performance of proposed hybrid algorithm and the comparisons among some other state-of-art reported algorithms are also presented. The experimental results show that the proposed hybrid algorithm has achieved good improvement on the optimization of JSP, which also verifies the effectiveness and efficiency of the proposed neighborhood structure evaluation method.     

Voxel-based three-view hybrid parallel network for 3D object classification

Three-dimensional models are widely used in the fields of multimedia, computer graphics, virtual reality, entertainment, design, and manufacturing because of the rich information that preserves the surface, color and texture of real objects. Therefore, effective 3D object classification technology has become an urgent need. Previous methods usually directly convert classic 2D convolution into 3D form and apply it to objects with binary voxel representation, which may lose internal information that is essential for recognition. In this paper, we propose a novel voxel-based three-view hybrid parallel network for 3D shape classification. This method first obtains the depth projection views of the three-dimensional model from the front view, the top view and the side view, so as to preserve the spatial information of the three-dimensional model to the greatest extent, and output its predicted probability value for the category of the three-dimensional model, and then combining the three-view parallel network with voxel sub-network performs weight fusion, and then uses Softmax for classification. We conducted a series of experiments to verify the design of the network and achieved competitive performance in the 3D object classification tasks of ModelNet10 and ModelNet40.     

An advanced hybrid method for the acoustic prediction

The present article proposes an advanced methodology for numerically simulating complex noise problems. More precisely, we consider the so-called multi-stage acoustic hybrid approach, which principle is to couple sound generation and acoustic propagation stages. Under that approach, we propose an advanced hybrid method which acoustic propagation stage relies on Computational AeroAcoustics (CAA) techniques. To this end, first, an innovative weak-coupling technique is developed, which allows an implicit forcing of the CAA stage with a given source signal coming from an a priori evaluation, whether the latter evaluation is of analytical or computational nature. Then, thanks to additional innovative solutions, the resulting CAA-based hybrid approach is optimized so that it can be applied to realistic and complex acoustic problems in an easier and safer way. All these innovative features are then validated on the basis of an academic test case, before the resulting advanced CAA-based hybrid methodology is applied to two problems of flow-induced noise radiation. This demonstrates the ability of the here proposed method to address realistic problems, by offering to handle at the same time both acoustic generation and propagation phenomena, despite their intrinsic multiscale character.     

一种改进的遗传算法的三维人脸建模方法

采用网格重采样算法提取人脸特征实现了真实人脸与原型三维人脸之间的精确对应,在此基础上,提出了基于改进的方向自学习遗传算法的模型匹配算法,该算法在局部搜索中引入方向信息,提高了算法的收敛速度和重建精度,实验结果表明:新算法可有效实现真实人脸与原型三维人脸之间的精确对应,减少了优化参数,提高了模型重建精度和速度,缩短了模型...     

A hybrid strategy for the time- and energy-efficient trajectory planning of parallel platform manipulators

In planning the trajectories of motor-driven parallel platform manipulators, the objective is to identify the trajectory which accomplishes the assigned motion with the minimal travel time and energy expenditure subject to the constraints imposed by the kinematics and dynamics of the manipulator structure. In this study, the possible trajectories of the manipulator are modeled using a parametric path representation, and the optimal trajectory is then obtained using a hybrid scheme comprising the particle swarm optimization method and the local conjugate gradient method. The numerical results confirm the feasibility of the optimized trajectories and show that the hybrid scheme is not only more computationally efficient than the standalone particle swarm optimization method, but also yields solutions of a higher quality.     

Reconstruction of the corrosion boundary for the Laplace equation by using a boundary collocation method

In this paper, we consider the identification of a corrosion boundary for the two-dimensional Laplace equation. A boundary collocation method is proposed for determining the unknown portion of the boundary from the Cauchy data on a part of the boundary. Since the resulting matrix equation is badly ill-conditioned, a regularized solution is obtained by employing the Tikhonov regularization technique, while the regularization parameter is provided by the generalized cross-validation criterion. Numerical examples show that the proposed method is reasonable and feasible.     

A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem

This paper proposes a hybrid variable neighborhood search (HVNS) algorithm that combines the chemical-reaction optimization (CRO) and the estimation of distribution (EDA), for solving the hybrid flow shop (HFS) scheduling problems. The objective is to minimize the maximum completion time. In the proposed algorithm, a well-designed decoding mechanism is presented to schedule jobs with more flexibility. Meanwhile, considering the problem structure, eight neighborhood structures are developed. A kinetic energy sensitive neighborhood change approach is proposed to extract global information and avoid being stuck at the local optima. In addition, contrary to the fixed neighborhood set in traditional VNS, a dynamic neighborhood set update mechanism is utilized to exploit the potential search space. Finally, for the population of local optima solutions, an effective EDA-based global search approach is investigated to direct the search process to promising regions. The proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of experimental results, the high performance of the proposed HVNS algorithm is shown in comparison with four efficient algorithms from the literature.     

A subspace trust-region method for seismic migration inversion

In seismic exploration, regularized migration inversion of seismic data usually requires solving a weighted least-squares problem with constrains. It is well known that directly solving this problem using some decomposition techniques is very time-consuming, which makes it less possible for practical use. For iterative methods, previous research is mainly on solving the inverse model in a full space. In this paper, a robust subspace method is applied to seismic migration inversion with Gaussian beam representations of Green's function. The problem is first formulated by incorporating regularizing constraints, and then, it is changed from full space to subspace and solved by a trust-region method. To test the potential of the application of the developed method, synthetic data simulations are performed. The results show that this method is very promising for ill-posed seismic migration inversion problems.     

The nonlinear Landweber method applied to an inverse scattering problem for sound-soft obstacles in 3D

The inverse scattering problem for sound-soft obstacles is considered for both smooth and piecewise smooth surfaces in 3D. The nonlinear and ill-posed integral equation of the first kind is solved by the nonlinear Landweber method. It is an iterative regularization scheme to obtain approximations for the unknown boundary of the obstacle. It is stable with respect to noise and essentially no extra work is required to incorporate several incident waves. So far, it has only been applied to the two dimensional case. Two different integral equations are presented to obtain far-field data. Furthermore, the domain derivative and its adjoint are characterized. The integral equations of the second kind are approximated by a boundary element collocation method. The two-grid method is used to solve the large and dense linear systems. Numerical examples are illustrated to show that both smooth and piecewise smooth obstacles can be reconstructed with this method, where the latter case has not yet been reported.