Robust Search Algorithm Research Articles

Optimal measurement setup for damage detection in piezoelectric plates

An optimization of the excitation–measurement configuration is proposed for the characterization of damage in PZT-4 piezoelectric plates, from a numerical point of view. To perform such an optimization, a numerical method to determine the location and extent of defects in piezoelectric plates is developed by combining the solution of an identification inverse problem, using genetic algorithms and gradient-based methods to minimize a cost functional, and using an optimized finite element code and meshing algorithm. In addition, a semianalytical estimate of the probability of detection is developed and validated, which provides a flexible criterion to optimize the experimental design. The experimental setup is optimized upon several criteria: maximizing the probability of detection against noise effects, ensuring robust search algorithm convergence and increasing the sensitivity to the presence of the defect. The measurement of voltage ϕ is concluded to provide the highest identifiability, combined with an excitation of the specimen by a mechanical traction transverse to the polarization direction. Sufficient accuracy is predicted for the damage location and sizing under realistic noise levels.

Applications of Advanced Polynomial Neural Networks for Prediction of Multi-well Reservoir Performance

The major impediment in the formal optimization of large petroleum-producing fields is the cost of computing the state of the objects being optimized. Previous studies for prediction of reservoir performance with respect to time have used local inflow performance relationships or material balance models. These approaches, however, ignored flow interactions among wells during the optimization process, often resulting in suboptimal operations. In this study, a new polynomial neural network (PNN) with layer over-passing structure has been developed to replace a relatively time consuming reservoir simulator through robust and systematic search algorithm. The networks are subject to some form of training based on a representative sample of simulations that can be used as a re-useable knowledge base of information for addressing many different management questions. The proposed approach significantly reduces computational effort for optimizing the development scheme within reasonable accuracy and outperforms other neural network models.

Frame-level temporal calibration of video sequences from unsynchronized cameras

This paper describes a method for temporally calibrating video sequences from unsynchronized cameras by image processing operations, and presents two search algorithms to match and align trajectories across different camera views. Existing multi-camera systems assume that input video sequences are synchronized either by genlock or by time stamp information and a centralized server. Yet, hardware-based synchronization increases installation cost. Hence, using image information is necessary to align frames from the cameras whose clocks are not synchronized. The system built for temporal calibration is composed of three modules: object tracking module, calibration data extraction module, and the search module. A robust and efficient search algorithm is introduced that recovers the frame offset by matching the trajectories in different views, and finding the most reliable match. Thanks to information obtained from multiple trajectories, this algorithm is robust to possible errors in background subtraction and location extraction. Moreover, the algorithm can handle very large frame offsets. A RANdom SAmple Consensus (RANSAC) based version of this search algorithm is also introduced. Results obtained with different video sequences are presented, which show the robustness of the algorithms in recovering various range of frame offsets for video sequences with varying levels of object activity.

A comparative genomics approach to identifying the plasticity transcriptome

BackgroundNeuronal activity regulates gene expression to control learning and memory, homeostasis of neuronal function, and pathological disease states such as epilepsy. A great deal of experimental evidence supports the involvement of two particular transcription factors in shaping the genomic response to neuronal activity and mediating plasticity: CREB and zif268 (egr-1, krox24, NGFI-A). The gene targets of these two transcription factors are of considerable interest, since they may help develop hypotheses about how neural activity is coupled to changes in neural function.ResultsWe have developed a computational approach for identifying binding sites for these transcription factors within the promoter regions of annotated genes in the mouse, rat, and human genomes. By combining a robust search algorithm to identify discrete binding sites, a comparison of targets across species, and an analysis of binding site locations within promoter regions, we have defined a group of candidate genes that are strong CREB- or zif268 targets and are thus regulated by neural activity. Our analysis revealed that CREB and zif268 share a disproportionate number of targets in common and that these common targets are dominated by transcription factors.ConclusionThese observations may enable a more detailed understanding of the regulatory networks that are induced by neural activity and contribute to the plasticity transcriptome. The target genes identified in this study will be a valuable resource for investigators who hope to define the functions of specific genes that underlie activity-dependent changes in neuronal properties.

An Image Rejection Mixer with AI-Based Improved Performance for WCDMA Applications

This paper proposes the combination of adjustable architecture and parameter optimization software, employing a method based on artificial intelligence (AI), to realize an image rejection mixer (IRM) that can enhance its image rejection ratio within a short period of time. The main components of the IRM are 6 Gilbert-cell multipliers. The tail current of each multiplier is adjusted by the optimization software, and the gain and phase characteristics are optimized. This adjustment is conventionally extremely difficult because the 6 tail currents to be adjusted simultaneously are mutually interdependent. In order to execute this adjustment efficiently, we employed a Genetic Algorithm (GA) that is a robust search algorithm that can find optimal parameter settings in a short time. We have successfully developed an IRM chip that has a performance of 71 dB and is suitable for single-chip integration with WCDMA applications.

Phase-coupled two-dimensional speckle tracking algorithm

A new two-dimensional (2-D) speckle tracking method for displacement estimation based on the gradients of the magnitude and phase of 2-D complex correlation in a search region is presented. The novelty of this approach is that it couples the phase and magnitude gradients near the correlation peak to determine its coordinates with sub-sample accuracy in both axial and lateral directions. This is achieved with a minimum level of lateral interpolation determined from the angles between the magnitude and phase gradient vectors on the sampled (laterally interpolated) 2-D cross-correlation grid. The key result behind this algorithm is that the magnitude gradient vectors' final approach to the true peak is orthogonal to the zero-phase contour. This leads to a 2-D robust projection on the zero-phase contour that results in subsample accuracy at interpolation levels well below those needed using previously proposed methods. A full description of the 2-D, phase-coupled approach is given, including two implementations based on a geometric projection and constrained optimization. In addition, a robust fast search algorithm that allows the localization of the true peak without the need for exhaustive search is given. Experimental validation on three data sets from speckle-generating phantoms undergoing uniform diagonal motion, uniform axial deformation, and nonuniform lateral flow is given. It is shown that estimated 2-D displacement fields obtained using the phase-coupled technique display a full range of values covering the dynamic range without evidence of quantization. In comparison, a previously published method using 1-D phase-projection after lateral interpolation produces severely quantized lateral displacement fields (at the same levels of interpolation as the 2-D, phase-coupled method).

An Improved Implementation of the LBFGS Algorithm for Automatic History Matching

Summary For large scale history matching problems, where it is not feasible to compute individual sensitivity coefficients, the limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS)is an efficient optimization algorithm, (Zhang and Reynolds, 2002; Zhang, 2002). However, computational experiments reveal that application of the original implementation of LBFGS may encounter the following problems: (i) converge to a model which gives an unacceptable match of production data; (ii) generate a bad search direction that either leads to false convergence or a restart with the steepest descent direction which radically reduces the convergence rate; (iii) exhibit overshooting and undershooting, i.e., converge to a vector of model parameters which contains some abnormally high or low values of model parameters which are physically unreasonable. Overshooting and undershooting can occur even though all history matching problems are formulated in a Bayesian framework with a prior model providing regularization. We show that the rate of convergence and the robustness of the algorithm can be significantly improved by: (1) a more robust line search algorithm motivated by the theoretical result that the Wolfe conditions should be satisfied; (2) an application of a data damping procedure at early iterations or (3) enforcing constraints on the model parameters. Computational experiments also indicate that (4) a simple rescaling of model parameters prior to application of the optimization algorithm can improve the convergence properties of the algorithm although the scaling procedure used can not be theoretically validated.

Hybrid Population-Based Algorithms for the Bi-Objective Quadratic Assignment Problem

We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant.

Computational modelling of plasticity induced by martensitic phase transformations

A time integration scheme is presented for the martensitic phase transformation model developed in recent theoretical work of Turteltaub and Suiker (A multi-scale thermomechanical model for cubic to tetragonal martensitic phase transformations 2005; Transformation-induced plasticity in ferrous alloys 2005). The phase transformation model can be used for analysing transformation-induced plasticity (TRIP) phenomena in ferrous alloys. The microstructural information for the phase transformation model is provided by the crystallographic theory of martensitic transformations. The transformation characteristics depend on the specific transformation systems activated during a loading process. The time integration scheme is formulated within a framework of finite deformations, where the stress-update algorithm is based on a fully implicit Euler backward discretization. A robust search algorithm is used for detecting the transformation systems activated during loading. The completion of the transformation process is prescribed by a constraint on the total martensitic volume fraction, which is accurately satisfied in the converged state using a sub-stepping algorithm. The computation of the consistent tangent operator is performed through a numerical differentiation method, which avoids the determination of extensive analytical derivatives and allows the model to be easily adapted if necessary. The ability of the algorithm to solve complex transformation-induced plasticity problems is illustrated with the aid of three-dimensional analyses, in which an aggregate of single-crystal grains of retained austenite embedded in a ferrite-based matrix is subjected to uniaxial tension. The response characteristics are in agreement with experimental findings of Jacques et al. (Philos. Mag. 2001; 81(7):1789; Metall. Mater. Trans. A 2001; 32A:2759) and Oliver et al. (Appl. Phys. A 2002; 74:S1143). Copyright © 2005 John Wiley & Sons, Ltd.

Numerical direct kinematic analysis of fully parallel linearly actuated platform type manipulators

AbstractThis article presents a new numerical approach for solving the direct kinematics problem of fully parallel, linearly actuated platform manipulators. The solution procedure consists of two stages. The first stage transforms the direct kinematics problem into an equivalent nonlinear programming program, and a robust search algorithm is developed to bring the moving platform from arbitrary initial approximation to a feasible configuration that is near to the true solution. The second stage uses the Newton‐Raphson iterative method to converge the solution to the desired accuracy. This approach is numerically stable and computationally efficient. In addition, by randomly perturbing the initial approximations, it can be implemented successively to find multiple solutions to the direct kinematics problem. Two numerical examples are presented to demonstrate the stability and efficiency of this approach. © 2002 Wiley Periodicals, Inc.

Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems

A robust search algorithm should ideally exhibit reasonable performance on a diverse and varied set of problems. In an earlier paper Lim et al. (Computational Optimization and Applications, vol. 15, no. 3, 2000), we outlined a class of hybrid genetic algorithms based on the k-gene exchange local search for solving the quadratic assignment problem (QAP). We follow up on our development of the algorithms by reporting in this paper the results of comprehensive testing of the hybrid genetic algorithms (GA) in solving QAP. Over a hundred instances of QAP benchmarks were tested using a standard set of parameters setting and the results are presented along with the results obtained using simple GA for comparisons. Results of our testing on all the benchmarks show that the hybrid GA can obtain good quality solutions of within 2.5% above the best-known solution for 98% of the instances of QAP benchmarks tested. The computation time is also reasonable. For all the instances tested, all except for one require computation time not exceeding one hour. The results will serve as a useful baseline for performance comparison against other algorithms using the QAP benchmarks as a basis for testing.

Protein Structure Prediction as a Hard Optimization Problem: The Genetic Algorithm Approach

Protein structure prediction can be shown to be an NP-hard problem; the number of conformations grows exponentially with the number of residues. The native conformations of proteins occupy a very small subset of these, hence an exploratory, robust search algorithm, such as a genetic algorithm (GA), is required. The dynamics of genetic algorithms tend to be complicated and problem-specific. Howver, their empirical success warrants their further study. In this paper, guidelines for the design of genetic algorithms for protein structure prediction are determined. To accomplish this, the performance of the simplest genetic algorithm is investigated for simple lattice-based protein structure prediction models (which is extendible to real-space), using energy minimization. The study has led us to two important conclusions for ‘protein-structure-prediction-genetic-algorithms’. Firstly, they require high resolution building blocks attainable by multi-point crossovers and secondly they require a local dynamics operator to ‘fine tune’ good conformations. Furthermore, we introduce a statistical mechanical approach to analyse the genetic algorithm dynamics and suggest a convergence criterion using a quantity analogous to the free energy of a population.

Automatic interpretation and coding of face images using flexible models

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Optimal design of the cure cycle for consolidation of thick composite laminates

AbstractAutoclave cure of the prepreg lay‐up has been an importance method for fabrication of advanced fiber reinforced composites in the aerospace industry. Quality of the autoclave cured composite is largely affected by the cure cycle. In this study, the genetic algorithm is applied to design a cure cycle for the consolidation of thick laminated composites. The objective of the design is to select a cure cycle that results in a shorter cycle time for the process while avoiding the thermal runaway inside the composites. The results show that the genetic algorithm is a feasible and robust search algorithm to determine the cure cycle for the consolidation process.

Interpretation of 3‐D permeability measurements for RTM modeling

AbstractThe resin transfer molding (RTM) technique often utilizes reinforcement with a complex fiber architecture. Several parameters, including the permeability tensor, are necessary to characterize the flow behavior in these intricate fibrous porous media. In this paper, a general procedure for extracting three‐dimensional permeability tensors from data is presented. A general procedure is warranted if the permeability tensor lies out of the material plane. An approximate solution to Darcy's law was employed to relate the components of the permeability tensor to experimental measurements. The procedure requires inverting six nonlinear equations with a robust binary search algorithm. The accuracy of the approximate solution to Darcy's law was checked and found to be in close agreement with a nearly exact solution to Darcy's law obtained by finite element methods.

Road‐Maintenance Planning Using Genetic Algorithms. I: Formulation

The present paper demonstrates the applicability of genetic algorithms, as an optimization tool capable of overcoming combinatorial explosion, to the road‐maintenance planning problem at the network level. Genetic algorithms are search algorithms based upon the principles of Darwinian evolution. The concept of the survival of the fittest is used in a structured, yet randomized, information exchange to form a robust search algorithm. Genetic algorithms efficiently exploit historical information to locate search points with improved performance. The theoretical basis and operations of genetic algorithms are presented. A computer model, PAVENET, formulated on the operating principles of genetic algorithms to serve as an analytical aid for pavement maintenance engineers, is introduced. The formulation of the PAVENET model is described in detail. Analyses are conducted to show the characteristics of important operating parameters of the PAVENET program. These parameters include: (1) Parent pool size; (2) mutat...

Rigid-plastic finite element analysis of sheet metal forming processes using continuous contact treatment and membrane elements incorporating bending effects

An improved method of contact treatment is developed in which both the FEM (finite element method) mesh and the tool surfaces are described with parametric patches which have C 1 continuity or more. The FEM mesh at the current configuration is globally converted into smooth surface patches of net form. A continuous sheet surface normal scheme is devised by using the globally smoothed mesh surface. A robust and efficient contact search algorithm is also developed in connection with the proposed continuous contact treatment. As an improved version of conventional membrane element, the BEAM (abbreviated from Bending Energy Augmented Membrane) element is newly developed based on node spring. In order to verify the effectiveness of the proposed continuous contact treatment and BEAM element, deep drawing processes of complicated parts including actual auto-body panel are analysed. Comparisons with the available experiment and analysis show that the proposed continuous contact treatment in connection with BEAM elements can be effectively applied to the analysis of sheet metal forming processes for arbitrarily curved parts with deformation regions in which the bending effect must be taken into account.

A NOTE ON THE REEL ALLOCATION PROBLEM

The reel allocation problem is to choose the numbers of reels of each of a number of types of items to be used in populating a printed circuit board by a SMT machine so as to maximize the length of an uninterrupted machine production run while using no more slots for reels than are available. We show this problem can be solved to optimality very efficiently with a quite simple and robust bisection search algorithm. Algorithm run times are less than 1 second on a 486 PC.

An adaptive predistorter for a power amplifier based on adjacent channel emissions (mobile communications)

A slowly adapting predistorter is presented. The approach is to minimize the transmitter output power in spectral regions occupied only by intermodulation (IM) products. In this way, only a spot power measurement is required. This technique relies on the principle that the power amplifier's characteristics vary slowly with time. By monitoring the out-of-band power one can obtain an estimate for the distortion introduced by the power amplifier. Adaptation is accomplished by iterative adjustment of the predistorter parameters to minimize the IM power. For a polynomial predistorter, the authors analytically demonstrate that the IM power is a quadratic function of the coefficients. A variety of algorithms therefore apply. The authors present an analog static predistortion linearization circuit that uses the envelope of the baseband signal to generate the nonlinear functional used in predistorting the input signal. The improvement obtained with an amplitude-modulated input signal was 15 dB in the third- and 5 dB in the fifth-order intermodulation products. The IM improvement could be maintained with the use of a robust direct search algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Rapid Identification of Critical Slip Surfaces: Structure

Nearly all limit equilibrium‐based computer programs for slope stability analysis are able to search for the critical slip surface and the associated minimum factor of safety. However, the vast majority of these programs use relatively inefficient search routines. A structure is developed to enable a more robust search algorithm—the simplex method of Nelder and Mead—to be readily incorporated into any existing limit equilibrium‐based slope stability program. An application is provided to illustrate that the simplex‐based search algorithm is accurate, efficient, and quite reliable. Extensions to other problems in geotechnical analysis and design also are described.