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Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity (T), hydraulic conductivity (K), storage coefficient (S), and leakance (L), for which the graphical method is widely used. In the present study, the efficacy of the genetic algorithm (GA) optimization technique is assessed in estimating aquifer parameters from the time-drawdown pumping test data. Computer codes were developed to optimize various aquifer parameters under different hydrogeologic conditions by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using 12 sets of the published and unpublished aquifer test data. The aquifer parameters were also estimated by the graphical method using AquiferTest software, and were compared with those obtained by the GA technique. The GA technique yielded significantly low values of the sum of square errors (SSE) for almost all the datasets under study. The results revealed that the GA technique is an efficient and reliable method for estimating various aquifer parameters, especially in the situation when the graphical matching is poor. Also, it was found that because of its inherent characteristics, GA avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. Furthermore, the performance evaluation of the developed GA-based computer codes showed that the fitness value (SSE) of the best point in a population reduces with increasing generation number and population size. The analysis of the sensitivity of the parameters during the performance of GA indicated that a unique set of aquifer parameters was obtained for all three aquifer systems. The GA-based computer programs with interactive windows developed in this study are user-friendly and can serve as a teaching and research tool, which could also be useful for practicing hydrologists and hydrogeologists. [ABSTRACT FROM AUTHOR]

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on the foundation pit dewatering project at the inverted siphon section of Xixiayuan canal head, specifically from pile number XZ0+326 to XZ0+500. It establishes an optimized mathematical model for foundation pit dewatering that incorporates three objectives. Additionally, a dewatering optimization program is developed by utilizing the MATLAB optimization toolbox and the multi-objective optimization algorithm program based on the NSGA-II algorithm (Gamultiobj). The multi-objective optimization mathematical model is solved, and a Pareto-optimal solution set with uniform distribution is obtained. The multi-objective optimization evaluation system based on AHP is constructed from the three aspects of dewatering cost, the impact of settlement on the environment, and the safety and stability of the foundation pit. The optimization scheme of the Pareto-optimal solution set is selected as the decision result to provide multiple feasible schemes for the dewatering construction of foundation pits. The optimization scheme is verified by using the GMS software. The simulation results demonstrate that the optimization scheme fulfills the requirements for water level and settlement control. Moreover, the developed optimization program efficiently solves the multi-objective optimization problem associated with foundation pit dewatering. Lastly, an evaluation system incorporating the NSGA-II algorithm and AHP is developed and utilized in the context of dewatering engineering in order to offer multiple viable optimal dewatering schemes. [ABSTRACT FROM AUTHOR]

Scrapers have established an important position in the earthmoving field as they are independently capable of accomplishing an earthmoving operation. Given that loading a scraper to its capacity does not entail its maximum production, optimizing the scraper's loading time is an essential prerequisite for successful operations management. The relevant literature addresses the loading time optimization through a graphical method that is founded on the invalid assumption that the hauling time is independent of the load time. To correct this, a new algorithmic optimization method that incorporates the golden section search and the bisection algorithm is proposed. Comparison of the results derived from the proposed and the existing method demonstrates that the latter entails the systematic needless prolongation of the loading stage thus resulting in reduced hourly production and increased cost. Therefore, the proposed method achieves an improved modeling of scraper earthmoving operations and contributes toward a more efficient cost management. [ABSTRACT FROM AUTHOR]

Purpose: Highly constrained backprojection-local reconstruction (HYPR-LR) has made a dramatic impact on magnetic resonance angiography (MRA) and shows promise for positron emission tomography (PET) because of the improvements in the signal-to-noise ratio (SNR) it provides dynamic images. For PET in particular, HYPR-LR could improve kinetic analysis methods that are sensitive to noise. In this work, the authors closely examine the performance of HYPR-LR in the context of kinetic analysis, they develop an implementation of the algorithm that can be tailored to specific PET imaging tasks to minimize bias and maximize improvement in variance, and they provide a framework for validating the use of HYPR-LR processing for a particular imaging task. Methods: HYPR-LR can introduce errors into non sparse PET studies that might bias kinetic parameter estimates. An implementation of HYPR-LR is proposed that uses multiple temporally summed composite images that are formed based on the kinetics of the tracer being studied (HYPR-LR-MC). The effects of HYPR-LR-MC and of HYPR-LR using a full composite formed with all the frames in the study (HYPR-LR-FC) on the kinetic analysis of Pittsburgh compound-B ([11C]-PIB) are studied. HYPR-LR processing is compared to spatial smoothing. HYPR-LR processing was evaluated using both simulated and human studies. Nondisplaceable binding potential (BPND) parametric images were generated from fifty noise realizations of the same numerical phantom and eight [11C]-PIB positive human scans before and after HYPR-LR processing or smoothing using the reference region Logan graphical method and receptor parametric mapping (RPM2). The bias and coefficient of variation in the frontal and parietal cortex in the simulated parametric images were calculated to evaluate the absolute performance of HYPR-LR processing. Bias in the human data was evaluated by comparing parametric image BPND values averaged over large regions of interest (ROIs) to Logan estimates of the BPND from TACs averaged over the same ROIs. Variance was assessed qualitatively in the parametric images and semiquantitatively by studying the correlation between voxel BPND estimates from Logan analysis and RPM2. Results: Both the simulated and human data show that HYPR-LR-FC overestimates BPND values in regions of high [11C]-PIB uptake. HYPR-LR-MC virtually eliminates this bias. Both implementations of HYPR-LR reduce variance in the parametric images generated with both Logan analysis and RPM2, and HYPR-LR-FC provides a greater reduction in variance. This reduction in variance nearly eliminates the noise-dependent Logan bias. The variance reduction is greater for the Logan method, particularly for HYPR-LR-MC, and the variance in the resulting Logan images is comparable to that in the RPM2 images. HYPR-LR processing compares favorably with spatial smoothing, particularly when the data are analyzed with the Logan method, as it provides a reduction in variance with no loss of spatial resolution. Conclusions: HYPR-LR processing shows significant potential for reducing variance in parametric images, and can eliminate the noise-dependent Logan bias. HYPR-LR-FC processing provides the greatest reduction in variance but introduces a positive bias into the BPND of high-uptake border regions. The proposed method for forming HYPR composite images, HYPR-LR-MC, eliminates this bias at the cost of less variance reduction. [ABSTRACT FROM AUTHOR]

Highlights • A graphical method is proposed for optimal vibration distribution. • Existence and optimality of feasible controllers are addressed. • Performance limit of controllers is obtained. • Influence of constraints on optimal solutions is delineated. Abstract A number of design methodologies have been proposed for active control of vibrations. Most of the approaches proceed with formulating control design as optimization problems. These optimization-based methods are powerful and easy to use since numerical algorithms are usually available for finding solutions. However, the available methods have to be significantly modified to answering questions such as controller existence and achievable performance etc. This paper proposes a performance-limit-oriented solution developed for the problem of optimal distribution. A graphical approach is utilized for representation of the solutions. A number of important results upon existence of feasible controllers, optimality and performance limit of controllers, controller synthesis, as well as constraints handling are obtained; the puzzling issue of compromise between performance indices is resolved; the influence of constraints on optimal solutions are developed within the same graphical framework. This leads to a systematic design methodology to active vibration control for optimal distribution. The theoretical results are demonstrated through numerical examples and real time simulations. [ABSTRACT FROM AUTHOR]

One of the main purposes for conducting well tests is to obtain information about reservoir parameters, such as its permeability and the existence of skin effects. Determining individual layer properties from pressure transient data in multilayer reservoirs is challenging, since pressure behavior is influenced by the properties of all layers. This work presents three methods for estimating layer properties from well testing data in multilayer reservoirs with multilateral wells. First, we extend two existing graphical straight-line methods for estimating individual layer properties in multilayer systems with vertical wells to stratified reservoirs with multilateral horizontal wells. These techniques are the rate-normalized pressure analysis and the delta transient method. Both methods require a clear identification of radial flow regimes, which may not occur in a practical case. Additionally, we show that a computer-assisted history matching method based on the Nelder–Mead optimization algorithm can also be used to evaluate layer permeabilities and skin. This optimization method does not rely on the identification of any specific flow regime. Estimates obtained from the graphical methods were used as initial guess for the assisted history matching. The proposed techniques are applied on a set of synthetic well-test cases, where pressure and layer flow-rate profiles are computed from an existing analytical model. Results show that all three methods are able to yield good estimates for layer properties if both early- and late-time radial flow are observed. In cases where only one of the radial flow regimes is identified, then the NM algorithm provides the best results, showing that the assisted history matching improved the estimates provided by the graphical method. • This work aims to determine individual layer permeabilities and skin factors. • It considers a multilayer reservoir drilled by a multilateral horizontal well. • The RNPA and DTM are extended to multilateral horizontal wells. • The Nelder–Mead optimization algorithm was also employed to estimate layer properties. • Good results were obtained in cases where both radial flow regimes are identified. [ABSTRACT FROM AUTHOR]

We propose a method to approximate Intuitionistic Fuzzy Sets (IFSs) with Shadowed Sets that could be used, in decision making or similar tasks, when the full information about membership values is not necessary, is difficult to process or to interpret. Our approach is based on an information‐theoretic perspective and aims at preserving the uncertainty, represented through an entropy measure, in the original IFS by minimizing the difference between the entropy in the input IFS and the output Shadowed Set. We propose three different efficient optimization algorithms that retain Fuzziness, Lack of Knowledge, or both, and illustrate their computation through an illustrative example. We also evaluate the application of the proposed approximation methods in the Machine Learning setting by showing that the approximation, through the proposed methods, of IFS k‐Nearest Neighbors is able to outperform, in terms of running time, the standard algorithm. [ABSTRACT FROM AUTHOR]

The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: “Which experimental outputs should be measured to ensure that unique model parameters can be calculated?”. Stated formally, we examine the topic of minimal output sets that guarantee a model’s structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times. [ABSTRACT FROM AUTHOR]

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal's two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm. [ABSTRACT FROM AUTHOR]

In this article we present algorithmic and coding process of estimating mean values based on estimating density methods over kernels. We present algorithms, to ease observation of covariational relationships and mutual behaviors of multidimensional arrays. Observing these relationships will be possible through tensors of estimated densities calculated values and their visualization on a graphical tool. The software can read data from a file. It accepts a variable number of columns (variables) and for each of those it calculates minimum and maximum values for further use. It is possible to choose up to two variables on their entire range and to use fixed values for the other variables. Algorithms used in this phase give mean dependence of segment variables to each others. The results arrays are then used as an input to the graphical class. The right optimizations have been made in the process of calculating the array's values. Realization of any n-1 dimensional cuts is used to give a graphical presentation for mean estimation of given data. The software produces graphics for above calculations [ABSTRACT FROM AUTHOR]

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts. [ABSTRACT FROM AUTHOR]

The design of cost optimal heat exchanger networks is a difficult optimization problem owing both to the nonlinear models required and the combinatorial size of the search space. When stream splitting is considered, the combinatorial aspects make the problem even harder. This article describes the implementation of a two-level evolutionary algorithm based on a string rewriting grammar for the evolution of the heat exchanger network structure. A biological analogue of genotypes and phenotypes is used to describe structures and specific solutions, respectively. The top-level algorithm evolves structures while the lower level optimizes specific structures. The result is a hybrid optimization procedure that can identify the best structures including stream splitting. Case studies from the literature are presented to demonstrate the capabilities of the novel procedure. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D* using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually apparent dark squares arrayed along the main diagonal of an image I(D*) of D*. The suggested clusters in the object set underlying the reordered relational data are found by defining an objective function that recognizes this blocky structure in the reordered data. The objective function is optimized when the boundaries in I(D*) are matched by those in an aligned partition of the objects. The objective function combines measures of contrast and edginess and is optimized by particle swarm optimization. We prove that the set of aligned partitions is exponentially smaller than the set of partitions that needs to be searched if clusters are sought in D. Six numerical examples are given to illustrate various facets of the algorithm. © 2009 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article presents a study on the identification of transmissivity in an anisotropic groundwater aquifer by a two-dimensional inverse model. It notes that the Tabu search algorithm with the adjacent state method is used to identify the hydrogeological characteristics of an anisotropic aquifer. It emphasizes that the Tabu search algorithm was applied to describe the anisotropic transmissivity components to obviate a local optimum. Moreover, the Adjoint State method is used in calculating the sensitivity of the parameters to increase the efficiency of optimization in aquifers. It also notes that the two-dimensional inverse model might successfully optimize the transmissivity components of anisotropic aquifer.

In this paper, we investigate optimal grasp points on an arbitrary-shaped grasped object using a required external force set. The required external force set is given based on a task, and consists of the external forces and moments, which must be balanced by virtue of contact forces applied by a robotic hand. When the origin is in the interior of the set, a force-closure grasp is required. When the dimension of the set is one, an equilibrium grasp is required. Therefore, we can investigate whatever the desired grasp is, such as when the desired grasp is a force closure and equilibrium grasps. Also, we only have to consider the forces contained in a given required external force set, not the whole set of possible resulting forces. Furthermore, we can avoid the frame-invariant problem (the criterion value changes with the change of the task (object) coordinate frame). We consider an optimization problem from the viewpoint of decreasing the magnitudes of the contact forces needed to balance any external force and moment contained in a given required external force set. In order to solve the problem, we present an algorithm based on a branch-and-bound method. We also present some numerical examples to show the validity of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider linear time invariant (LTI) systems with parameter uncertainty. For such systems, we present global optimization techniques to determine permissible perturbations of the parameters of the system that maintain stability (the so-called parameter stability margins), for cases in which the coefficients of the characteristic equation of the system are polynomial functions of the uncertain parameters. The parameter uncertainty domains for maintaining stability are characterized as hypersolids, defined with respect to l p -norms for various values of p ∈ (1, ∞). Algorithms are devised based on the reformulation–linearization/convexification technique (RLT) in concert with branch-and-bound methods to solve the underlying parametric non-convex subproblems for computing the stability margins. Several illustrative examples are solved to demonstrate the efficacy of the proposed approach towards producing global optimal solutions. We also present comparative computational experience with the commercial global optimizer BARON. [ABSTRACT FROM AUTHOR]

An algorithm is proposed to target the minimum freshwater for fixed flow rate (FF) problems in single-contaminant water networks. The approach is based on an extension of the limiting composite curve concept proposed earlier for fixed contaminant-load (FC) problems. The targeting method is elegant, noniterative, and can be applied to FF problems involving regeneration to minimize waste discharge. To design networks that achieve the minimum freshwater targets set for FF problems, an algorithm is presented based on the principle of nearest neighbors. The principle simply states that the sources to be chosen to satisfy a particular water demand must be the nearest available neighbors in terms of contaminant concentration. A significant advantage of the approach is that the same targeting concepts can be profitably used to determine the minimum makeup utility in hydrogen networks. Furthermore, hydrogen networks may be designed by the unified conceptual approach using the nearest neighbors algorithm. The hydrogen networks may then be evolved to account for the pressure constraints imposed by compressors or improved by regeneration through purification processes such as pressure-swing adsorption. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of the efficient exploration of the architectural design space for parameterized embedded systems. The exploration problem is multi-objective (e.g., energy and delay), so the main goal of this work is to find a good approximation of the Pareto-optimal configurations representing the best energy/delay trade-offs by varying the architectural parameters of the target system. In particular, the paper presents a Design Space Exploration (DSE) framework to simulate the target system and to dynamically profile the target applications. In the proposed DSE framework, a set of heuristic algorithms have been analyzed to reduce the overall exploration time by computing an approximated Pareto set of configurations with respect to the selected figures of merit. Once the approximated Pareto set has been built, the designer can quickly select the best system configuration satisfying the constraints. Experimental results, derived from the application of the proposed DSE framework to a superscalar architecture, show that the exploration time can be reduced by three orders of magnitude with respect to the full search approach, while maintaining a good level of accuracy. [ABSTRACT FROM AUTHOR]

An algorithm for the verification of composite position tolerances is proposed and its computer implementation is described. It is shown that a prevalent graphical verification method does not adequately reflect the standard. Composite position tolerance requirements are mathematically formulated as a non-linear optimization problem with a linear objective function and quadratic constraints. The method of feasible directions is used as the basis for an efficient computer implementation. Its performance is evaluated under various test conditions as well as against a simplex pattern search. [ABSTRACT FROM AUTHOR]