Automatic Optimization Algorithm Research Articles

Probabilistic optimization for conceptual rainfall‐runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms

Automatic optimization algorithms are used routinely to calibrate conceptual rainfall‐runoff (CRR) models. The goal of calibration is to estimate a feasible and unique (global) set of parameter estimates that best fit the observed runoff data. Most if not all optimization algorithms have difficulty in locating the global optimum because of response surfaces that contain multiple local optima with regions of attraction of differing size, discontinuities, and long ridges and valleys. Extensive research has been undertaken to develop efficient and robust global optimization algorithms over the last 10 years. This study compares the performance of two probabilistic global optimization methods: the shuffled complex evolution algorithm SCE‐UA, and the three‐phase simulated annealing algorithm SA‐SX. Both algorithms are used to calibrate two parameter sets of a modified version of Boughton's [1984] SFB model using data from two Australian catchments that have low and high runoff yields. For the reduced, well‐identified parameter set the algorithms have a similar efficiency for the low‐yielding catchment, but SCE‐UA is almost twice as robust. Although the robustness of the algorithms is similar for the high‐yielding catchment, SCE‐UA is six times more efficient than SA‐SX. When fitting the full parameter set the performance of SA‐SX deteriorated markedly for both catchments. These results indicated that SCE‐UA's use of multiple complexes and shuffling provided a more effective search of the parameter space than SA‐SX's single simplex with stochastic step acceptance criterion, especially when the level of parameterization is increased. Examination of the response surface for the low‐yielding catchment revealed some reasons why SCE‐UA outperformed SA‐SX and why probabilistic optimization algorithms can experience difficulty in locating the global optimum.

Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections.

Segmentation of intact cell nuclei in three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a border around the nucleus under consideration in all successive two-dimensional (2D) planes of the 3D image. The present paper describes an algorithm with two main advantages over the existing method. First, the analyst draws borders only in 2D planes that cut approximately through the center of the nucleus under consideration so that the nuclear borders generally are most distinct. Second, the analyst draws only five borders around each nucleus, and then the algorithm interpolates the entire surface. The algorithm results in segmented objects that correspond to individual, visually identifiable nuclei. The segmented surfaces, however, may not exactly represent the true nuclear surface. An optional, automatic surface optimization algorithm can be applied to reduce this error.

Multi-objective global optimization for hydrologic models

The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study.

Aeroelastic optimization of a helicopter rotor with two-cell composite blades

Aeroelastic and sensitivity analyses of the rotor based on a finite element in space and time are linked to an automated optimization algorithm to perform optimization studies for a four-bladed, soft in-plane composite rotor consisting of a two-cell thin-walled beam. The design variables used in this study are the ply angles of the laminated walls of the composite beam. The objective function minimizes the 4/rev hub loads, with constraints on blade frequencies and aeroelastic stability in forward flight. Optimum design solutions show a reduction in the objective function of about 20% due to elastic stiffnesses and an additional 13% due to composite couplings. Starting from an initially infeasible design, the optimum design solution with negative lag bending-torsion coupling results in an increase in lag mode damping of about 140% compared to the baseline layup. c* c* c* ^d-> ^/> ^m CT c

Aeroelastic Optimization of an Advanced Geometry Helicopter Rotor

Sensitivity derivatives of blade loads and aeroelastic stability of a helicopter rotor in forward flight are calculated as an integral part of a basic aeroelastic analysis using a direct analytical approach. Design variables include nonstructural mass and its placement, chordwise offset of blade center of gravity and aerodynamic center from the elastic axis, blade bending stiffnesses (flap, lag, torsion), and tip geometry (sweep, anhedral, pretwist and planform taper). By means of a sensitivity study, the importance of different design variables on oscillatory hub loads and damping of blade modes is examined. Aeroelastic and sensitivity analyses of the rotor based on a finite element method in space and time are linked with automated optimization algorithms to perform optimization studies of rotor blades. Optimum design solutions, calculated for a four-bladed, soft-inplane hingeless rotor achieved a reduction of 25-60 percent of all 4/rev loads.

Geometrical optimization of sensors for eddy currents. Non destructive testing and evaluation

Design of NDT and NDE sensors is possible by solving Maxwell's relations with FEM or BIM. But the large number of geometrical and electrical parameters of sensor and tested material implies that many results do not give necessarily a well adapted sensor. The authors have used a genetic algorithm for automatic optimization. After having tested this algorithm with analytical solution of Maxwell's relations for cladding thickness measurement, the method has been implemented in a finite element package and applied to the design of a sensor for tracks detection under installed fasteners in an aircraft structure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A fully automated optimization algorithm for determining the 3-D patient contour from photo-peak projection data in SPECT

The class of noninverse problems versus inverse problems is discussed. A general optimization approach to solving certain constrained noninverse problems is presented. This approach is applied in the area of single photon emission computed tomography to estimate the patient outer contour directly from the photo-peak projection data. The resulting algorithm is fully automated, fast, and the determined 3-D patient contour satisfies smoothness constraints. The accuracy and reliability of the algorithm is evaluated through Monte Carlo simulations, patient studies, and an experimental validation study.

Design of mobile phone IIDT-type SAW filters with block-like distribution of transducers

Wide band low-loss SAW filters employing interdigitated interdigital transducers (IIDT's) and suitable for cellular radio applications are presented. After an investigation of the many design parameters that have to be taken into account, a new block-like distribution of transducers within the IIDT structure is studied in detail. This distribution leads to almost identical impedances at the filter input and output. An automatic optimization algorithm is also described. In particular, the finger numbers and the withdrawal weighting functions employed to increase the stopband rejection have been optimized by this algorithm. Filters fabricated on 36/spl deg/ rot Y, X-LiTaO/sub 3/ exhibit fractional bandwidths of up to 5.2% with passband ripples smaller than 1 dB at a center frequency of 500 MHz. The minimum insertion loss and the stopband rejection obtained were 1.8 and 23 dB, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Aeroelastic optimization of a helicopter rotor using an efficient sensitivity analysis

A structural optimization analysis of a hingeless helicopter rotor is developed and applied with the objective of reducing oscillatory hub loads in forward flight. The aeroelastic analysis of the rotor is based on a finite element method in space and time and is linked with automated optimization algorithms. Two types of structural blade representations are used: a generic stiffness-distribution beam and a single-cell, thin-walled beam. For the generic beam representation the design variables are nonstructural mass and its placement, chordwise center of gravity offset from the elastic axis, and structural stiffness (flap, lag, and torsion). For the second type of structural representation, spar width, height, and thickness are used as design variables instead of blade stiffness. Constraints on frequency placement, autorotational inertia, and aeroelastic stability of the blade are included. Sensitivity derivatives are efficiently calculated using a direct analytical approach, with a resulting 80% reduction in total CPU time required to obtain an optimum solution compared with a commonly used finite-difference approach. Optimum solutions resulted in reductions of 25-77% for the generic blade, and 30-50% for the box-beam blade relative to baseline values of the objective function that was comprised of all six components of hub load.

WATFLOOD: a Micro-Computer Based Flood Forecasting System Based on Real-Time Weather Radar

WATFLOOD is a menu-driven micro-computer based flood forecasting system incorporating data management programs to enable real-time use of weather radar for flood forecasting. It includes a radar calibration model called CALIBR and a simulation model called SIMPLE, which is the heart of the system. SIMPLE is a process-based hydrologic simulation model programmed in FORTRAN77. Only the most dominant hydrologic processes affecting flood flows are included. The programming for inputing streamflow, gage rainfall, and reservoir releases uses a spreadsheet type of input format.SIMPLE features an automatic pattern search optimization algorithm to determine which combination of parameters best fit measured conditions. The parameters for optimization are: soil permeability, overland flow roughness, channel roughness, depression storage, and an upper zone depletion factor. In the operational mode, the calibration option is used to determine the initial soil moisture based on real-time measurements of streamflow.Rada...

Models in radiotherapy: definition of decision criteria.

A method is presented by which dose distributions in radiotherapy may be judged. This method, based on statistical decision theory, combines the calculated probabilities of radiation induced complications (including failure to control disease) with the therapist's judgment of the morbidity of each complication to yield a single value representing the clinical utility of the dose distribution. Using this figure of merit, alternative dose distributions can be compared on a clinical basis. If the morbidities associated with each injury are satisfactorily estimated, and the dose response parameters are adjusted to match clinical experience, this method can be used to evaluate novel treatment techniques prior to their implementation. Automatic optimization algorithms can be used to maximize the figure of merit as a function of the physical treatment parameters so as to provide a statistically optimal treatment.