Homotopy optimization methods for global optimization.
Dunlavy, Daniel M.; O'Leary, Dianne P. (University of Maryland, College Park, MD)
2005-12-01
We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating performance of HOM and HOPE.
Enhancing Polyhedral Relaxations for Global Optimization
ERIC Educational Resources Information Center
Bao, Xiaowei
2009-01-01
During the last decade, global optimization has attracted a lot of attention due to the increased practical need for obtaining global solutions and the success in solving many global optimization problems that were previously considered intractable. In general, the central question of global optimization is to find an optimal solution to a given…
Enhancing Polyhedral Relaxations for Global Optimization
ERIC Educational Resources Information Center
Bao, Xiaowei
2009-01-01
During the last decade, global optimization has attracted a lot of attention due to the increased practical need for obtaining global solutions and the success in solving many global optimization problems that were previously considered intractable. In general, the central question of global optimization is to find an optimal solution to a given…
Method of constrained global optimization
NASA Astrophysics Data System (ADS)
Altschuler, Eric Lewin; Williams, Timothy J.; Ratner, Edward R.; Dowla, Farid; Wooten, Frederick
1994-04-01
We present a new method for optimization: constrained global optimization (CGO). CGO iteratively uses a Glauber spin flip probability and the Metropolis algorithm. The spin flip probability allows changing only the values of variables contributing excessively to the function to be minimized. We illustrate CGO with two problems-Thomson's problem of finding the minimum-energy configuration of unit charges on a spherical surface, and a problem of assigning offices-for which CGO finds better minima than other methods. We think CGO will apply to a wide class of optimization problems.
Global optimality of extremals: An example
NASA Technical Reports Server (NTRS)
Kreindler, E.; Newman, F.
1980-01-01
The question of the existence and location of Darboux points is crucial for minimally sufficient conditions for global optimality and for computation of optimal trajectories. A numerical investigation is presented of the Darboux points and their relationship with conjugate points for a problem of minimum fuel, constant velocity, and horizontal aircraft turns to capture a line. This simple second order optimal control problem shows that ignoring the possible existence of Darboux points may play havoc with the computation of optimal trajectories.
Global optimization of digital circuits
NASA Astrophysics Data System (ADS)
Flandera, Richard
1991-12-01
This thesis was divided into two tasks. The first task involved developing a parser which could translate a behavioral specification in Very High-Speed Integrated Circuits (VHSIC) Hardware Description Language (VHDL) into the format used by an existing digital circuit optimization tool, Boolean Reasoning In Scheme (BORIS). Since this tool is written in Scheme, a dialect of Lisp, the parser was also written in Scheme. The parser was implemented is Artez's modification of Earley's Algorithm. Additionally, a VHDL tokenizer was implemented in Scheme and a portion of the VHDL grammar was converted into the format which the parser uses. The second task was the incorporation of intermediate functions into BORIS. The existing BORIS contains a recursive optimization system that optimizes digital circuits by using circuit outputs as inputs into other circuits. Intermediate functions provide a greater selection of functions to be used as circuits inputs. Using both intermediate functions and output functions, the costs of the circuits in the test set were reduced by 43 percent. This is a 10 percent reduction when compared to the existing recursive optimization system. Incorporating intermediate functions into BORIS required the development of an intermediate-function generator and a set of control methods to keep the computation time from increasing exponentially.
Sensitivity analysis, optimization, and global critical points
Cacuci, D.G. )
1989-11-01
The title of this paper suggests that sensitivity analysis, optimization, and the search for critical points in phase-space are somehow related; the existence of such a kinship has been undoubtedly felt by many of the nuclear engineering practitioners of optimization and/or sensitivity analysis. However, a unified framework for displaying this relationship has so far been lacking, especially in a global setting. The objective of this paper is to present such a global and unified framework and to suggest, within this framework, a new direction for future developments for both sensitivity analysis and optimization of the large nonlinear systems encountered in practical problems.
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm
NASA Astrophysics Data System (ADS)
Chakraborty, Angana; Bandyopadhyay, Sanghamitra
2013-04-01
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
Lens design: optimization with Global Explorer
NASA Astrophysics Data System (ADS)
Isshiki, Masaki
2013-02-01
The optimization method damped least squares method (DLS) was almost completed late in the 1960s. DLS has been overwhelming in the local optimization technology. After that, various efforts were made to seek the global optimization. They came into the world after 1990 and the Global Explorer (GE) was one of them invented by the author to find plural solutions, each of which has the local minimum of the merit function. The robustness of the designed lens is also an important factor as well as the performance of the lens; both of these requirements are balanced in the process of optimization with GE2 (the second version of GE). An idea is also proposed to modify GE2 for aspherical lens systems. A design example is shown.
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Global Design Optimization for Fluid Machinery Applications
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa
2000-01-01
Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.
Global optimization methods for engineering design
NASA Technical Reports Server (NTRS)
Arora, Jasbir S.
1990-01-01
The problem is to find a global minimum for the Problem P. Necessary and sufficient conditions are available for local optimality. However, global solution can be assured only under the assumption of convexity of the problem. If the constraint set S is compact and the cost function is continuous on it, existence of a global minimum is guaranteed. However, in view of the fact that no global optimality conditions are available, a global solution can be found only by an exhaustive search to satisfy Inequality. The exhaustive search can be organized in such a way that the entire design space need not be searched for the solution. This way the computational burden is reduced somewhat. It is concluded that zooming algorithm for global optimizations appears to be a good alternative to stochastic methods. More testing is needed; a general, robust, and efficient local minimizer is required. IDESIGN was used in all numerical calculations which is based on a sequential quadratic programming algorithm, and since feasible set keeps on shrinking, a good algorithm to find an initial feasible point is required. Such algorithms need to be developed and evaluated.
Electronic neural networks for global optimization
NASA Technical Reports Server (NTRS)
Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.
1990-01-01
An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.
Global Optimality of the Successive Maxbet Algorithm.
ERIC Educational Resources Information Center
Hanafi, Mohamed; ten Berge, Jos M. F.
2003-01-01
It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)
Global Optimization Ensemble Model for Classification Methods
Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab
2014-01-01
Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382
Solving global optimization problems on GPU cluster
Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya
2016-06-08
The paper contains the results of investigation of a parallel global optimization algorithm combined with a dimension reduction scheme. This allows solving multidimensional problems by means of reducing to data-independent subproblems with smaller dimension solved in parallel. The new element implemented in the research consists in using several graphic accelerators at different computing nodes. The paper also includes results of solving problems of well-known multiextremal test class GKLS on Lobachevsky supercomputer using tens of thousands of GPU cores.
Global optimization of cryogenic-optical sensors
NASA Astrophysics Data System (ADS)
Yatsenko, Vitaliy A.; Pardalos, Panos M.
2001-12-01
We describe a phenomenon in which a macroscopic superconducting probe, as large as 2 - 6 cm, is chaotically and magnetically levitated. We have found that, when feedback is used, the probe chaotically moves near an equilibrium state. The global optimization approach to highly sensitive measurement of weak signal is considered. Furthermore an accurate mathematical model of asymptotically stable estimation of a limiting weak noisy signal using the stochastic measurement model is considered.
A global optimization perspective on molecular clusters.
Marques, J M C; Pereira, F B; Llanio-Trujillo, J L; Abreu, P E; Albertí, M; Aguilar, A; Pirani, F; Bartolomei, M
2017-04-28
Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters; some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca(2+) ions with various types of solvents.This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'.
Global optimization of cholic acid aggregates
NASA Astrophysics Data System (ADS)
Jójárt, Balázs; Viskolcz, Béla; Poša, Mihalj; Fejer, Szilard N.
2014-04-01
In spite of recent investigations into the potential pharmaceutical importance of bile acids as drug carriers, the structure of bile acid aggregates is largely unknown. Here, we used global optimization techniques to find the lowest energy configurations for clusters composed between 2 and 10 cholate molecules, and evaluated the relative stabilities of the global minima. We found that the energetically most preferred geometries for small aggregates are in fact reverse micellar arrangements, and the classical micellar behaviour (efficient burial of hydrophobic parts) is achieved only in systems containing more than five cholate units. Hydrogen bonding plays a very important part in keeping together the monomers, and among the size range considered, the most stable structure was found to be the decamer, having 17 hydrogen bonds. Molecular dynamics simulations showed that the decamer has the lowest dissociation propensity among the studied aggregation numbers.
On Global Optimal Sailplane Flight Strategy
NASA Technical Reports Server (NTRS)
Sander, G. J.; Litt, F. X.
1979-01-01
The derivation and interpretation of the necessary conditions that a sailplane cross-country flight has to satisfy to achieve the maximum global flight speed is considered. Simple rules are obtained for two specific meteorological models. The first one uses concentrated lifts of various strengths and unequal distance. The second one takes into account finite, nonuniform space amplitudes for the lifts and allows, therefore, for dolphin style flight. In both models, altitude constraints consisting of upper and lower limits are shown to be essential to model realistic problems. Numerical examples illustrate the difference with existing techniques based on local optimality conditions.
LDRD Final Report: Global Optimization for Engineering Science Problems
HART,WILLIAM E.
1999-12-01
For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
Global Optimization Techniques for Fluid Flow and Propulsion Devices
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Vaidyanathan, Raj; Tucker, Kevin; Griffin, Lisa; Dorney, Dan; Huber, Frank; Tran, Ken; Turner, James E. (Technical Monitor)
2001-01-01
This viewgraph presentation gives an overview of global optimization techniques for fluid flow and propulsion devices. Details are given on the need, characteristics, and techniques for global optimization. The techniques include response surface methodology (RSM), neural networks and back-propagation neural networks, design of experiments, face centered composite design (FCCD), orthogonal arrays, outlier analysis, and design optimization.
A Collective Neurodynamic Approach to Constrained Global Optimization.
Yan, Zheng; Fan, Jianchao; Wang, Jun
2016-04-01
Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.
Global optimization strategies for high-performance controls
Hartman, T.B.
1995-12-31
The current trend of extending digital heating, ventilating, and air-conditioning (HVAC) and lighting controls to terminal devices has had an enormous impact on the role of global strategies for energy and comfort optimization. In some respects optimization algorithms are becoming simpler because more complete information about conditions throughout the building is now available to the control system. However, the task of analyzing this information often adds a new layer of complexity to the process of developing these algorithms. Also, the extension of direct digital control (DDC) to terminal devices offers new energy and comfort control optimization opportunities that require additional global optimization algorithms. This paper discusses the changing role of global optimization strategies as the integration of DDC systems is extended to terminal equipment. The discussion offers suggestions about how the development of more powerful global optimization strategies needs to be considered in the design of the mechanical equipment. Specifically, four areas of global optimization are discussed: optimization of variable-air-volume (VAV) airflow, optimization of lighting level via dimming ballasts, optimization of space temperature setpoint, and optimization of chiller and boiler operation. In each of these categories, a control philosophy employing global optimization is discussed, sample control algorithms are provided, and a discussion of the implication of these new control opportunities on the design of the mechanical components is included.
Quantum Tunneling Parameter in Global Optimization
NASA Astrophysics Data System (ADS)
Itami, Teturo
Quantum tunneling that helps particles escape from local minima has been applied in “quantum annealing” method to global optimization of nonlinear functions. To control size of kinetic energy of quantum particles, we form a “quantum tunneling parameter” QT≡m/HR2, where HR corresponds to a physical constant h, Planck's constant divided by 2π, that determines the lowest eigenvalue of quantum particles with mass m. Assumptions on profiles of the function V(x) around its minimum point x0, harmonic oscillator type and square well type, make us possible to write down analytical formulae of the kinetic energy K in terms of QT. The formulae tell that we can make quantum expectation value of particle coordinates x approximate to the minimum point x0 in QT→∞. For systems where we have almost degenerate eigenvalues, examination working with our QT, that x→x0 in QT→∞, is analytically shown also efficient. Similar results that x→x0 under QT→∞ are also obtained when we utilize random-walk quantum Monte Carlo method to represent tunneling phenomena according to conventional quantum annealing.
An approximation based global optimization strategy for structural synthesis
NASA Technical Reports Server (NTRS)
Sepulveda, A. E.; Schmit, L. A.
1991-01-01
A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.
Global smoothing and continuation for large-scale molecular optimization
More, J.J.; Wu, Zhijun
1995-10-01
We discuss the formulation of optimization problems that arise in the study of distance geometry, ionic systems, and molecular clusters. We show that continuation techniques based on global smoothing are applicable to these molecular optimization problems, and we outline the issues that must be resolved in the solution of large-scale molecular optimization problems.
Strategies for Global Optimization of Temporal Preferences
NASA Technical Reports Server (NTRS)
Morris, Paul; Morris, Robert; Khatib, Lina; Ramakrishnan, Sailesh
2004-01-01
A temporal reasoning problem can often be naturally characterized as a collection of constraints with associated local preferences for times that make up the admissible values for those constraints. Globally preferred solutions to such problems emerge as a result of well-defined operations that compose and order temporal assignments. The overall objective of this work is a characterization of different notions of global preference, and to identify tractable sub-classes of temporal reasoning problems incorporating these notions. This paper extends previous results by refining the class of useful notions of global temporal preference that are associated with problems that admit of tractable solution techniques. This paper also answers the hitherto open question of whether problems that seek solutions that are globally preferred from a Utilitarian criterion for global preference can be found tractably.
Efficient Globally Optimal Consensus Maximisation with Tree Search.
Chin, Tat-Jun; Purkait, Pulak; Eriksson, Anders; Suter, David
2017-04-01
Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising the criterion is still customarily done by randomised sample-and-test techniques, which do not guarantee optimality of the result. Several globally optimal algorithms exist, but they are too slow to challenge the dominance of randomised methods. Our work aims to change this state of affairs by proposing an efficient algorithm for global maximisation of consensus. Under the framework of LP-type methods, we show how consensus maximisation for a wide variety of vision tasks can be posed as a tree search problem. This insight leads to a novel algorithm based on A* search. We propose efficient heuristic and support set updating routines that enable A* search to efficiently find globally optimal results. On common estimation problems, our algorithm is much faster than previous exact methods. Our work identifies a promising direction for globally optimal consensus maximisation.
Applications of parallel global optimization to mechanics problems
NASA Astrophysics Data System (ADS)
Schutte, Jaco Francois
Global optimization of complex engineering problems, with a high number of variables and local minima, requires sophisticated algorithms with global search capabilities and high computational efficiency. With the growing availability of parallel processing, it makes sense to address these requirements by increasing the parallelism in optimization strategies. This study proposes three methods of concurrent processing. The first method entails exploiting the structure of population-based global algorithms such as the stochastic Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA). As a demonstration of how such an algorithm may be adapted for concurrent processing we modify and apply the PSO to several mechanical optimization problems on a parallel processing machine. Desirable PSO algorithm features such as insensitivity to design variable scaling and modest sensitivity to algorithm parameters are demonstrated. A second approach to parallelism and improving algorithm efficiency is by utilizing multiple optimizations. With this method a budget of fitness evaluations is distributed among several independent sub-optimizations in place of a single extended optimization. Under certain conditions this strategy obtains a higher combined probability of converging to the global optimum than a single optimization which utilizes the full budget of fitness evaluations. The third and final method of parallelism addressed in this study is the use of quasiseparable decomposition, which is applied to decompose loosely coupled problems. This yields several sub-problems of lesser dimensionality which may be concurrently optimized with reduced effort.
Acceleration techniques in the univariate Lipschitz global optimization
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela
2016-10-01
Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.
Modeling and Global Optimization of DNA separation
Fahrenkopf, Max A.; Ydstie, B. Erik; Mukherjee, Tamal; Schneider, James W.
2014-01-01
We develop a non-convex non-linear programming problem that determines the minimum run time to resolve different lengths of DNA using a gel-free micelle end-labeled free solution electrophoresis separation method. Our optimization framework allows for efficient determination of the utility of different DNA separation platforms and enables the identification of the optimal operating conditions for these DNA separation devices. The non-linear programming problem requires a model for signal spacing and signal width, which is known for many DNA separation methods. As a case study, we show how our approach is used to determine the optimal run conditions for micelle end-labeled free-solution electrophoresis and examine the trade-offs between a single capillary system and a parallel capillary system. Parallel capillaries are shown to only be beneficial for DNA lengths above 230 bases using a polydisperse micelle end-label otherwise single capillaries produce faster separations. PMID:24764606
Global search algorithm for optimal control
NASA Technical Reports Server (NTRS)
Brocker, D. H.; Kavanaugh, W. P.; Stewart, E. C.
1970-01-01
Random-search algorithm employs local and global properties to solve two-point boundary value problem in Pontryagin maximum principle for either fixed or variable end-time problems. Mixed boundary value problem is transformed to an initial value problem. Mapping between initial and terminal values utilizes hybrid computer.
Globally optimal trial design for local decision making.
Eckermann, Simon; Willan, Andrew R
2009-02-01
Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials.
Global optimization framework for solar building design
NASA Astrophysics Data System (ADS)
Silva, N.; Alves, N.; Pascoal-Faria, P.
2017-07-01
The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.
Global nonlinear optimization of spacecraft protective structures design
NASA Technical Reports Server (NTRS)
Mog, R. A.; Lovett, J. N., Jr.; Avans, S. L.
1990-01-01
The global optimization of protective structural designs for spacecraft subject to hypervelocity meteoroid and space debris impacts is presented. This nonlinear problem is first formulated for weight minimization of the space station core module configuration using the Nysmith impact predictor. Next, the equivalence and uniqueness of local and global optima is shown using properties of convexity. This analysis results in a new feasibility condition for this problem. The solution existence is then shown, followed by a comparison of optimization techniques. Finally, a sensitivity analysis is presented to determine the effects of variations in the systemic parameters on optimal design. The results show that global optimization of this problem is unique and may be achieved by a number of methods, provided the feasibility condition is satisfied. Furthermore, module structural design thicknesses and weight increase with increasing projectile velocity and diameter and decrease with increasing separation between bumper and wall for the Nysmith predictor.
Dispositional optimism and terminal decline in global quality of life.
Zaslavsky, Oleg; Palgi, Yuval; Rillamas-Sun, Eileen; LaCroix, Andrea Z; Schnall, Eliezer; Woods, Nancy F; Cochrane, Barbara B; Garcia, Lorena; Hingle, Melanie; Post, Stephen; Seguin, Rebecca; Tindle, Hilary; Shrira, Amit
2015-06-01
We examined whether dispositional optimism relates to change in global quality of life (QOL) as a function of either chronological age or years to impending death. We used a sample of 2,096 deceased postmenopausal women from the Women's Health Initiative clinical trials who were enrolled in the 2005-2010 Extension Study and for whom at least 1 global QOL and optimism measure were analyzed. Growth curve models were examined. Competing models were contrasted using model fit criteria. On average, levels of global QOL decreased with both higher age and closer proximity to death (e.g., M(score) = 7.7 eight years prior to death vs. M(score) = 6.1 one year prior to death). A decline in global QOL was better modeled as a function of distance to death (DtD) than as a function of chronological age (Bayesian information criterion [BIC](DtD) = 22,964.8 vs. BIC(age) = 23,322.6). Optimism was a significant correlate of both linear (estimate(DtD) = -0.01, SE(DtD) = 0.005; ρ = 0.004) and quadratic (estimate(DtD) = -0.006, SE(DtD) = 0.002; ρ = 0.004) terminal decline in global QOL so that death-related decline in global QOL was steeper among those with a high level of optimism than those with a low level of optimism. We found that dispositional optimism helps to maintain positive psychological perspective in the face of age-related decline. Optimists maintain higher QOL compared with pessimists when death-related trajectories were considered; however, the gap between those with high optimism and those with low optimism progressively attenuated with closer proximity to death, to the point that is became nonsignificant at the time of death.
A global optimization approach for Lennard-Jones microclusters
NASA Astrophysics Data System (ADS)
Maranas, Costas D.; Floudas, Christodoulos A.
1992-11-01
A global optimization approach is proposed for finding the global minimum energy configuration of Lennard-Jones microclusters. First, the original nonconvex total potential energy function, composed by rational polynomials, is transformed to the difference of two convex functions (DC transformation) via a novel procedure performed for each pair potential that constitute the total potential energy function. Then, a decomposition strategy based on the global optimization (GOP) algorithm [C. A. Floudas and V. Visweswaran, Comput. Chem. Eng. 14, 1397 (1990); V. Visweswaran and C. A. Floudas, ibid. 14, 1419 (1990); Proc. Process Systems Eng. 1991, I.6.1; C. A. Floudas and V. Visweswaran, J. Opt. Theory Appl. (in press)] is designed to provide tight bounds on the global minimum through the solutions of a sequence of relaxed dual subproblems. A number of theoretical results are included which expedite the computational effort by exploiting the special mathematical structure of the problem. The proposed approach attains ɛ convergence to the global minimum in a finite number of iterations. Based on this procedure, global optimum solutions are generated for small microclusters n≤7. For larger clusters 8≤N≤24 tight lower and upper bounds on the global solution are provided serving as excellent initial points for local optimization approaches. Finally, improved lower bounds on the minimum interparticle distance at the global minimum are provided.
Optimizing human activity patterns using global sensitivity analysis
Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2014-01-01
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations. PMID:25580080
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.
Optimizing human activity patterns using global sensitivity analysis.
Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M; Del Valle, Sara Y; Hyman, James M
2014-12-01
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.
Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; ...
2016-02-10
We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developedmore » techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.« less
Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; Huff, Joshua; Tawarmalani, Mohit; Agrawal, Rakesh
2016-02-10
We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developed techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.
Meningococcal conjugate vaccines: optimizing global impact
Terranella, Andrew; Cohn, Amanda; Clark, Thomas
2011-01-01
Meningococcal conjugate vaccines have several advantages over polysaccharide vaccines, including the ability to induce greater antibody persistence, avidity, immunologic memory, and herd immunity. Since 1999, meningococcal conjugate vaccine programs have been established across the globe. Many of these vaccination programs have resulted in significant decline in meningococcal disease in several countries. Recent introduction of serogroup A conjugate vaccine in Africa offers the potential to eliminate meningococcal disease as a public health problem in Africa. However, the duration of immune response and the development of widespread herd immunity in the population remain important questions for meningococcal vaccine programs. Because of the unique epidemiology of meningococcal disease around the world, the optimal vaccination strategy for long-term disease prevention will vary by country. PMID:22114508
Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting
Das Sarma, Akash; Parameswaran, Aditya; Widom, Jennifer
2017-01-01
We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying Expectation-Maximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management. PMID:28149000
Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting.
Das Sarma, Akash; Parameswaran, Aditya; Widom, Jennifer
2016-01-01
We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying Expectation-Maximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management.
Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories
NASA Technical Reports Server (NTRS)
Ng, Hok Kwan; Sridhar, Banavar
2016-01-01
This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
Shang, C. Wales, D. J.
2014-08-21
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Differential evolution algorithm for global optimizations in nuclear physics
NASA Astrophysics Data System (ADS)
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
Global search acceleration in the nested optimization scheme
NASA Astrophysics Data System (ADS)
Grishagin, Vladimir A.; Israfilov, Ruslan A.
2016-06-01
Multidimensional unconstrained global optimization problem with objective function under Lipschitz condition is considered. For solving this problem the dimensionality reduction approach on the base of the nested optimization scheme is used. This scheme reduces initial multidimensional problem to a family of one-dimensional subproblems being Lipschitzian as well and thus allows applying univariate methods for the execution of multidimensional optimization. For two well-known one-dimensional methods of Lipschitz optimization the modifications providing the acceleration of the search process in the situation when the objective function is continuously differentiable in a vicinity of the global minimum are considered and compared. Results of computational experiments on conventional test class of multiextremal functions confirm efficiency of the modified methods.
An Efficient Globally Optimal Algorithm for Asymmetric Point Matching.
Lian, Wei; Zhang, Lei; Yang, Ming-Hsuan
2016-08-29
Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem where each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a branch-and-bound algorithm based on rectangular subdivision where in each iteration, multiple rectangles are used to increase the chances of subdividing the one containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; ...
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimizationmore » problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less
Orbit design and optimization based on global telecommunication performance metrics
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Lee, Charles H.; Kerridge, Stuart; Cheung, Kar-Ming; Edwards, Charles D.
2006-01-01
The orbit selection of telecommunications orbiters is one of the critical design processes and should be guided by global telecom performance metrics and mission-specific constraints. In order to aid the orbit selection, we have coupled the Telecom Orbit Analysis and Simulation Tool (TOAST) with genetic optimization algorithms. As a demonstration, we have applied the developed tool to select an optimal orbit for general Mars telecommunications orbiters with the constraint of being a frozen orbit. While a typical optimization goal is to minimize tele-communications down time, several relevant performance metrics are examined: 1) area-weighted average gap time, 2) global maximum of local maximum gap time, 3) global maximum of local minimum gap time. Optimal solutions are found with each of the metrics. Common and different features among the optimal solutions as well as the advantage and disadvantage of each metric are presented. The optimal solutions are compared with several candidate orbits that were considered during the development of Mars Telecommunications Orbiter.
Orbit design and optimization based on global telecommunication performance metrics
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Lee, Charles H.; Kerridge, Stuart; Cheung, Kar-Ming; Edwards, Charles D.
2006-01-01
The orbit selection of telecommunications orbiters is one of the critical design processes and should be guided by global telecom performance metrics and mission-specific constraints. In order to aid the orbit selection, we have coupled the Telecom Orbit Analysis and Simulation Tool (TOAST) with genetic optimization algorithms. As a demonstration, we have applied the developed tool to select an optimal orbit for general Mars telecommunications orbiters with the constraint of being a frozen orbit. While a typical optimization goal is to minimize tele-communications down time, several relevant performance metrics are examined: 1) area-weighted average gap time, 2) global maximum of local maximum gap time, 3) global maximum of local minimum gap time. Optimal solutions are found with each of the metrics. Common and different features among the optimal solutions as well as the advantage and disadvantage of each metric are presented. The optimal solutions are compared with several candidate orbits that were considered during the development of Mars Telecommunications Orbiter.
Global, Multi-Objective Trajectory Optimization With Parametric Spreading
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.
2017-01-01
Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating.
A global optimization paradigm based on change of measures
Sarkar, Saikat; Roy, Debasish; Vasu, Ram Mohan
2015-01-01
A global optimization framework, COMBEO (Change Of Measure Based Evolutionary Optimization), is proposed. An important aspect in the development is a set of derivative-free additive directional terms, obtainable through a change of measures en route to the imposition of any stipulated conditions aimed at driving the realized design variables (particles) to the global optimum. The generalized setting offered by the new approach also enables several basic ideas, used with other global search methods such as the particle swarm or the differential evolution, to be rationally incorporated in the proposed set-up via a change of measures. The global search may be further aided by imparting to the directional update terms additional layers of random perturbations such as ‘scrambling’ and ‘selection’. Depending on the precise choice of the optimality conditions and the extent of random perturbation, the search can be readily rendered either greedy or more exploratory. As numerically demonstrated, the new proposal appears to provide for a more rational, more accurate and, in some cases, a faster alternative to many available evolutionary optimization schemes. PMID:26587268
Global Optimal Trajectory in Chaos and NP-Hardness
NASA Astrophysics Data System (ADS)
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Examining the Bernstein global optimization approach to optimal power flow problem
NASA Astrophysics Data System (ADS)
Patil, Bhagyesh V.; Sampath, L. P. M. I.; Krishnan, Ashok; Ling, K. V.; Gooi, H. B.
2016-10-01
This work addresses a nonconvex optimal power flow problem (OPF). We introduce a `new approach' in the context of OPF problem based on the Bernstein polynomials. The applicability of the approach is studied on a real-world 3-bus power system. The numerical results obtained with this new approach for a 3-bus system reveal a satisfactory improvement in terms of optimality. The results are found to be competent with generic global optimization solvers BARON and COUENNE.
Global dynamic optimization approach to predict activation in metabolic pathways.
de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R
2014-01-06
During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary
Global dynamic optimization approach to predict activation in metabolic pathways
2014-01-01
Background During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. Results In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. Conclusions The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
NASA Astrophysics Data System (ADS)
Basu, Mousumi
2016-12-01
Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
Endgame implementations for the Efficient Global Optimization (EGO) algorithm
NASA Astrophysics Data System (ADS)
Southall, Hugh L.; O'Donnell, Teresa H.; Kaanta, Bryan
2009-05-01
Efficient Global Optimization (EGO) is a competent evolutionary algorithm which can be useful for problems with expensive cost functions [1,2,3,4,5]. The goal is to find the global minimum using as few function evaluations as possible. Our research indicates that EGO requires far fewer evaluations than genetic algorithms (GAs). However, both algorithms do not always drill down to the absolute minimum, therefore the addition of a final local search technique is indicated. In this paper, we introduce three "endgame" techniques. The techniques can improve optimization efficiency (fewer cost function evaluations) and, if required, they can provide very accurate estimates of the global minimum. We also report results using a different cost function than the one previously used [2,3].
Obstetricians’ Opinions of the Optimal Caesarean Rate: A Global Survey
Cavallaro, Francesca L.; Cresswell, Jenny A.; Ronsmans, Carine
2016-01-01
Background The debate surrounding the optimal caesarean rate has been ongoing for several decades, with the WHO recommending an “acceptable” rate of 5–15% since 1997, despite a weak evidence base. Global expert opinion from obstetric care providers on the optimal caesarean rate has not been documented. The objective of this study was to examine providers’ opinions of the optimal caesarean rate worldwide, among all deliveries and within specific sub-groups of deliveries. Methods A global online survey of medical doctors who had performed at least one caesarean in the last five years was conducted between August 2013 and January 2014. Respondents were asked to report their opinion of the optimal caesarean rate—defined as the caesarean rate that would minimise poor maternal and perinatal outcomes—at the population level and within specific sub-groups of deliveries (including women with demographic and clinical risk factors for caesareans). Median reported optimal rates and corresponding inter-quartile ranges (IQRs) were calculated for the sample, and stratified according to national caesarean rate, institutional caesarean rate, facility level, and respondent characteristics. Results Responses were collected from 1,057 medical doctors from 96 countries. The median reported optimal caesarean rate was 20% (IQR: 15–30%) for all deliveries. Providers in private for-profit facilities and in facilities with high institutional rates reported optimal rates of 30% or above, while those in Europe, in public facilities and in facilities with low institutional rates reported rates of 15% or less. Reported optimal rates were lowest among low-risk deliveries and highest for Absolute Maternal Indications (AMIs), with wide IQRs observed for most categories other than AMIs. Conclusions Three-quarters of respondents reported an optimal caesarean rate above the WHO 15% upper threshold. There was substantial variation in responses, highlighting a lack of consensus around
Neoliberal Optimism: Applying Market Techniques to Global Health.
Mei, Yuyang
2016-09-23
Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.
Automated parameterization of intermolecular pair potentials using global optimization techniques
NASA Astrophysics Data System (ADS)
Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk
2014-12-01
In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.
Globally Optimal Segmentation of Permanent-Magnet Systems
NASA Astrophysics Data System (ADS)
Insinga, A. R.; Bjørk, R.; Smith, A.; Bahl, C. R. H.
2016-06-01
Permanent-magnet systems are widely used for generation of magnetic fields with specific properties. The reciprocity theorem, an energy-equivalence principle in magnetostatics, can be employed to calculate the optimal remanent flux density of the permanent-magnet system, given any objective functional that is linear in the magnetic field. This approach, however, yields a continuously varying remanent flux density, while in practical applications, magnetic assemblies are realized by combining uniformly magnetized segments. The problem of determining the optimal shape of each of these segments remains unsolved. We show that the problem of optimal segmentation of a two-dimensional permanent-magnet assembly with respect to a linear objective functional can be reduced to the problem of piecewise linear approximation of a plane curve by perimeter maximization. Once the problem has been cast into this form, the globally optimal solution can be easily computed employing dynamic programming.
Tabu search method with random moves for globally optimal design
NASA Astrophysics Data System (ADS)
Hu, Nanfang
1992-09-01
Optimum engineering design problems are usually formulated as non-convex optimization problems of continuous variables. Because of the absence of convexity structure, they can have multiple minima, and global optimization becomes difficult. Traditional methods of optimization, such as penalty methods, can often be trapped at a local optimum. The tabu search method with random moves to solve approximately these problems is introduced. Its reliability and efficiency are examined with the help of standard test functions. By the analysis of the implementations, it is seen that this method is easy to use, and no derivative information is necessary. It outperforms the random search method and composite genetic algorithm. In particular, it is applied to minimum weight design examples of a three-bar truss, coil springs, a Z-section and a channel section. For the channel section, the optimal design using the tabu search method with random moves saved 26.14 percent over the weight of the SUMT method.
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)
2000-01-01
Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design
A global optimization approach to multi-polarity sentiment analysis.
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
p-MEMPSODE: Parallel and irregular memetic global optimization
NASA Astrophysics Data System (ADS)
Voglis, C.; Hadjidoukas, P. E.; Parsopoulos, K. E.; Papageorgiou, D. G.; Lagaris, I. E.; Vrahatis, M. N.
2015-12-01
A parallel memetic global optimization algorithm suitable for shared memory multicore systems is proposed and analyzed. The considered algorithm combines two well-known and widely used population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with two efficient and parallelizable local search procedures. The sequential version of the algorithm was first introduced as MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution) and published in the CPC program library. We exploit the inherent and highly irregular parallelism of the memetic global optimization algorithm by means of a dynamic and multilevel approach based on the OpenMP tasking model. In our case, tasks correspond to local optimization procedures or simple function evaluations. Parallelization occurs at each iteration step of the memetic algorithm without affecting its searching efficiency. The proposed implementation, for the same random seed, reaches the same solution irrespectively of being executed sequentially or in parallel. Extensive experimental evaluation has been performed in order to illustrate the speedup achieved on a shared-memory multicore server.
Efficient global optimization of a limited parameter antenna design
NASA Astrophysics Data System (ADS)
O'Donnell, Teresa H.; Southall, Hugh L.; Kaanta, Bryan
2008-04-01
Efficient Global Optimization (EGO) is a competent evolutionary algorithm suited for problems with limited design parameters and expensive cost functions. Many electromagnetics problems, including some antenna designs, fall into this class, as complex electromagnetics simulations can take substantial computational effort. This makes simple evolutionary algorithms such as genetic algorithms or particle swarms very time-consuming for design optimization, as many iterations of large populations are usually required. When physical experiments are necessary to perform tradeoffs or determine effects which may not be simulated, use of these algorithms is simply not practical at all due to the large numbers of measurements required. In this paper we first present a brief introduction to the EGO algorithm. We then present the parasitic superdirective two-element array design problem and results obtained by applying EGO to obtain the optimal element separation and operating frequency to maximize the array directivity. We compare these results to both the optimal solution and results obtained by performing a similar optimization using the Nelder-Mead downhill simplex method. Our results indicate that, unlike the Nelder-Mead algorithm, the EGO algorithm did not become stuck in local minima but rather found the area of the correct global minimum. However, our implementation did not always drill down into the precise minimum and the addition of a local search technique seems to be indicated.
Comments upon the usage of derivatives in Lipschitz global optimization
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.
2016-06-01
An optimization problem is considered where the objective function f (x) is black-box and multiextremal and the information about its gradient ∇ f (x) is available during the search. It is supposed that ∇ f (x) satisfies the Lipschitz condition over the admissible hyperinterval with an unknown Lipschitz constant K. Some numerical Lipschitz global optimization methods based on geometric ideas with the usage of different estimates of the Lipschitz constant K are presented. Results of their systematic experimental investigation are reported and commented on.
Imperialist competitive algorithm combined with chaos for global optimization
NASA Astrophysics Data System (ADS)
Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.
2012-03-01
A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions.
Global optimization using the y-ybar diagram
NASA Astrophysics Data System (ADS)
Brown, Daniel M.
1991-12-01
Software is under development at Teledyne Brown Engineering to represent a lens configuration as a y-ybar or Delano diagram. The program determines third-order Seidel and chromatic aberrations for each configuration. It performs a global search through all valid permutations of configuration space and determines, to within a step increment of the space, the configuration with smallest third-order aberrations. The program was developed to generate first-order optical layouts which promised to reach global minima during subsequent conventional optimization. Other operations allowed by the program are: add or delete surfaces, couple surfaces (for Mangin mirrors), shift the stop position, and display first-order properties and the optical layout (surface radii and thicknesses) for subsequent entry into a conventional lens-design program with automatic optimization. Algorithms for performing some of the key functions, not covered by previous authors, are discussed in this paper.
Multi-fidelity global design optimization including parallelization potential
NASA Astrophysics Data System (ADS)
Cox, Steven Edward
The DIRECT global optimization algorithm is a relatively new space partitioning algorithm designed to determine the globally optimal design within a designated design space. This dissertation examines the applicability of the DIRECT algorithm to two classes of design problems: unimodal functions where small amplitude, high frequency fluctuations in the objective function make optimization difficult; and multimodal functions where multiple local optima are formed by the underlying physics of the problem (as opposed to minor fluctuations in the analysis code). DIRECT is compared with two other multistart local optimization techniques on two polynomial test problems and one engineering conceptual design problem. Three modifications to the DIRECT algorithm are proposed to increase the effectiveness of the algorithm. The DIRECT-BP algorithm is presented which alters the way DIRECT searches the neighborhood of the current best point as optimization progresses. The algorithm reprioritizes which points to analyze at each iteration. This is to encourage analysis of points that surround the best point but that are farther away than the points selected by the DIRECT algorithm. This increases the robustness of the DIRECT search and provides more information on the characteristics of the neighborhood of the point selected as the global optimum. A multifidelity version of the DIRECT algorithm is proposed to reduce the cost of optimization using DIRECT. By augmenting expensive high-fidelity analysis with cheap low-fidelity analysis, the optimization can be performed with fewer high-fidelity analyses. Two correction schemes are examined using high- and low-fidelity results at one point to correct the low-fidelity result at a nearby point. This corrected value is then used in place of a high-fidelity analysis by the DIRECT algorithm. In this way the number of high-fidelity analyses required is reduced and the optimization became less expensive. Finally the DIRECT algorithm is
Multidisciplinary optimization of controlled space structures with global sensitivity equations
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.
1991-01-01
A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.
Proposal of Evolutionary Simplex Method for Global Optimization Problem
NASA Astrophysics Data System (ADS)
Shimizu, Yoshiaki
To make an agile decision in a rational manner, role of optimization engineering has been notified increasingly under diversified customer demand. With this point of view, in this paper, we have proposed a new evolutionary method serving as an optimization technique in the paradigm of optimization engineering. The developed method has prospects to solve globally various complicated problem appearing in real world applications. It is evolved from the conventional method known as Nelder and Mead’s Simplex method by virtue of idea borrowed from recent meta-heuristic method such as PSO. Mentioning an algorithm to handle linear inequality constraints effectively, we have validated effectiveness of the proposed method through comparison with other methods using several benchmark problems.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well. PMID:26421005
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
The protein folding problem: global optimization of the force fields.
Scheraga, H A; Liwo, A; Oldziej, S; Czaplewski, C; Pillardy, J; Ripoll, D R; Vila, J A; Kazmierkiewicz, R; Saunders, J A; Arnautova, Y A; Jagielska, A; Chinchio, M; Nanias, M
2004-09-01
The evolutionary development of a theoretical approach to the protein folding problem, in our laboratory, is traced. The theoretical foundations and the development of a suitable empirical all-atom potential energy function and a global optimization search are examined. Whereas the all-atom approach has thus far succeeded for relatively small molecules and for alpha-helical proteins containing up to 46 residues, it has been necessary to develop a hierarchical approach to treat larger proteins. In the hierarchical approach to single- and multiple-chain proteins, global optimization is carried out for a simplified united residue (UNRES) description of a polypeptide chain to locate the region in which the global minimum lies. Conversion of the UNRES structures in this region to all-atom structures is followed by a local search in this region. The performance of this approach in successive CASP blind tests for predicting protein structure by an ab initio physics-based method is described. Finally, a recent attempt to compute a folding pathway is discussed.
STP: A Stochastic Tunneling Algorithm for Global Optimization
Oblow, E.M.
1999-05-20
A stochastic approach to solving continuous function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen et al, by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.
Multi-objective global optimization for hydrologic models
NASA Astrophysics Data System (ADS)
Yapo, Patrice Ogou; Gupta, Hoshin Vijai; Sorooshian, Soroosh
1998-01-01
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.
A Novel Hybrid Firefly Algorithm for Global Optimization
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
2016-01-01
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869
A Global Optimization Approach to Multi-Polarity Sentiment Analysis
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
Finding multiple reaction pathways via global optimization of action
NASA Astrophysics Data System (ADS)
Lee, Juyong; Lee, In-Ho; Joung, Insuk; Lee, Jooyoung; Brooks, Bernard R.
2017-05-01
Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes.
Practical strategy for global optimization of zoom lenses
NASA Astrophysics Data System (ADS)
Kuper, Thomas G.; Harris, Thomas I.
1998-09-01
The effectiveness of global optimizers for non-zoomed lenses has been steadily improving, but until recently their application to zoom lens design has been less successful. Although some methods have been able to make minor improvements to initial design forms, the algorithms have not consistently discovered new solutions with different group power distributions in a single run. In many cases, the difficulty appears related to how effective focal length (EFL) is controlled across zoom positions. Improvements made to the Global SynthesisTM (GS) algorithm in Code VTM, together with a revised strategy for controlling the EFL via weighted constraints, have significantly improved the ability of GS to discover distinct zoom lens solutions, including those with different group powers. We offer a plausible explanation for the success of these changes, and we discuss an example zoom lens design problem based on a 2-group, 7-element patent design.
Remarks on global optimization using space-filling curves
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav
2016-10-01
The problem of finding the global minimum of a real function on a set S ⊆ RN occurs in many real world problems. In this paper, the global optimization problem with a multiextremal objective function satisfying the Lipschitz condition over a hypercube is considered. We propose a local tuning technique that adaptively estimates the local Lipschitz constants over different zones of the search region and a technique, called the local improvement, in order to accelerate the search. Peano-type space-filling curves for reduction of the dimension of the problem are used. Convergence condition are given. Numerical experiments executed on several hundreds of test functions show quite a promising performance of the introduced acceleration techniques.
Solving Globally-Optimal Threading Problems in ''Polynomial-Time''
Uberbacher, E.C.; Xu, D.; Xu, Y.
1999-04-12
Computational protein threading is a powerful technique for recognizing native-like folds of a protein sequence from a protein fold database. In this paper, we present an improved algorithm (over our previous work) for solving the globally-optimal threading problem, and illustrate how the computational complexity and the fold recognition accuracy of the algorithm change as the cutoff distance for pairwise interactions changes. For a given fold of m residues and M core secondary structures (or simply cores) and a protein sequence of n residues, the algorithm guarantees to find a sequence-fold alignment (threading) that is globally optimal, measured collectively by (1) the singleton match fitness, (2) pairwise interaction preference, and (3) alignment gap penalties, in O(mn + MnN{sup 1.5C-1}) time and O(mn + nN{sup C-1}) space. C, the topological complexity of a fold as we term, is a value which characterizes the overall structure of the considered pairwise interactions in the fold, which are typically determined by a specified cutoff distance between the beta carbon atoms of a pair of amino acids in the fold. C is typically a small positive integer. N represents the maximum number of possible alignments between an individual core of the fold and the protein sequence when its neighboring cores are already aligned, and its value is significantly less than n. When interacting amino acids are required to see each other, C is bounded from above by a small integer no matter how large the cutoff distance is. This indicates that the protein threading problem is polynomial-time solvable if the condition of seeing each other between interacting amino acids is sufficient for accurate fold recognition. A number of extensions have been made to our basic threading algorithm to allow finding a globally-optimal threading under various constraints, which include consistencies with (1) specified secondary structures (both cores and loops), (2) disulfide bonds, (3) active sites, etc.
PROSPECT: A Computer System for Globally-Optimal Threading
Xu, D.; Xu, Y.
1999-08-06
This paper presents a new computer system, PROSPECT, for protein threading. PROSPECT employs an energy function that consists of three additive terms: (1) a singleton fitness term, (2) a distance-dependent pairwise-interaction preference term, and (3) alignment gap penalty; and currently uses FSSP as its threading template database. PROSPECT uses a divide-and-conquer algorithm to find an alignment between a query protein sequence and a protein fold template, which is guaranteed to be globally optimal for its energy function. The threading algorithm presented here significantly improves the computational efficiency of our previously-published algorithm, which makes PROSPECT a practical tool even for large protein threading problems. Mathematically, PROSPECT finds a globally-optimal threading between a query sequence of n residues and a fold template of m residues and M core secondary structures in O(nm + MnN{sup 1.5C{minus}1}) time and O(nm + nN{sup C{minus}1}) space, where C, the topological complexity of the template fold as we term, is a value which characterizes the overall structure of the considered pairwise interactions in the fold; and N represents the maximum number of possible alignments between an individual core of the fold and the query sequence when its neighboring cores are already aligned. PROSPECT allows a user to incorporate known biological constraints about the query sequence during the threading process. For given constraints, the system finds a globally-optimal threading which satisfies the constraints. Currently PROSPECT can deal with constraints which reflect geometrical relationships among residues of disulfide bonds, active sites, or determined by the NOE constraints of (low-resolution) NMR spectral data.
Design and global optimization of high-efficiency thermophotovoltaic systems.
Bermel, Peter; Ghebrebrhan, Michael; Chan, Walker; Yeng, Yi Xiang; Araghchini, Mohammad; Hamam, Rafif; Marton, Christopher H; Jensen, Klavs F; Soljačić, Marin; Joannopoulos, John D; Johnson, Steven G; Celanovic, Ivan
2010-09-13
Despite their great promise, small experimental thermophotovoltaic (TPV) systems at 1000 K generally exhibit extremely low power conversion efficiencies (approximately 1%), due to heat losses such as thermal emission of undesirable mid-wavelength infrared radiation. Photonic crystals (PhC) have the potential to strongly suppress such losses. However, PhC-based designs present a set of non-convex optimization problems requiring efficient objective function evaluation and global optimization algorithms. Both are applied to two example systems: improved micro-TPV generators and solar thermal TPV systems. Micro-TPV reactors experience up to a 27-fold increase in their efficiency and power output; solar thermal TPV systems see an even greater 45-fold increase in their efficiency (exceeding the Shockley-Quiesser limit for a single-junction photovoltaic cell).
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Global optimization of silicon photovoltaic cell front coatings.
Ghebrebrhan, Michael; Bermel, Peter; Avniel, Yehuda; Joannopoulos, John D; Johnson, Steven G
2009-04-27
The front-coating (FC) of a solar cell controls its efficiency, determining admission of light into the absorbing material and potentially trapping light to enhance thin absorbers. Single-layer FC designs are well known, especially for thick absorbers where their only purpose is to reduce reflections. Multilayer FCs could improve performance, but require global optimization to design. For narrow bandwidths, one can always achieve nearly 100% absorption. For the entire solar bandwidth, however, a second FC layer improves performance by 6.1% for 256 microm wafer-based cells, or by 3.6% for 2 microm thin-film cells, while additional layers yield rapidly diminishing returns.
Reliability-based design optimization using efficient global reliability analysis.
Bichon, Barron J.; Mahadevan, Sankaran; Eldred, Michael Scott
2010-05-01
Finding the optimal (lightest, least expensive, etc.) design for an engineered component that meets or exceeds a specified level of reliability is a problem of obvious interest across a wide spectrum of engineering fields. Various methods for this reliability-based design optimization problem have been proposed. Unfortunately, this problem is rarely solved in practice because, regardless of the method used, solving the problem is too expensive or the final solution is too inaccurate to ensure that the reliability constraint is actually satisfied. This is especially true for engineering applications involving expensive, implicit, and possibly nonlinear performance functions (such as large finite element models). The Efficient Global Reliability Analysis method was recently introduced to improve both the accuracy and efficiency of reliability analysis for this type of performance function. This paper explores how this new reliability analysis method can be used in a design optimization context to create a method of sufficient accuracy and efficiency to enable the use of reliability-based design optimization as a practical design tool.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Optimizing a global alignment of protein interaction networks
Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie
2013-01-01
Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352
Zhang, Yong-Feng; Chiang, Hsiao-Dong
2016-06-20
A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.
D'Helon, CD
2004-08-18
The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
2004-12-07
Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
Fast globally optimal single surface segmentation using regional properties
NASA Astrophysics Data System (ADS)
Dou, Xin; Wu, Xiaodong
2010-03-01
Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image analysis applications. Intra-class variance has been successfully utilized, for instance, in the Chan-Vese model especially for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume) bounded by a smooth terrain-like surface, whose intra-class variance is minimized. A novel polynomial time algorithm is developed. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence of O(n) maximum flows in the derived graphs, where n is the size of the input image. Our further investigation shows that those O(n) graphs form a monotone parametric flow network, which enables to solving the optimal region detection problem in the complexity of computing a single maximum flow. The method has been validated on computer-synthetic volumetric images. Its applicability to clinical data sets was demonstrated on 20 3-D airway wall CT images from 6 subjects. The achieved results were highly accurate. The mean unsigned surface positioning error of outer walls of the tubes is 0.258 +/- 0.297mm, given a voxel size of 0.39 x 0.39 x 0.6mm3.
Combining global and local parallel optimization for medical image registration
NASA Astrophysics Data System (ADS)
Wachowiak, Mark P.; Peters, Terry M.
2005-04-01
Optimization is an important component in linear and nonlinear medical image registration. While common non-derivative approaches such as Powell's method are accurate and efficient, they cannot easily be adapted for parallel hardware. In this paper, new optimization strategies are proposed for parallel, shared-memory (SM) architectures. The Dividing Rectangles (DIRECT) global method is combined with the local Generalized Pattern Search (GPS) and Multidirectional Search (MDS) and to improve efficiency on multiprocessor systems. These methods require no derivatives, and can be used with all similarity metrics. In a multiresolution framework, DIRECT is performed with relaxed convergence criteria, followed by local refinement with MDS or GPS. In 3D-3D MRI rigid registration of simulated MS lesion volumes to normal brains with varying noise levels, DIRECT/MDS had the highest success rate, followed by DIRECT/GPS. DIRECT/GPS was the most efficient (5--10 seconds with 8 CPUs, and 10--20 seconds with 4 CPUs). DIRECT followed by MDS or GPS greatly increased efficiency while maintaining accuracy. Powell's method generally required more than 30 seconds (1 CPU) with a low success rate (0.3 or lower). This work indicates that parallel optimization on shared memory systems can markedly improve registration speed and accuracy, particularly for large initial misorientations.
NASA Astrophysics Data System (ADS)
Hamza, Karim; Shalaby, Mohamed
2014-09-01
This article presents a framework for simulation-based design optimization of computationally expensive problems, where economizing the generation of sample designs is highly desirable. One popular approach for such problems is efficient global optimization (EGO), where an initial set of design samples is used to construct a kriging model, which is then used to generate new 'infill' sample designs at regions of the search space where there is high expectancy of improvement. This article attempts to address one of the limitations of EGO, where generation of infill samples can become a difficult optimization problem in its own right, as well as allow the generation of multiple samples at a time in order to take advantage of parallel computing in the evaluation of the new samples. The proposed approach is tested on analytical functions, and then applied to the vehicle crashworthiness design of a full Geo Metro model undergoing frontal crash conditions.
Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.
Dash, Tirtharaj; Sahu, Prabhat K
2015-05-30
The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.
WFH: closing the global gap--achieving optimal care.
Skinner, Mark W
2012-07-01
For 50 years, the World Federation of Hemophilia (WFH) has been working globally to close the gap in care and to achieve Treatment for All patients, men and women, with haemophilia and other inherited bleeding disorders, regardless of where they might live. The WFH estimates that more than one in 1000 men and women has a bleeding disorder equating to 6,900,000 worldwide. To close the gap in care between developed and developing nations a continued focus on the successful strategies deployed heretofore will be required. However, in response to the rapid advances in treatment and emerging therapeutic advances on the horizon it will also require fresh approaches and renewed strategic thinking. It is difficult to predict what each therapeutic advance on the horizon will mean for the future, but there is no doubt that we are in a golden age of research and development, which has the prospect of revolutionizing treatment once again. An improved understanding of "optimal" treatment is fundamental to the continued evolution of global care. The challenges of answering government and payer demands for evidence-based medicine, and cost justification for the introduction and enhancement of treatment, are ever-present and growing. To sustain and improve care it is critical to build the body of outcome data for individual patients, within haemophilia treatment centers (HTCs), nationally, regionally and globally. Emerging therapeutic advances (longer half-life therapies and gene transfer) should not be justified or brought to market based only on the notion that they will be economically more affordable, although that may be the case, but rather more importantly that they will be therapeutically more advantageous. Improvements in treatment adherence, reductions in bleeding frequency (including microhemorrhages), better management of trough levels, and improved health outcomes (including quality of life) should be the foremost considerations. As part of a new WFH strategic plan
Quantum-inspired immune clonal algorithm for global optimization.
Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong
2008-10-01
Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
A practical globalization of one-shot optimization for optimal design of tokamak divertors
NASA Astrophysics Data System (ADS)
Blommaert, Maarten; Dekeyser, Wouter; Baelmans, Martine; Gauger, Nicolas R.; Reiter, Detlev
2017-01-01
In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes only state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved.
A practical globalization of one-shot optimization for optimal design of tokamak divertors
Blommaert, Maarten; Dekeyser, Wouter; Baelmans, Martine; Gauger, Nicolas R.; Reiter, Detlev
2017-01-01
In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes only state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved.
Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be
Optimizing global liver function in radiation therapy treatment planning
NASA Astrophysics Data System (ADS)
Wu, Victor W.; Epelman, Marina A.; Wang, Hesheng; Romeijn, H. Edwin; Feng, Mary; Cao, Yue; Ten Haken, Randall K.; Matuszak, Martha M.
2016-09-01
Liver stereotactic body radiation therapy (SBRT) patients differ in both pre-treatment liver function (e.g. due to degree of cirrhosis and/or prior treatment) and radiosensitivity, leading to high variability in potential liver toxicity with similar doses. This work investigates three treatment planning optimization models that minimize risk of toxicity: two consider both voxel-based pre-treatment liver function and local-function-based radiosensitivity with dose; one considers only dose. Each model optimizes different objective functions (varying in complexity of capturing the influence of dose on liver function) subject to the same dose constraints and are tested on 2D synthesized and 3D clinical cases. The normal-liver-based objective functions are the linearized equivalent uniform dose (\\ell \\text{EUD} ) (conventional ‘\\ell \\text{EUD} model’), the so-called perfusion-weighted \\ell \\text{EUD} (\\text{fEUD} ) (proposed ‘fEUD model’), and post-treatment global liver function (GLF) (proposed ‘GLF model’), predicted by a new liver-perfusion-based dose-response model. The resulting \\ell \\text{EUD} , fEUD, and GLF plans delivering the same target \\ell \\text{EUD} are compared with respect to their post-treatment function and various dose-based metrics. Voxel-based portal venous liver perfusion, used as a measure of local function, is computed using DCE-MRI. In cases used in our experiments, the GLF plan preserves up to 4.6 % ≤ft(7.5 % \\right) more liver function than the fEUD (\\ell \\text{EUD} ) plan does in 2D cases, and up to 4.5 % ≤ft(5.6 % \\right) in 3D cases. The GLF and fEUD plans worsen in \\ell \\text{EUD} of functional liver on average by 1.0 Gy and 0.5 Gy in 2D and 3D cases, respectively. Liver perfusion information can be used during treatment planning to minimize the risk of toxicity by improving expected GLF; the degree of benefit varies with perfusion pattern. Although fEUD model optimization is computationally inexpensive and
Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka
2013-01-01
Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelievre, P. G.; Bijani, R.; Farquharson, C. G.
2015-12-01
Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.
Automatic Construction and Global Optimization of a Multisentiment Lexicon
Zhang, Zhongqiu; Mo, Yuting; Li, Lianbei
2016-01-01
Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent. PMID:28042290
Automatic Construction and Global Optimization of a Multisentiment Lexicon.
Yang, Xiaoping; Zhang, Zhongxia; Zhang, Zhongqiu; Mo, Yuting; Li, Lianbei; Yu, Li; Zhu, Peican
2016-01-01
Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.
A global optimization algorithm for simulation-based problems via the extended DIRECT scheme
NASA Astrophysics Data System (ADS)
Liu, Haitao; Xu, Shengli; Wang, Xiaofang; Wu, Junnan; Song, Yang
2015-11-01
This article presents a global optimization algorithm via the extension of the DIviding RECTangles (DIRECT) scheme to handle problems with computationally expensive simulations efficiently. The new optimization strategy improves the regular partition scheme of DIRECT to a flexible irregular partition scheme in order to utilize information from irregular points. The metamodelling technique is introduced to work with the flexible partition scheme to speed up the convergence, which is meaningful for simulation-based problems. Comparative results on eight representative benchmark problems and an engineering application with some existing global optimization algorithms indicate that the proposed global optimization strategy is promising for simulation-based problems in terms of efficiency and accuracy.
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.
Sorribas, Albert; Pozo, Carlos; Vilaprinyo, Ester; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Alves, Rui
2010-09-01
Cells are natural factories that can adapt to changes in external conditions. Their adaptive responses to specific stress situations are a result of evolution. In theory, many alternative sets of coordinated changes in the activity of the enzymes of each pathway could allow for an appropriate adaptive readjustment of metabolism in response to stress. However, experimental and theoretical observations show that actual responses to specific changes follow fairly well defined patterns that suggest an evolutionary optimization of that response. Thus, it is important to identify functional effectiveness criteria that may explain why certain patterns of change in cellular components and activities during adaptive response have been preferably maintained over evolutionary time. Those functional effectiveness criteria define sets of physiological requirements that constrain the possible adaptive changes and lead to different operation principles that could explain the observed response. Understanding such operation principles can also facilitate biotechnological and metabolic engineering applications. Thus, developing methods that enable the analysis of cellular responses from the perspective of identifying operation principles may have strong theoretical and practical implications. In this paper we present one such method that was designed based on nonlinear global optimization techniques. Our methodology can be used with a special class of nonlinear kinetic models known as GMA models and it allows for a systematic characterization of the physiological requirements that may underlie the evolution of adaptive strategies.
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods. PMID:27057157
Fournier, René; Mohareb, Amir
2016-01-14
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of CumSnn (+) (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each CumSnn (+) species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV.
NASA Astrophysics Data System (ADS)
Fournier, René; Mohareb, Amir
2016-01-01
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of Cu m Snn + (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each Cu m S nn + species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV.
Optimization Case Study: ISR Allocation in the Global Force Management Process
2016-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release. Distribution is unlimited. OPTIMIZATION ...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE OPTIMIZATION CASE STUDY: ISR ALLOCATION IN THE GLOBAL FORCE MANAGEMENT PROCESS 5...provided by the military services to Geographic Combatant Commander requirements. This thesis is a proof of concept for an optimization model that
Jørgensen, Mathias S; Groves, Michael N; Hammer, Bjørk
2017-03-14
Predicting structures at the atomic scale is of great importance for understanding the properties of materials. Such predictions are infeasible without efficient global optimization techniques. Many current techniques produce a large amount of idle intermediate data before converging to the global minimum. If this information could be analyzed during optimization, many new possibilities emerge for more rational search algorithms. We combine an evolutionary algorithm (EA) and clustering, a machine-learning technique, to produce a rational algorithm for global structure optimization. Clustering the configuration space of intermediate structures into regions of geometrically similar structures enables the EA to suppress certain regions and favor others. For two test systems, an organic molecule and an oxide surface, the global minimum search proves significantly faster when favoring stable structures in unexplored regions. This clustering-enhanced EA is a step toward adaptive global optimization techniques that can act upon information in accumulated data.
Theory and Algorithms for Global/Local Design Optimization
2005-09-29
algorithm with memory for optimal design of laminated sandwich composite panels ", Composite Structures, 58 (2002) 513-520. V. B. Gantovnik, Z. Giirdal, L...34, AIAA J., 43 (2005) 1844-1849. D. B. Adams, L. T. Watson, and Z. Gilrdal, " Optimization and blending of composite laminates using genetic algorithms ...Anderson-Cook, " Genetic algorithm optimization and blending of composite laminates by locally
Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints
NASA Technical Reports Server (NTRS)
Englander, Jacob A.; Vavrina, Matthew A.; Hinckley, David
2016-01-01
Low-thrust interplanetary space missions are highly complex and there can be many locally optimal solutions. While several techniques exist to search for globally optimal solutions to low-thrust trajectory design problems, they are typically limited to unconstrained trajectories. The operational design community in turn has largely avoided using such techniques and has primarily focused on accurate constrained local optimization combined with grid searches and intuitive design processes at the expense of efficient exploration of the global design space. This work is an attempt to bridge the gap between the global optimization and operational design communities by presenting a mathematical framework for global optimization of low-thrust trajectories subject to complex constraints including the targeting of planetary landing sites, a solar range constraint to simplify the thermal design of the spacecraft, and a real-world multi-thruster electric propulsion system that must switch thrusters on and off as available power changes over the course of a mission.
Local versus global optimal sports techniques in a group of athletes.
Huchez, Aurore; Haering, Diane; Holvoët, Patrice; Barbier, Franck; Begon, Mickael
2015-01-01
Various optimization algorithms have been used to achieve optimal control of sports movements. Nevertheless, no local or global optimization algorithm could be the most effective for solving all optimal control problems. This study aims at comparing local and global optimal solutions in a multistart gradient-based optimization by considering actual repetitive performances of a group of athletes performing a transition move on the uneven bars. Twenty-four trials by eight national-level female gymnasts were recorded using a motion capture system, and then multistart sequential quadratic programming optimizations were performed to obtain global optimal, local optimal and suboptimal solutions. The multistart approach combined with a gradient-based algorithm did not often find the local solution to be the best and proposed several other solutions including global optimal and suboptimal techniques. The qualitative change between actual and optimal techniques provided three directions for training: to increase hip flexion-abduction, to transfer leg and arm angular momentum to the trunk and to straighten hand path to the bar.
Global optimization using homotopy with 2-step predictor-corrector method
NASA Astrophysics Data System (ADS)
Chang, Kerk Lee; Ahmad, Rohanin Bt.
2014-06-01
In this research, we suggest a new method for solving global optimization problem by improving Homotopy Optimization with Perturbations and Ensembles (HOPE) method. Our new method, named as Homotopy 2-Step Predictor-corrector Method (HSPM) is based on the intermediate Value Theorem (IVT) coupled with modified Predictor-Corrector Halley method (PCH) for solving global optimization problem. HSPM does not require a good initial guess since it contains the element of homotopy, which is a globally convergent method. This paper discusses the time complexity of the new algorithm, which makes it more efficient than HOPE.
Optimal Detection of Global Warming using Temperature Profiles
NASA Technical Reports Server (NTRS)
Leroy, Stephen S.
1997-01-01
Optimal fingerprinting is applied to estimate the amount of time it would take to detect warming by increased concentrations of carbon dioxide in monthly averages of temperature profiles over the Indian Ocean.
Optimal Detection of Global Warming using Temperature Profiles
NASA Technical Reports Server (NTRS)
Leroy, Stephen S.
1997-01-01
Optimal fingerprinting is applied to estimate the amount of time it would take to detect warming by increased concentrations of carbon dioxide in monthly averages of temperature profiles over the Indian Ocean.
On a global aerodynamic optimization of a civil transport aircraft
NASA Technical Reports Server (NTRS)
Savu, G.; Trifu, O.
1991-01-01
An aerodynamic optimization procedure developed to minimize the drag to lift ratio of an aircraft configuration: wing - body - tail, in accordance with engineering restrictions, is described. An algorithm developed to search a hypersurface with 18 dimensions, which define an aircraft configuration, is discussed. The results, when considered from the aerodynamic point of view, indicate the optimal configuration is one that combines a lifting fuselage with a canard.
A Global Optimization Algorithm Using Stochastic Differential Equations.
1985-02-01
Bari (Italy).2Istituto di Fisica , 2 UniversitA di Roma "Tor Vergata", Via Orazio Raimondo, 00173 (La Romanina) Roma (Italy). 3Istituto di Matematica ...accompanying Algorithm. lDipartininto di Matematica , Universita di Bari, 70125 Bar (Italy). Istituto di Fisica , 2a UniversitA di Roim ’"Tor Vergata", Via...Optimization, Stochastic Differential Equations Work Unit Number 5 (Optimization and Large Scale Systems) 6Dipartimento di Matematica , Universita di Bari, 70125
Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval.
Zhang, P G; Yang, C L; Xu, Z H; Cao, Z L; Mu, Q Q; Xuan, L
2016-10-31
The core problem of phase diversity phase retrieval (PDPR) is to find suitable optimization algorithms for wave-front sensing of different scales, especially for large-scale wavefront sensing. When dealing with large-scale wave-front sensing, existing gradient-based local optimization algorithms used in PDPR are easily trapped in local minimums near initial positions, and available global optimization algorithms possess low convergence efficiency. We construct a practicable optimization algorithm used in PDPR for large-scale wave-front sensing. This algorithm, named EPSO-BFGS, is a two-step hybrid global optimization algorithm based on the combination of evolutionary particle swarm optimization (EPSO) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Firstly, EPSO provides global search and obtains a rough global minimum position in limited search steps. Then, BFGS initialized by the rough global minimum position approaches the global minimum with high accuracy and fast convergence speed. Numerical examples testify to the feasibility and reliability of EPSO-BFGS for wave-front sensing of different scales. Two numerical cases also validate the ability of EPSO-BFGS for large-scale wave-front sensing. The effectiveness of EPSO-BFGS is further affirmed by performing a verification experiment.
Optimal function explains forest responses to global change
Roderick Dewar; Oskar Franklin; Annikki Makela; Ross E. McMurtrie; Harry T. Valentine
2009-01-01
Plant responses to global changes in carbon dioxide (CO2), nitrogen, and water availability are critical to future atmospheric CO2 concentrations, hydrology, and hence climate. Our understanding of those responses is incomplete, however. Multiple-resource manipulation experiments and empirical observations have revealed a...
The Tunneling Method for Global Optimization in Multidimensional Scaling.
ERIC Educational Resources Information Center
Groenen, Patrick J. F.; Heiser, Willem J.
1996-01-01
A tunneling method for global minimization in multidimensional scaling is introduced and adjusted for multidimensional scaling with general Minkowski distances. The method alternates a local search step with a tunneling step in which a different configuration is sought with the same STRESS implementation. (SLD)
NASA Astrophysics Data System (ADS)
Radovanovic, Jelena; Milanovic, Vitomir; Ikonic, Zoran; Indjin, Dragan
2002-07-01
A procedure is proposed for finding the optimal profile of a semiconductor quantum well to obtain maximal value of the optical rectification coefficient. It relies on the variational calculus, i.e. the optimal control theory, combined with the method of simulated annealing, and should deliver a globally optimized profile, unconstrained to any particular class of functional forms. For the purpose of illustration, the procedure is applied to the optimized design of AlxGa1-xAs based quantum wells, for rectification of ℎω = 116 meV (CO2 laser) radiation. The optimal smooth profile may eventually be discretized to make the structure fabrication easier.
Saborido, Rubén; Ruiz, Ana B; Luque, Mariano
2016-02-08
In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA (global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.
Avoiding spurious submovement decompositions : a globally optimal algorithm.
Rohrer, Brandon Robinson; Hogan, Neville
2003-07-01
Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to extract them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.
Global stability and optimal control of an SIRS epidemic model on heterogeneous networks
NASA Astrophysics Data System (ADS)
Chen, Lijuan; Sun, Jitao
2014-09-01
In this paper, we consider an SIRS epidemic model with vaccination on heterogeneous networks. By constructing suitable Lyapunov functions, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. Also we firstly study an optimally controlled SIRS epidemic model on complex networks. We show that an optimal control exists for the control problem. Finally some examples are presented to show the global stability and the efficiency of this optimal control. These results can help in adopting pragmatic treatment upon diseases in structured populations.
Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm
NASA Astrophysics Data System (ADS)
Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel
2011-09-01
The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.
New Methods for Large Scale Local and Global Optimization
1994-07-08
investigators together with Jorge Nocedal of Northwestern University was completed during this research period has been accepted for publication by...easier to implement for a particular application. We have written a paper based on this work with Jorge Nocedal . In addition we have developed and...Liu, D., and J. Nocedal , "On the behavior of Broyden’s class of quasi-Newton methods," SlAM Journal on Optimization 2, 1992, pp. 533-557. (2) R. H
An evolutionary algorithm for global optimization based on self-organizing maps
NASA Astrophysics Data System (ADS)
Barmada, Sami; Raugi, Marco; Tucci, Mauro
2016-10-01
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior.
Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron
2013-01-01
We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.
Globally Optimal Path Planning with Anisotropic Running Costs
2013-03-01
gradient vector differential operator, ∇ = ∑n i=1 ei ∂ ∂zi h triangulation diameter Xh triangulated mesh of diameter h xi a mesh point in Xh Ωh...grid spacing Z set of integers (i, j) integer mesh co-ordinate x(i, j) mesh point in Ωh with integer mesh co-ordinate (i, j) ΩZh set of integer mesh...may not converge to the optimal path as the computational mesh is refined. The final point primarily arises in graph-based methods, and has profound
Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks
NASA Astrophysics Data System (ADS)
Ronellenfitsch, Henrik; Katifori, Eleni
2016-09-01
Highly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. In this work we show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as leaf and animal vasculature.
Global optimization of parameters in the reactive force field ReaxFF for SiOH.
Larsson, Henrik R; van Duin, Adri C T; Hartke, Bernd
2013-09-30
We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn-ed out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We have finally arrive-ed at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields.
Handling inequality constraints in continuous nonlinear global optimization
Wang, Tao; Wah, B.W.
1996-12-31
In this paper, we present a new method to handle inequality constraints and apply it in NOVEL (Nonlinear Optimization via External Lead), a system we have developed for solving constrained continuous nonlinear optimization problems. In general, in applying Lagrange-multiplier methods to solve these problems, inequality constraints are first converted into equivalent equality constraints. One such conversion method adds a slack variable to each inequality constraint in order to convert it into an equality constraint. The disadvantage of this conversion is that when the search is inside a feasible region, some satisfied constraints may still pose a non-zero weight in the Lagrangian function, leading to possible oscillations and divergence when a local optimum lies on the boundary of a feasible region. We propose a new conversion method called the MaxQ method such that all satisfied constraints in a feasible region always carry zero weight in the Lagrange function; hence, minimizing the Lagrange function in a feasible region always leads to local minima of the objective function. We demonstrate that oscillations do not happen in our method. We also propose methods to speed up convergence when a local optimum lies on the boundary of a feasible region. Finally, we show improved experimental results in applying our proposed method in NOVEL on some existing benchmark problems and compare them to those obtained by applying the method based on slack variables.
NASA Astrophysics Data System (ADS)
Sarkar, Kanchan; Bhattacharyya, S. P.
2013-08-01
We propose and implement a simple adaptive heuristic to optimize the geometries of clusters of point charges or ions with the ability to find the global minimum energy configurations. The approach uses random mutations of a single string encoding the geometry and accepts moves that decrease the energy. Mutation probability and mutation intensity are allowed to evolve adaptively on the basis of continuous evaluation of past explorations. The resulting algorithm has been called Completely Adaptive Random Mutation Hill Climbing method. We have implemented this method to search through the complex potential energy landscapes of parabolically confined 3D classical Coulomb clusters of hundreds or thousands of charges—usually found in high frequency discharge plasmas. The energy per particle (EN/N) and its first and second differences, structural features, distribution of the oscillation frequencies of normal modes, etc., are analyzed as functions of confinement strength and the number of charges in the system. Certain magic numbers are identified. In order to test the feasibility of the algorithm in cluster geometry optimization on more complex energy landscapes, we have applied the algorithm for optimizing the geometries of MgO clusters, described by Coulomb-Born-Mayer potential and finding global minimum of some Lennard-Jones clusters. The convergence behavior of the algorithm compares favorably with those of other existing global optimizers.
Sarkar, Kanchan; Bhattacharyya, S P
2013-08-21
We propose and implement a simple adaptive heuristic to optimize the geometries of clusters of point charges or ions with the ability to find the global minimum energy configurations. The approach uses random mutations of a single string encoding the geometry and accepts moves that decrease the energy. Mutation probability and mutation intensity are allowed to evolve adaptively on the basis of continuous evaluation of past explorations. The resulting algorithm has been called Completely Adaptive Random Mutation Hill Climbing method. We have implemented this method to search through the complex potential energy landscapes of parabolically confined 3D classical Coulomb clusters of hundreds or thousands of charges--usually found in high frequency discharge plasmas. The energy per particle (EN∕N) and its first and second differences, structural features, distribution of the oscillation frequencies of normal modes, etc., are analyzed as functions of confinement strength and the number of charges in the system. Certain magic numbers are identified. In order to test the feasibility of the algorithm in cluster geometry optimization on more complex energy landscapes, we have applied the algorithm for optimizing the geometries of MgO clusters, described by Coulomb-Born-Mayer potential and finding global minimum of some Lennard-Jones clusters. The convergence behavior of the algorithm compares favorably with those of other existing global optimizers.
Kim, Seongho; Li, Lang
2011-08-01
NONMEM is one of the most popular approaches to a population pharmacokinetics/pharmacodynamics (PK/PD) analysis in fitting nonlinear mixed-effects models. As a local optimization algorithm, NONMEM usually requires an initial value close enough to the global optimum. This paper proposes a novel global search algorithm called P-NONMEM. It combines the global search strategy by particle swarm optimization (PSO) and the local estimation strategy of NONMEM. In the proposed algorithm, initial values (particles) are generated randomly by PSO, and NONMEM is implemented for each particle to find a local optimum for fixed effects and variance parameters. P-NONMEM guarantees the global optimization for fixed effects and variance parameters. Under certain regularity conditions, it also leads to global optimization for random effects. Because P-NONMEM doesn't run PSO search for random effect estimation, it avoids tremendous computational burden. In the simulation studies, we have shown that P-NONMEM has much improved convergence performance than NONMEM. Even when the initial values were far away from the global optima, P-NONMEM converged nicely for all fixed effects, random effects, and variance components.
A Global Optimization Methodology for Rocket Propulsion Applications
NASA Technical Reports Server (NTRS)
2001-01-01
While the response surface method is an effective method in engineering optimization, its accuracy is often affected by the use of limited amount of data points for model construction. In this chapter, the issues related to the accuracy of the RS approximations and possible ways of improving the RS model using appropriate treatments, including the iteratively re-weighted least square (IRLS) technique and the radial-basis neural networks, are investigated. A main interest is to identify ways to offer added capabilities for the RS method to be able to at least selectively improve the accuracy in regions of importance. An example is to target the high efficiency region of a fluid machinery design space so that the predictive power of the RS can be maximized when it matters most. Analytical models based on polynomials, with controlled level of noise, are used to assess the performance of these techniques.
Metamodel-based global optimization using fuzzy clustering for design space reduction
NASA Astrophysics Data System (ADS)
Li, Yulin; Liu, Li; Long, Teng; Dong, Weili
2013-09-01
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models. For computation-intensive engineering design problems, efficient global optimization methods must be developed to relieve the computational burden. A new metamodel-based global optimization method using fuzzy clustering for design space reduction (MGO-FCR) is presented. The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel, whose accuracy is improved with increasing number of sample points gradually. Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively. Modeling efficiency and accuracy are directly related to the design space, so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms. The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated. The first pseudo reduction algorithm improves the speed of clustering, while the second pseudo reduction algorithm ensures the design space to be reduced. Through several numerical benchmark functions, comparative studies with adaptive response surface method, approximated unimodal region elimination method and mode-pursuing sampling are carried out. The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions. And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems. Based on this global design optimization method, a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms. This method possesses favorable performance on efficiency, robustness
NASA Astrophysics Data System (ADS)
Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei
2014-04-01
Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
Method for using global optimization to the estimation of surface-consistent residual statics
Reister, David B.; Barhen, Jacob; Oblow, Edward M.
2001-01-01
An efficient method for generating residual statics corrections to compensate for surface-consistent static time shifts in stacked seismic traces. The method includes a step of framing the residual static corrections as a global optimization problem in a parameter space. The method also includes decoupling the global optimization problem involving all seismic traces into several one-dimensional problems. The method further utilizes a Stochastic Pijavskij Tunneling search to eliminate regions in the parameter space where a global minimum is unlikely to exist so that the global minimum may be quickly discovered. The method finds the residual statics corrections by maximizing the total stack power. The stack power is a measure of seismic energy transferred from energy sources to receivers.
Global optimization of fuel consumption in J2 rendezvous using interval analysis
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie; Liang, Yuying
2017-03-01
This paper addresses an open-time Lambert problem under first-order gravitational perturbations with unfixed parking time and transfer time. The perturbations are compensated by introducing its analytical solutions derived from Lagrange's planetary equations into Lambert problem. A drift vector of aim position correction is defined to reduce the aim position bias caused by the perturbations. The first purpose of optimization is to find sufficiently small intervals involving the global optimal parking time, transfer time, drift vector and velocity increment. The second is to determine the global solution or the solution close to it in these intervals. Interval analysis and a double-deck gradient-based method with GA estimating the initial range of drift vector are utilized to obtain the sufficiently small intervals including the global minimum velocity increment and the global minimum solution or one sufficiently close to it in these intervals.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
More on conditions of local and global minima coincidence in discrete optimization problems
Lebedeva, T.T.; Sergienko, I.V.; Soltan, V.P.
1994-05-01
In some areas of discrete optimization, it is necessary to isolate classes of problems whose target functions do not have local or strictly local minima that differ from the global minima. Examples include optimizations on discrete metric spaces and graphs, lattices and partially ordered sets, and linear combinatorial problems. A unified schema that to a certain extent generalizes the convexity models on which the above-cited works are based has been presented in articles. This article is a continuation of that research.
An adaptive metamodel-based global optimization algorithm for black-box type problems
NASA Astrophysics Data System (ADS)
Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan
2015-11-01
In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.
A global optimization algorithm inspired in the behavior of selfish herds.
Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián
2017-10-01
In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.
Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods
NASA Astrophysics Data System (ADS)
Rogers, Adam; Safi-Harb, Samar; Fiege, Jason
2015-08-01
The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.
Long-term stability of the Tevatron by verified global optimization
NASA Astrophysics Data System (ADS)
Berz, Martin; Makino, Kyoko; Kim, Youn-Kyung
2006-03-01
The tools used to compute high-order transfer maps based on differential algebraic (DA) methods have recently been augmented by methods that also allow a rigorous computation of an interval bound for the remainder. In this paper we will show how such methods can also be used to determine rigorous bounds for the global extrema of functions in an efficient way. The method is used for the bounding of normal form defect functions, which allows rigorous stability estimates for repetitive particle accelerator. However, the method is also applicable to general lattice design problems and can enhance the commonly used local optimization with heuristic successive starting point modification. The global optimization approach studied rests on the ability of the method to suppress the so-called dependency problem common to validated computations, as well as effective polynomial bounding techniques. We review the linear dominated bounder (LDB) and the quadratic fast bounder (QFB) and study their performance for various example problems in global optimization. We observe that the method is superior to other global optimization approaches and can prove stability times similar to what is desired, without any need for expensive long-term tracking and in a fully rigorous way.
A New Large-Scale Global Optimization Method and Its Application to Lennard-Jones Problems
1992-11-01
stochastic methods. Computational results on Lennard - Jones problems show that the new method is considerably more successful than any other method that...our method does not find as good a solution as has been found by the best special purpose methods for Lennard - Jones problems. This illustrates the inherent difficulty of large scale global optimization.
Comfort improvement of a nonlinear suspension using global optimization and in situ measurements
NASA Astrophysics Data System (ADS)
Deprez, K.; Moshou, D.; Ramon, H.
2005-06-01
The health problems encountered by operators of off-road vehicles demonstrate that a lot of effort still has to be put into the design of effective seat and cabin suspensions. Owing to the nonlinear nature of the suspensions and the use of in situ measurements for the optimization, classical local optimization techniques are prone to getting stuck in local minima. Therefore this paper develops a method for optimizing nonlinear suspension systems based on in situ measurements, using the global optimization technique DIRECT to avoid local minima. Evaluation of the comfort improvement of the suspension was carried out using the objective comfort parameters used in standards. As a test case, the optimization of a hydropneumatic element that can serve as part of a cabin suspension for off-road machinery was performed.
NASA Astrophysics Data System (ADS)
Shabbir, Faisal; Omenzetter, Piotr
2014-04-01
Much effort is devoted nowadays to derive accurate finite element (FE) models to be used for structural health monitoring, damage detection and assessment. However, formation of a FE model representative of the original structure is a difficult task. Model updating is a branch of optimization which calibrates the FE model by comparing the modal properties of the actual structure with these of the FE predictions. As the number of experimental measurements is usually much smaller than the number of uncertain parameters, and, consequently, not all uncertain parameters are selected for model updating, different local minima may exist in the solution space. Experimental noise further exacerbates the problem. The attainment of a global solution in a multi-dimensional search space is a challenging problem. Global optimization algorithms (GOAs) have received interest in the previous decade to solve this problem, but no GOA can ensure the detection of the global minimum either. To counter this problem, a combination of GOA with sequential niche technique (SNT) has been proposed in this research which systematically searches the whole solution space. A dynamically tested full scale pedestrian bridge is taken as a case study. Two different GOAs, namely particle swarm optimization (PSO) and genetic algorithm (GA), are investigated in combination with SNT. The results of these GOA are compared in terms of their efficiency in detecting global minima. The systematic search enables to find different solutions in the search space, thus increasing the confidence of finding the global minimum.
Decomposition method of complex optimization model based on global sensitivity analysis
NASA Astrophysics Data System (ADS)
Qiu, Qingying; Li, Bing; Feng, Peien; Gao, Yu
2014-07-01
The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.
Mayorga, René V; Arriaga, Mariano
2007-10-01
In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function. The decreasing process can continue even after reaching local minima and, in general, the algorithm stops when converging to solutions near the global minimum. The implementation of the proposed technique by conventional numerical methods may require a considerable computational effort on the approximation of the inverse function. Thus, here a novel Artificial Neural Network (ANN) approach is implemented to reduce the computational requirements of the proposed optimization technique. This approach is successfully tested on some highly non-linear functions possessing several local minima. The results obtained demonstrate that the proposed approach compares favorably over some current conventional numerical (Matlab functions) methods, and other non-conventional (Evolutionary Algorithms, Simulated Annealing) optimization methods.
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions.
Shutao Li; Mingkui Tan; Tsang, I W; Kwok, James Tin-Yau
2011-08-01
Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems.
A nonlinear interval number programming method based on RBF global optimization technique
NASA Astrophysics Data System (ADS)
Zhao, Ziheng; Han, Xu; Chao, Jiang
2010-05-01
In this paper, a new nonlinear interval-based programming (NIP) method based on Radial basis function (RBF) approximation models and RBF global search technique method is proposed. In NIP, searching for the extreme responses of objective and constraints are integrated with the main optimization, which leads to extremely low efficiency. Approximation models are commonly used to promote the computational efficiency. Consequently, two inevitable problems are encountered. The first one is how to obtain the global minimum and maximum in the sub-optimizations. The second one is how to diminish the approximation errors on the response bounds of system. The present method combined with RBF global search technique shows a good feature to overcome these problems. High accuracy and low computational cost can be achieved simultaneously. Two numerical examples are used to test the effectiveness of the present method.
NASA Astrophysics Data System (ADS)
Cruz, S. M. A.; Marques, J. M. C.; Pereira, F. B.
2016-10-01
We propose improvements to our evolutionary algorithm (EA) [J. M. C. Marques and F. B. Pereira, J. Mol. Liq. 210, 51 (2015)] in order to avoid dissociative solutions in the global optimization of clusters with competing attractive and repulsive interactions. The improved EA outperforms the original version of the method for charged colloidal clusters in the size range 3 ≤ N ≤ 25, which is a very stringent test for global optimization algorithms. While the Bernal spiral is the global minimum for clusters in the interval 13 ≤ N ≤ 18, the lowest-energy structure is a peculiar, so-called beaded-necklace, motif for 19 ≤ N ≤ 25. We have also applied the method for larger sizes and unusual quasi-linear and branched clusters arise as low-energy structures.
On computing the global time-optimal motions of robotic manipulators in the presence of obstacles
NASA Technical Reports Server (NTRS)
Shiller, Zvi; Dubowsky, Steven
1991-01-01
A method for computing the time-optimal motions of robotic manipulators is presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem is reduced to a search for the time-optimal path in the n-dimensional position space. A small set of near-optimal paths is first efficiently selected from a grid, using a branch and bound search and a series of lower bound estimates on the traveling time along a given path. These paths are further optimized with a local path optimization to yield the global optimal solution. Obstacles are considered by eliminating the collision points from the tessellated space and by adding a penalty function to the motion time in the local optimization. The computational efficiency of the method stems from the reduced dimensionality of the searched spaced and from combining the grid search with a local optimization. The method is demonstrated in several examples for two- and six-degree-of-freedom manipulators with obstacles.
On computing the global time-optimal motions of robotic manipulators in the presence of obstacles
NASA Technical Reports Server (NTRS)
Shiller, Zvi; Dubowsky, Steven
1991-01-01
A method for computing the time-optimal motions of robotic manipulators is presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem is reduced to a search for the time-optimal path in the n-dimensional position space. A small set of near-optimal paths is first efficiently selected from a grid, using a branch and bound search and a series of lower bound estimates on the traveling time along a given path. These paths are further optimized with a local path optimization to yield the global optimal solution. Obstacles are considered by eliminating the collision points from the tessellated space and by adding a penalty function to the motion time in the local optimization. The computational efficiency of the method stems from the reduced dimensionality of the searched spaced and from combining the grid search with a local optimization. The method is demonstrated in several examples for two- and six-degree-of-freedom manipulators with obstacles.
Global optimal design of ground water monitoring network using embedded kriging.
Dhar, Anirban; Datta, Bithin
2009-01-01
We present a methodology for global optimal design of ground water quality monitoring networks using a linear mixed-integer formulation. The proposed methodology incorporates ordinary kriging (OK) within the decision model formulation for spatial estimation of contaminant concentration values. Different monitoring network design models incorporating concentration estimation error, variance estimation error, mass estimation error, error in locating plume centroid, and spatial coverage of the designed network are developed. A big-M technique is used for reformulating the monitoring network design model to a linear decision model while incorporating different objectives and OK equations. Global optimality of the solutions obtained for the monitoring network design can be ensured due to the linear mixed-integer programming formulations proposed. Performances of the proposed models are evaluated for both field and hypothetical illustrative systems. Evaluation results indicate that the proposed methodology performs satisfactorily. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal ground water contaminant monitoring network design.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
Ambur, D.R.; Jaunky, N.; Knight, N.F. Jr.
1996-04-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Image segmentation using globally optimal growth in three dimensions with an adaptive feature set
NASA Astrophysics Data System (ADS)
Taylor, David C.; Barrett, William A.
1994-09-01
A globally optimal region growing algorithm for 3D segmentation of anatomical objects is developed. The notion of simple 3D connected component labelling is extended to enable the combination of arbitrary features in the segmentation process. This algorithm uses a hybrid octree-btree structure to segment an object of interest in an ordered fashion. This tree structure overcomes the computational complexity of global optimality in three dimensions. The segmentation process is controlled by a set of active features, which work in concert to extract the object of interest. The cost function used to enforce the order is based on the combination of active features. The characteristics of the data throughout the volume dynamically influences which features are active. A foundation for applying user interaction with the object directly to the feature set is established. The result is a system which analyzes user input and neighborhood data and optimizes the tools used in the segmentation process accordingly.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
NASA Technical Reports Server (NTRS)
Ambur, Damodar R.; Jaunky, Navin; Knight, Norman F., Jr.
1996-01-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes
Liu, Sheng; Jin, Haiqiang; Mao, Xiaojun; Zhai, Binbin; Zhan, Ye; Feng, Xiaofei
2013-01-01
This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches. PMID:23766717
Video coding using arbitrarily shaped block partitions in globally optimal perspective
NASA Astrophysics Data System (ADS)
Paul, Manoranjan; Murshed, Manzur
2011-12-01
Algorithms using content-based patterns to segment moving regions at the macroblock (MB) level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as an extra mode. The content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we propose a near- optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the MBs into several disjoint sets, with a direct pattern selection algorithm by allowing all the MBs in multiple pattern modes outperforms the existing pattern-based coding when embedded into the H.264.
Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization
Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan
2016-11-27
Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO_{3}-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.
Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization
Xi, Maolong; Lu, Dan; Gui, Dongwei; ...
2016-11-27
Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less
Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization
NASA Astrophysics Data System (ADS)
Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan
2017-01-01
Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.
An improved hybrid global optimization method for protein tertiary structure prediction
McAllister, Scott R.
2009-01-01
First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations. PMID:20357906
A comparative study of expected improvement-assisted global optimization with different surrogates
NASA Astrophysics Data System (ADS)
Wang, Hu; Ye, Fan; Li, Enying; Li, Guangyao
2016-08-01
Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Optimization of a semianalytical ocean color model for global-scale applications.
Maritorena, Stéphane; Siegel, David A; Peterson, Alan R
2002-05-20
Semianalytical (SA) ocean color models have advantages over conventional band ratio algorithms in that multiple ocean properties can be retrieved simultaneously from a single water-leaving radiance spectrum. However, the complexity of SA models has stalled their development, and operational implementation as optimal SA parameter values are hard to determine because of limitations in development data sets and the lack of robust tuning procedures. We present a procedure for optimizing SA ocean color models for global applications. The SA model to be optimized retrieves simultaneous estimates for chlorophyll (Chl) concentration, the absorption coefficient for dissolved and detrital materials [a(cdm)(443)], and the particulate backscatter coefficient [b(bp)(443)] from measurements of the normalized water-leaving radiance spectrum. Parameters for the model are tuned by simulated annealing as the global optimization protocol. We first evaluate the robustness of the tuning method using synthetic data sets, and we then apply the tuning procedure to an in situ data set. With the tuned SA parameters, the accuracy of retrievals found with the globally optimized model (the Garver-Siegel-Maritorena model version 1; hereafter GSM01) is excellent and results are comparable with the current Sea-viewing Wide Field-of-view sensor (SeaWiFS) algorithm for Chl. The advantage of the GSM01 model is that simultaneous retrievals of a(cdm)(443) and b(bp)(443) are made that greatly extend the nature of global applications that can be explored. Current limitations and further developments of the model are discussed.
Limitations of Parallel Global Optimization for Large-Scale Human Movement Problems
Koh, Byung-Il; Reinbolt, Jeffrey A.; George, Alan D.; Haftka, Raphael T.; Fregly, Benjamin J.
2009-01-01
Global optimization algorithms (e.g., simulated annealing, genetic, and particle swarm) have been gaining popularity in biomechanics research, in part due to advances in parallel computing. To date, such algorithms have only been applied to small- or medium-scale optimization problems (< 100 design variables). This study evaluates the applicability of a parallel particle swarm global optimization algorithm to large-scale human movement problems. The evaluation was performed using two large-scale (660 design variables) optimization problems that utilized a dynamic, 27 degree-of-freedom, full-body gait model to predict new gait motions from a nominal gait motion. Both cost functions minimized a quantity that reduced the knee adduction torque. The first one minimized foot path errors corresponding to an increased toe out angle of 15 deg, while the second one minimized the knee adduction torque directly without changing the foot path. Constraints on allowable changes in trunk orientation, joint angles, joint torques, centers of pressure, and ground reactions were handled using a penalty method. For both problems, a single run with a gradient-based nonlinear least squares algorithm found a significantly better solution than did 10 runs with the global particle swarm algorithm. Due to the penalty terms, the physically-realistic gradient-based solutions were located within a narrow “channel” in design space that was difficult to enter without gradient information. Researchers should exercise caution when extrapolating the performance of parallel global optimizers to human movement problems with hundreds of design variables, especially when penalty terms are included in the cost function. PMID:19036629
Optimal satellite sampling to resolve global-scale dynamics in the I-T system
NASA Astrophysics Data System (ADS)
Rowland, D. E.; Zesta, E.; Connor, H. K.; Pfaff, R. F., Jr.
2016-12-01
The recent Decadal Survey highlighted the need for multipoint measurements of ion-neutral coupling processes to study the pathways by which solar wind energy drives dynamics in the I-T system. The emphasis in the Decadal Survey is on global-scale dynamics and processes, and in particular, mission concepts making use of multiple identical spacecraft in low earth orbit were considered for the GDC and DYNAMIC missions. This presentation will provide quantitative assessments of the optimal spacecraft sampling needed to significantly advance our knowledge of I-T dynamics on the global scale.We will examine storm time and quiet time conditions as simulated by global circulation models, and determine how well various candidate satellite constellations and satellite schemes can quantify the plasma and neutral convection patterns and global-scale distributions of plasma density, neutral density, and composition, and their response to changes in the IMF. While the global circulation models are data-starved, and do not contain all the physics that we might expect to observe with a global-scale constellation mission, they are nonetheless an excellent "starting point" for discussions of the implementation of such a mission. The result will be of great utility for the design of future missions, such as GDC, to study the global-scale dynamics of the I-T system.
SGO: A fast engine for ab initio atomic structure global optimization by differential evolution
NASA Astrophysics Data System (ADS)
Chen, Zhanghui; Jia, Weile; Jiang, Xiangwei; Li, Shu-Shen; Wang, Lin-Wang
2017-10-01
As the high throughout calculations and material genome approaches become more and more popular in material science, the search for optimal ways to predict atomic global minimum structure is a high research priority. This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and a plane-wave density functional theory code running on GPU machines. The purpose is to show what can be achieved by combining the superior algorithms at the different levels of the searching scheme. SGO can search the global-minimum configurations of crystals, two-dimensional materials and quantum clusters without prior symmetry restriction in a relatively short time (half or several hours for systems with less than 25 atoms), thus making such a task a routine calculation. Comparisons with other existing methods such as minima hopping and genetic algorithm are provided. One motivation of our study is to investigate the properties of magnetic systems in different phases. The SGO engine is capable of surveying the local minima surrounding the global minimum, which provides the information for the overall energy landscape of a given system. Using this capability we have found several new configurations for testing systems, explored their energy landscape, and demonstrated that the magnetic moment of metal clusters fluctuates strongly in different local minima.
Estimation of the global average temperature with optimally weighted point gauges
NASA Technical Reports Server (NTRS)
Hardin, James W.; Upson, Robert B.
1993-01-01
This paper considers the minimum mean squared error (MSE) incurred in estimating an idealized Earth's global average temperature with a finite network of point gauges located over the globe. We follow the spectral MSE formalism given by North et al. (1992) and derive the optimal weights for N gauges in the problem of estimating the Earth's global average temperature. Our results suggest that for commonly used configurations the variance of the estimate due to sampling error can be reduced by as much as 50%.
GFS algorithm based on batch Monte Carlo trials for solving global optimization problems
NASA Astrophysics Data System (ADS)
Popkov, Yuri S.; Darkhovskiy, Boris S.; Popkov, Alexey Y.
2016-10-01
A new method for global optimization of Hölder goal functions under compact sets given by inequalities is proposed. All functions are defined only algorithmically. The method is based on performing simple Monte Carlo trials and constructing the sequences of records and the sequence of their decrements. An estimating procedure of Hölder constants is proposed. Probability estimation of exact global minimum neighborhood using Hölder constants estimates is presented. Results on some analytical and algorithmic test problems illustrate the method's performance.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints
NASA Technical Reports Server (NTRS)
Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren
2015-01-01
Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.
NASA Astrophysics Data System (ADS)
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration.
Yang, Jiaolong; Li, Hongdong; Campbell, Dylan; Jia, Yunde
2016-11-01
The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the L2 error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space SE(3). By exploiting the special structure of SE(3) geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.
Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates
NASA Astrophysics Data System (ADS)
Lu, D.; Ricciuto, D. M.; Gu, L.
2016-12-01
Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Matsuno, Takashi; Maeda, Kengo; Kawazoe, Hiromitsu
2015-09-01
A kriging-based genetic algorithm called efficient global optimization (EGO) was employed to optimize the parameters for the operating conditions of plasma actuators. The aerodynamic performance was evaluated by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The proposed system was used on two design problems to design the power supply for a plasma actuator. The first case was the drag minimization problem around a semicircular cylinder. In this case, the inhibitory effect of flow separation was also observed. The second case was the lift maximization problem around a circular cylinder. This case was similar to the aerofoil design, because the circular cylinder has potential to work as an aerofoil owing to the control of the flow circulation by the plasma actuators with four design parameters. In this case, applicability to the multi-variant design problem was also investigated. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment.
Global bifurcation investigation of an optimal velocity traffic model with driver reaction time
NASA Astrophysics Data System (ADS)
Orosz, Gábor; Wilson, R. Eddie; Krauskopf, Bernd
2004-08-01
We investigate an optimal velocity model which includes the reflex time of drivers. After an analytical study of the stability and local bifurcations of the steady-state solution, we apply numerical continuation techniques to investigate the global behavior of the system. Specifically, we find branches of oscillating solutions connecting Hopf bifurcation points, which may be super- or subcritical, depending on parameters. This analysis reveals several regions of multistability.
Efficient searching of globally optimal and smooth multi-surfaces with shape priors
NASA Astrophysics Data System (ADS)
Xu, Lei; Stojkovic, Branislav; Ding, Hu; Song, Qi; Wu, Xiaodong; Sonka, Milan; Xu, Jinhui
2012-02-01
Despite extensive studies in the past, the problem of segmenting globally optimal multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method based on a volumetric graph representation of the 3D image that incorporates curvature and shape prior information. Compared with the graph-based method, applying the shape prior to construct the graph on a specific preferred shape model allows easy incorporation of a wide spectrum of shape prior information. Furthermore, the key insight that computation of the objective function can be done independently in the x and y directions makes local improvement possible. Thus, instead of using global optimization technique such as maximum flow algorithm, the iteration based method is much faster. Additionally, the utilization of the curvature in the objective function ensures the smoothness. To the best of our knowledge, this is the first paper to combine the shape-prior penalties with utilizing curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. To evaluate the performance of our method, we test it on a set of 14 3D OCT images. Comparing to the best existing approaches, our experiments suggest that the proposed method reduces the unsigned surface positioning errors form 5.44 +/- 1.07(μm) to 4.52 +/- 0.84(μm). Moreover, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. The proposed method is also suitable for parallel implementation on GPUs, which could potentially allow us to segment highly noisy volumetric images in real time.
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.
Ringed Seal Search for Global Optimization via a Sensitive Search Model
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Application of Global Optimization to the Estimation of Surface-Consistent Residual Statics
Reister, D.B.; Oblow, E.M.; Barhen, J.; DuBose, J.B.
1999-10-01
Since the objective function that is used to estimate surface-consistent residual statics can have many local maxima, a global optimization method is required to find the optimum values for the residual statics. As reported in several recent papers, we had developed a new method (TRUST) for solving global optimization problems and had demonstrated it was superior to all competing methods for a standard set of nonconvex benchmark problems. The residual statics problem can be very large with hundreds or thousands of parameters, and large global optimization problems are much harder to solve than small problems. To solve the very challenging residual statics problem, we have made several significant advances in the mathematical description of the residual statics problem (derivation of two novel stack power bounds and disaggregation of the original problem into a large number of small problems). Using the enhanced version of TRUST, we have performed extensive simulations on a realistic sample problem that had been artificially created by large static disruptions. Our simulations have demonstrated that TRUST can reach many plausible distinct ''solutions'' that could not be discovered by more conventional approaches. An unexpected result was that high values of the stack power may be eliminate cycle skips.
Paschalidis, Ioannis Ch.; Shen, Yang; Vakili, Pirooz; Vajda, Sandor
2007-01-01
This paper introduces a new stochastic global optimization method targeting protein-protein docking problems, an important class of problems in computational structural biology. The method is based on finding general convex quadratic underestimators to the binding energy function that is funnel-like. Finding the optimum underestimator requires solving a semidefinite programming problem, hence the name semidefinite programming-based underestimation (SDU). The underestimator is used to bias sampling in the search region. It is established that under appropriate conditions SDU locates the global energy minimum with probability approaching one as the sample size grows. A detailed comparison of SDU with a related method of convex global underestimator (CGU), and computational results for protein-protein docking problems are provided. PMID:19759849
Paschalidis, Ioannis Ch; Shen, Yang; Vakili, Pirooz; Vajda, Sandor
2007-04-01
This paper introduces a new stochastic global optimization method targeting protein-protein docking problems, an important class of problems in computational structural biology. The method is based on finding general convex quadratic underestimators to the binding energy function that is funnel-like. Finding the optimum underestimator requires solving a semidefinite programming problem, hence the name semidefinite programming-based underestimation (SDU). The underestimator is used to bias sampling in the search region. It is established that under appropriate conditions SDU locates the global energy minimum with probability approaching one as the sample size grows. A detailed comparison of SDU with a related method of convex global underestimator (CGU), and computational results for protein-protein docking problems are provided.
Economic optimization of a global strategy to address the pandemic threat
Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C.; Daszak, Peter
2014-01-01
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral “One Health” pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual. PMID:25512538
Liu, Qunfeng; Chen, Wei-Neng; Deng, Jeremiah D; Gu, Tianlong; Zhang, Huaxiang; Yu, Zhengtao; Zhang, Jun
2017-02-07
The popular performance profiles and data profiles for benchmarking deterministic optimization algorithms are extended to benchmark stochastic algorithms for global optimization problems. A general confidence interval is employed to replace the significance test, which is popular in traditional benchmarking methods but suffering more and more criticisms. Through computing confidence bounds of the general confidence interval and visualizing them with performance profiles and (or) data profiles, our benchmarking method can be used to compare stochastic optimization algorithms by graphs. Compared with traditional benchmarking methods, our method is synthetic statistically and therefore is suitable for large sets of benchmark problems. Compared with some sample-mean-based benchmarking methods, e.g., the method adopted in black-box-optimization-benchmarking workshop/competition, our method considers not only sample means but also sample variances. The most important property of our method is that it is a distribution-free method, i.e., it does not depend on any distribution assumption of the population. This makes it a promising benchmarking method for stochastic optimization algorithms. Some examples are provided to illustrate how to use our method to compare stochastic optimization algorithms.
Freier, Lars; von Lieres, Eric
2017-07-01
Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets, such as productivity and yield. We address these issues by introducing a novel algorithm that combines recently developed approaches for utilizing statistical regression models in multi-objective optimization. The proposed algorithm is demonstrated by simultaneous optimization of elution gradient and pooling strategy for chromatographic separation of a three-component system with respect to purity, yield, and processing time. Gaussian Process Regression Models (GPM) are used for estimating functional relationships between design variables (gradient, pooling) and performance indicators (purity, yield, time). The Pareto front is iteratively approximated by planning new experiments such as to maximize the Expected Hypervolume Improvement (EHVI) as determined from the GPM by Markov Chain Monte Carlo (MCMC) sampling. A comprehensive Monte-Carlo study with in-silico data illustrates efficiency, effectiveness and robustness of the presented Multi-Objective Global Optimization (MOGO) algorithm in determining best compromises between conflicting objectives with comparably very low experimental effort. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
NASA Technical Reports Server (NTRS)
Jaunky, N.; Ambur, D. R.; Knight, N. F., Jr.
1998-01-01
A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and strength constraints was developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory was used for the global analysis. Local buckling of skin segments were assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments were also assessed. Constraints on the axial membrane strain in the skin and stiffener segments were imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study were the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence and stiffening configuration, where stiffening configuration is a design variable that indicates the combination of axial, transverse and diagonal stiffener in the grid-stiffened cylinder. The design optimization process was adapted to identify the best suited stiffening configurations and stiffener spacings for grid-stiffened composite cylinder with the length and radius of the cylinder, the design in-plane loads and material properties as inputs. The effect of having axial membrane strain constraints in the skin and stiffener segments in the optimization process is also studied for selected stiffening configurations.
NASA Technical Reports Server (NTRS)
Jaunky, Navin; Knight, Norman F., Jr.; Ambur, Damodar R.
1998-01-01
A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and, strength constraints is developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory is used for the global analysis. Local buckling of skin segments are assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments are also assessed. Constraints on the axial membrane strain in the skin and stiffener segments are imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study are the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence, and stiffening configuration, where herein stiffening configuration is a design variable that indicates the combination of axial, transverse, and diagonal stiffener in the grid-stiffened cylinder. The design optimization process is adapted to identify the best suited stiffening configurations and stiffener spacings for grid-stiffened composite cylinder with the length and radius of the cylinder, the design in-plane loads, and material properties as inputs. The effect of having axial membrane strain constraints in the skin and stiffener segments in the optimization process is also studied for selected stiffening configuration.
SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization
Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei
2015-06-15
Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is
NASA Technical Reports Server (NTRS)
Malone, Brett; Mason, W. H.
1992-01-01
An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.
NASA Technical Reports Server (NTRS)
Malone, Brett; Mason, W. H.
1992-01-01
An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.
A global carbon assimilation system based on a dual optimization method
NASA Astrophysics Data System (ADS)
Zheng, H.; Li, Y.; Chen, J. M.; Wang, T.; Huang, Q.; Huang, W. X.; Li, S. M.; Yuan, W. P.; Zheng, X.; Zhang, S. P.; Chen, Z. Q.; Jiang, F.
2014-10-01
Ecological models are effective tools to simulate the distribution of global carbon sources and sinks. However, these models often suffer from substantial biases due to inaccurate simulations of complex ecological processes. We introduce a set of scaling factors (parameters) to an ecological model on the basis of plant functional type (PFT) and latitudes. A global carbon assimilation system (GCAS-DOM) is developed by employing a Dual Optimization Method (DOM) to invert the time-dependent ecological model parameter state and the net carbon flux state simultaneously. We use GCAS-DOM to estimate the global distribution of the CO2 flux on 1° ×1° grid cells for the period from 2000 to 2007. Results show that land and ocean absorb -3.69 ± 0.49 Pg C year-1 and -1.91 ± 0.16 Pg C year-1, respectively. North America, Europe and China contribut -0.96 ± 0.15 Pg C year-1, -0.42 ± 0.08 Pg C year-1 and -0.21 ± 0.28 Pg C year-1, respectively. The uncertainties in the flux after optimization by GCAS-DOM have been remarkably reduced by more than 60%. Through parameter optimization, GCAS-DOM can provide improved estimates of the carbon flux for each PFT. Coniferous forest (-0.97 ± 0.27 Pg C year-1) is the largest contributor to the global carbon sink. Fluxes of once-dominant deciduous forest generated by BEPS is reduced to -0.79 ± 0.22 Pg C year-1, being the third largest carbon sink.
A global carbon assimilation system based on a dual optimization method
NASA Astrophysics Data System (ADS)
Zheng, H.; Li, Y.; Chen, J. M.; Wang, T.; Huang, Q.; Huang, W. X.; Wang, L. H.; Li, S. M.; Yuan, W. P.; Zheng, X.; Zhang, S. P.; Chen, Z. Q.; Jiang, F.
2015-02-01
Ecological models are effective tools for simulating the distribution of global carbon sources and sinks. However, these models often suffer from substantial biases due to inaccurate simulations of complex ecological processes. We introduce a set of scaling factors (parameters) to an ecological model on the basis of plant functional type (PFT) and latitudes. A global carbon assimilation system (GCAS-DOM) is developed by employing a dual optimization method (DOM) to invert the time-dependent ecological model parameter state and the net carbon flux state simultaneously. We use GCAS-DOM to estimate the global distribution of the CO2 flux on 1° × 1° grid cells for the period from 2001 to 2007. Results show that land and ocean absorb -3.63 ± 0.50 and -1.82 ± 0.16 Pg C yr-1, respectively. North America, Europe and China contribute -0.98 ± 0.15, -0.42 ± 0.08 and -0.20 ± 0.29 Pg C yr-1, respectively. The uncertainties in the flux after optimization by GCAS-DOM have been remarkably reduced by more than 60%. Through parameter optimization, GCAS-DOM can provide improved estimates of the carbon flux for each PFT. Coniferous forest (-0.97 ± 0.27 Pg C yr-1) is the largest contributor to the global carbon sink. Fluxes of once-dominant deciduous forest generated by the Boreal Ecosystems Productivity Simulator (BEPS) are reduced to -0.78 ± 0.23 Pg C yr-1, the third largest carbon sink.
Optimizing rice yields while minimizing yield-scaled global warming potential.
Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A
2014-05-01
To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. © 2013 John Wiley & Sons Ltd.
Global-Local Analysis and Optimization of a Composite Civil Tilt-Rotor Wing
NASA Technical Reports Server (NTRS)
Rais-Rohani, Masound
1999-01-01
This report gives highlights of an investigation on the design and optimization of a thin composite wing box structure for a civil tilt-rotor aircraft. Two different concepts are considered for the cantilever wing: (a) a thin monolithic skin design, and (b) a thick sandwich skin design. Each concept is examined with three different skin ply patterns based on various combinations of 0, +/-45, and 90 degree plies. The global-local technique is used in the analysis and optimization of the six design models. The global analysis is based on a finite element model of the wing-pylon configuration while the local analysis uses a uniformly supported plate representing a wing panel. Design allowables include those on vibration frequencies, panel buckling, and material strength. The design optimization problem is formulated as one of minimizing the structural weight subject to strength, stiffness, and d,vnamic constraints. Six different loading conditions based on three different flight modes are considered in the design optimization. The results of this investigation reveal that of all the loading conditions the one corresponding to the rolling pull-out in the airplane mode is the most stringent. Also the frequency constraints are found to drive the skin thickness limits, rendering the buckling constraints inactive. The optimum skin ply pattern for the monolithic skin concept is found to be (((0/+/-45/90/(0/90)(sub 2))(sub s))(sub s), while for the sandwich skin concept the optimal ply pattern is found to be ((0/+/-45/90)(sub 2s))(sub s).
Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach.
Francisco, Alexandre P; Bugalho, Miguel; Ramirez, Mário; Carriço, João A
2009-05-18
Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Here, we present a globally optimized implementation of the eBURST algorithm - goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. goeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several
Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution
NASA Astrophysics Data System (ADS)
Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing
2016-12-01
The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.
Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.
Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing
2016-12-21
The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of [Formula: see text], yielding a mean Dice similarity coefficient of [Formula: see text], and an average symmetric surface distance of [Formula: see text] mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.
Comparison of global optimization approaches for robust calibration of hydrologic model parameters
NASA Astrophysics Data System (ADS)
Jung, I. W.
2015-12-01
Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Energy landscape paving with local search for global optimization of the BLN off-lattice model
NASA Astrophysics Data System (ADS)
Liu, Jingfa; Huang, Weibo; Liu, Wenjie; Song, Beibei; Sun, Yuanyuan; Chen, Mao
2014-02-01
The optimization problem for finding the global minimum energy structure is one of the main problems of protein structure prediction and is known to be an NP-hard problem in computational molecular biology. The low-energy conformational search problem in the hydrophobic-hydrophilic-neutral (BLN) off-lattice model is studied. We convert the problem into an unconstrained optimization problem by introducing the penalty function. By putting forward a new updating mechanism of the histogram function in the energy landscape paving (ELP) method and incorporating heuristic conformation update strategies into the ELP method, we obtain an improved ELP (IELP) method. Subsequently, by combining the IELP method with the local search (LS) based on the gradient descent method, we propose a hybrid algorithm, denoted by IELP-LS, for the conformational search of the off-lattice BLN model. Simulation results indicate that IELP-LS can find lower-energy states than other methods in the literature, showing that the proposed method is an effective tool for global optimization in the BLN off-lattice protein model.
Liang, Faming; Cheng, Yichen; Lin, Guang
2014-06-13
Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.
QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.
Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon
2012-01-01
Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.
3-D carotid multi-region MRI segmentation by globally optimal evolution of coupled surfaces.
Ukwatta, Eranga; Yuan, Jing; Rajchl, Martin; Qiu, Wu; Tessier, David; Fenster, Aaron
2013-04-01
In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.
On the use of global optimization methods for acoustic source mapping.
Malgoezar, Anwar M N; Snellen, Mirjam; Merino-Martinez, Roberto; Simons, Dick G; Sijtsma, Pieter
2017-01-01
Conventional beamforming with a microphone array is a well-established method for localizing and quantifying sound sources. It provides estimates for the source strengths on a predefined grid by determining the agreement between the pressures measured and those modeled for a source located at the grid point under consideration. As such, conventional beamforming can be seen as an exhaustive search for those locations that provide a maximum match between measured and modeled pressures. In this contribution, the authors propose to, instead of the exhaustive search, use an efficient global optimization method to search for the source locations that maximize the agreement between model and measurement. Advantages are two-fold. First, the efficient optimization allows for inclusion of more unknowns, such as the source position in three-dimensional or environmental parameters such as the speed of sound. Second, the model for the received pressure field can be readily adapted to reflect, for example, the presence of more sound sources or environmental parameters that affect the received signals. For the work considered, the global optimization method, Differential Evolution, is selected. Results with simulated and experimental data show that sources can be accurately identified, including the distance from the source to the array.
A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments
NASA Technical Reports Server (NTRS)
McDowell, Mark
2008-01-01
An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent
NASA Astrophysics Data System (ADS)
Peng, Guanghan; Lu, Weizhen; He, Hongdi
2016-09-01
In this paper, a new car-following model is proposed by considering the global average optimal velocity difference effect on the basis of the full velocity difference (FVD) model. We investigate the influence of the global average optimal velocity difference on the stability of traffic flow by making use of linear stability analysis. It indicates that the stable region will be enlarged by taking the global average optimal velocity difference effect into account. Subsequently, the mKdV equation near the critical point and its kink-antikink soliton solution, which can describe the traffic jam transition, is derived from nonlinear analysis. Furthermore, numerical simulations confirm that the effect of the global average optimal velocity difference can efficiently improve the stability of traffic flow, which show that our new consideration should be taken into account to suppress the traffic congestion for car-following theory.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2015-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allow-ing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for com-putational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2016-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
NASA Astrophysics Data System (ADS)
Rogers, Adam
2012-01-01
Strong gravitational lensing produces multiple distorted images of a background source when it is closely aligned with a mass distribution along the line of sight. The lensed images provide constraints on the parameters of a model of the lens, and the images themselves can be inverted providing a model of the source. Both of these aspects of lensing are extremely valuable, as lensing depends on the total matter distribution, both luminous and dark. Furthermore, lensed sources are commonly located at cosmological distances and are magnified by the lensing effect. This provides a chance to image sources that would be unobservable when viewed with conventional optics. The semilinear method expresses the source modeling step as a least-squares problem for a given set of lens model parameters. The blurring effect due to the point spread function of the instrument used to observe the lensed images is also taken into account. In general, regularization is needed to solve the source deconvolution problem. We use Krylov subspace methods to solve for the pixelated sources. These optimization techniques, such as the Conjugate Gradient method, provide natural regularizing effects from simple truncated iteration. Using these routines, we are able to avoid the explicit construction of the lens and blurring matrices and solve the least squares source optimization problem iteratively. We explore several regularization parameter selection methods commonly used in standard image deconvolution problems, which lead to previously derived expressions for the number of source degrees of freedom. The parameters that describe the lens density distribution are found by global optimization methods including genetic algorithms and particle swarm optimizers. In general, global optimizers are useful in non-linear optimization problems such as lens modeling due to their parameter space mapping capabilities. However, these optimization methods require many function evaluations and iterative
2012-01-01
Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by
Miró, Anton; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Egea, Jose A; Jiménez, Laureano
2012-05-10
The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
Lee, JongHyup; Pak, Dohyun
2016-08-29
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
NASA Astrophysics Data System (ADS)
Do, Khac Duc
2015-03-01
This paper presents a design of optimal controllers with respect to a meaningful cost function to force an underactuated omni-directional intelligent navigator (ODIN) under unknown constant environmental loads to track a reference trajectory in two-dimensional space. Motivated by the vehicle's steering practice, the yaw angle regarded as a virtual control plus the surge thrust force are used to force the position of the vehicle to globally track its reference trajectory. The control design is based on several recent results developed for inverse optimal control and stability analysis of nonlinear systems, a new design of bounded disturbance observers, and backstepping and Lyapunov's direct methods. Both state- and output-feedback control designs are addressed. Simulations are included to illustrate the effectiveness of the proposed results.
Using support vector machine and dynamic parameter encoding to enhance global optimization
NASA Astrophysics Data System (ADS)
Zheng, Z.; Chen, X.; Liu, C.; Huang, K.
2016-05-01
This study presents an approach which combines support vector machine (SVM) and dynamic parameter encoding (DPE) to enhance the run-time performance of global optimization with time-consuming fitness function evaluations. SVMs are used as surrogate models to partly substitute for fitness evaluations. To reduce the computation time and guarantee correct convergence, this work proposes a novel strategy to adaptively adjust the number of fitness evaluations needed according to the approximate error of the surrogate model. Meanwhile, DPE is employed to compress the solution space, so that it not only accelerates the convergence but also decreases the approximate error. Numerical results of optimizing a few benchmark functions and an antenna in a practical application are presented, which verify the feasibility, efficiency and robustness of the proposed approach.
PID Controller Design Based on Global Optimization Technique with Additional Constraints
NASA Astrophysics Data System (ADS)
Ozana, Stepan; Docekal, Tomas
2016-05-01
This paper deals with design of PID controller with the use of methods of global optimization implemented in Matlab environment and Optimization Toolbox. It is based on minimization of a chosen integral criterion with respect to additional requirements on control quality such as overshoot, phase margin and limits for manipulated value. The objective function also respects user-defined weigh coefficients for its particular terms for a different penalization of individual requirements that often clash each other such as for example overshoot and phase margin. The described solution is designated for continuous linear time-invariant static systems up to 4th order and thus efficient for the most of real control processes in practice.
A genetic algorithm for first principles global structure optimization of supported nano structures
Vilhelmsen, Lasse B.; Hammer, Bjørk
2014-07-28
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA’s with a description of optimal parameters to use. New results for the adsorption of M{sub 8} clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO{sub 2}(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
Electronic neural network for solving traveling salesman and similar global optimization problems
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
Prediction of Protein Structure by Ab Initio Global Optimization of Potential Energy
NASA Astrophysics Data System (ADS)
Scheraga, Harold; Liwo, Adam; Pillardy, Jarek; Czaplewski, Czarek; Lee, Jooyoung; Ripoll, Daniel; Kazmierkiewicz, Rajmund; Oldziej, Stanislaw; Arnautova, Yelena; Wedemeyer, William; Saunders, Jeff
2002-03-01
A hierarchical ab initio method is used to predict the three-dimensional structures of globular proteins. The protein is described initially as a virtual-bond chain, with its side chains represented as ellipsoids. After searching the conformational space with this united-residue (UNRES) model, with many-body interactions, the UNRES model is converted to an all-atom model, and the global optimization of the potential energy is continued with the ECEPP/3 force field and the SRFOPT hydration free-energy. Results of the application of this methodology in the CASP3 and CASP4 exercises, and more-recent methodological improvements, will be presented.
Design of zero reference codes by means of a global optimization method.
Saez-Landete, José; Alonso, José; Bernabeu, Eusebio
2005-01-10
The grating measurement systems can be used for displacement and angle measurements. They require of zero reference codes to obtain zero reference signals and absolute measures. The zero reference signals are obtained from the autocorrelation of two identical zero reference codes. The design of codes which generate optimum signals is rather complex, especially for larges codes. In this paper we present a global optimization method, a DIRECT algorithm for the design of zero reference codes. This method proves to be a powerful tool for solving this inverse problem.
Design of zero reference codes by means of a global optimization method
NASA Astrophysics Data System (ADS)
Saez Landete, José; Alonso, José; Bernabeu, Eusebio
2005-01-01
The grating measurement systems can be used for displacement and angle measurements. They require of zero reference codes to obtain zero reference signals and absolute measures. The zero reference signals are obtained from the autocorrelation of two identical zero reference codes. The design of codes which generate optimum signals is rather complex, especially for larges codes. In this paper we present a global optimization method, a DIRECT algorithm for the design of zero reference codes. This method proves to be a powerful tool for solving this inverse problem.
Model-data fusion across ecosystems: from multi-site optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-05-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net carbon (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multi-site approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are: reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modeling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multi-site parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modeled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global scale evaluation with remote sensing NDVI measurements indicates an improvement of the simulated seasonal variations of
Model-data fusion across ecosystems: from multisite optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-11-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP - gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index
A Global Scale 30m Water Surface Detection Optimized and Validated for Landsat 8
NASA Astrophysics Data System (ADS)
Pekel, J. F.; Cottam, A.; Clerici, M.; Belward, A.; Dubois, G.; Bartholome, E.; Gorelick, N.
2014-12-01
Life on Earth as we know it is impossible without water. Its importance to biological diversity, human well-being and the very functioning of the Earth-system cannot be overstressed, but we have remarkably little detailed knowledge concerning the spatial and temporal distribution of this vital resource. Earth observing satellites operating with high temporal revisits yet moderate spatial resolution have provided global datasets documenting spatial and temporal changes to water bodies on the Earth's surface. Landsat 8 has a data acquisition strategy such that global coverage of all land surfaces now occurs more frequently than from any preceding Landsat mission and provides 30 m resolution data. Whilst not the last word in temporal sampling this presents a basis for mapping and monitoring changes to global surface water resources at unprecedented levels of spatial detail. In this paper we provide a first 30 m resolution global synthesis of surface water occurrence, we document permanent water surfaces, seasonal water surfaces and always-dry surfaces. These products have been derived by optimizing a methodology previously developed for use with moderate resolution MODIS imagery for use with Landsat 8. The approach is based on a transformation of RGB color space into HSV combined with a sequence of cloud, topographic and temperature masks. Analysis at the global scale used the Google Earth Engine platform applied to all Landsat 8 acquisitions between June 2013 and June 2014. Systematic validation is done and demonstrated our ability to map surface water. Our method can be applied to other Landsat missions offering the potential to document changes in surface water over three decades; our study shows examples illustrating the capacity to map new water surfaces and ephemeral water surfaces in addition to the three previous classes. Thanks to an optimized data acquisition strategy, a full-free and open data policy and the processing capacity of the GEE global land
Protein structure modeling for CASP10 by multiple layers of global optimization.
Joo, Keehyoung; Lee, Juyong; Sim, Sangjin; Lee, Sun Young; Lee, Kiho; Heo, Seungryong; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung
2014-02-01
In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps.
NASA Astrophysics Data System (ADS)
Penuelas, J.; Sardans, J.
2016-12-01
There are several processes underlying the shifts in organism's functions, species composition and ecosystem adaptation to the fast rates of environmental changes resulting from global change drivers. These environmental changes imply a shift in the use and cycling of resources, and in particular of nutrients, by organisms, communities and ecosystems. We will review the different use of bio-elements related to global change drivers such as climate change (warming and drought), increased concentrations of atmospheric CO2, or expansion of invasive species among others. Thereafter, we will discuss the resulting progressive change in nutrient cycling and its coupling with organism's, species, communities and ecosystem function in the frame of the biogeochemical niche hypothesis (Peñuelas et al., 2008; 2010). This hypothesis, based on the fact that each bio-element participates in different proportion in distinct organism's structures and functions, claims that each species has an optimal elemental composition and stoichiometry that allows reaching an optimal functioning. Species are nonetheless expected to exhibit a certain degree of stoichiometric flexibility (adaptive capacity) necessary to respond to environmental changes and competition, probably with a trade-off with stability. We will present data for the dominant tree species in Europe showing that the elemental foliar composition differences among species can be explained by their phylogenetic distances, current climate differences in their distribution areas and niche speciation in sympatric species, but also by some more recent human-driven impacts such as N deposition, thus showing the suitability and sensitivity of the "biogeochemical niche" concept to understand recent organism's, species, and ecosystem responses to novel environmental conditions imposed by human activity. We will finally discuss possible clues to improve the projections of ecosystem shifts in global change scenarios based on this concept.
Export dynamics as an optimal growth problem in the network of global economy
NASA Astrophysics Data System (ADS)
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-08-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
Export dynamics as an optimal growth problem in the network of global economy.
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L
2016-08-17
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
Export dynamics as an optimal growth problem in the network of global economy
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years. PMID:27530505
Song, Zhaoliang; Parr, Jeffrey F; Guo, Fengshan
2013-01-01
The occlusion of carbon (C) by phytoliths, the recalcitrant silicified structures deposited within plant tissues, is an important persistent C sink mechanism for croplands and other grass-dominated ecosystems. By constructing a silica content-phytolith content transfer function and calculating the magnitude of phytolith C sink in global croplands with relevant crop production data, this study investigated the present and potential of phytolith C sinks in global croplands and its contribution to the cropland C balance to understand the cropland C cycle and enhance long-term C sequestration in croplands. Our results indicate that the phytolith sink annually sequesters 26.35 ± 10.22 Tg of carbon dioxide (CO2) and may contribute 40 ± 18% of the global net cropland soil C sink for 1961-2100. Rice (25%), wheat (19%) and maize (23%) are the dominant contributing crop species to this phytolith C sink. Continentally, the main contributors are Asia (49%), North America (17%) and Europe (16%). The sink has tripled since 1961, mainly due to fertilizer application and irrigation. Cropland phytolith C sinks may be further enhanced by adopting cropland management practices such as optimization of cropping system and fertilization.
Song, Zhaoliang; Parr, Jeffrey F.; Guo, Fengshan
2013-01-01
The occlusion of carbon (C) by phytoliths, the recalcitrant silicified structures deposited within plant tissues, is an important persistent C sink mechanism for croplands and other grass-dominated ecosystems. By constructing a silica content-phytolith content transfer function and calculating the magnitude of phytolith C sink in global croplands with relevant crop production data, this study investigated the present and potential of phytolith C sinks in global croplands and its contribution to the cropland C balance to understand the cropland C cycle and enhance long-term C sequestration in croplands. Our results indicate that the phytolith sink annually sequesters 26.35±10.22 Tg of carbon dioxide (CO2) and may contribute 40±18% of the global net cropland soil C sink for 1961–2100. Rice (25%), wheat (19%) and maize (23%) are the dominant contributing crop species to this phytolith C sink. Continentally, the main contributors are Asia (49%), North America (17%) and Europe (16%). The sink has tripled since 1961, mainly due to fertilizer application and irrigation. Cropland phytolith C sinks may be further enhanced by adopting cropland management practices such as optimization of cropping system and fertilization. PMID:24066067
NASA Astrophysics Data System (ADS)
Hew, Y. M.; Linscott, I.; Close, S.
2015-12-01
Meteoroids and orbital debris, collectively referred to as hypervelocity impactors, travel between 7 and 72 km/s in free space. Upon their impact onto the spacecraft, the energy conversion from kinetic to ionization/vaporization occurs within a very brief timescale and results in a small and dense expanding plasma with a very strong optical flash. The radio frequency (RF) emission produced by this plasma can potentially lead to electrical anomalies within the spacecraft. In addition, space weather, such as solar activity and background plasma, can establish spacecraft conditions which can exaggerate the damages done by these impacts. During the impact, a very strong impact flash will be generated. Through the studying of this emission spectrum of the impact, we hope to study the impact generated gas cloud/plasma properties. The impact flash emitted from a ground-based hypervelocity impact test is long expected by many scientists to contain the characteristics of the impact generated plasma, such as plasma temperature and density. This paper presents a method for the time-resolved plasma temperature estimation using three-color visible band photometry data with a global pattern search optimization method. The equilibrium temperature of the plasma can be estimated using an optical model which accounts for both the line emission and continuum emission from the plasma. Using a global pattern search based optimizer, the model can isolate the contribution of the continuum emission versus the line emission from the plasma. The plasma temperature can thus be estimated. Prior to the optimization step, a Gaussian process is also applied to extract the optical emission signal out of the noisy background. The resultant temperature and line-to-continuum emission weighting factor are consistent with the spectrum of the impactor material and current literature.
Romero, Vicente J.
1999-05-18
Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed here (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.
Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization
NASA Technical Reports Server (NTRS)
Moeller, Timothy M.; Wilhite, Alan W.; Liles, Kaitlin A.
2014-01-01
Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.
NASA Astrophysics Data System (ADS)
Huang, Zhipeng; Gao, Lihong; Wang, Yangwei; Wang, Fuchi
2016-09-01
The Johnson-Cook (J-C) constitutive model is widely used in the finite element simulation, as this model shows the relationship between stress and strain in a simple way. In this paper, a cluster global optimization algorithm is proposed to determine the J-C constitutive model parameters of materials. A set of assumed parameters is used for the accuracy verification of the procedure. The parameters of two materials (401 steel and 823 steel) are determined. Results show that the procedure is reliable and effective. The relative error between the optimized and assumed parameters is no more than 4.02%, and the relative error between the optimized and assumed stress is 0.2% × 10-5. The J-C constitutive parameters can be determined more precisely and quickly than the traditional manual procedure. Furthermore, all the parameters can be simultaneously determined using several curves under different experimental conditions. A strategy is also proposed to accurately determine the constitutive parameters.
Piro, M. H. A.; Simunovic, S.
2016-03-17
Several global optimization methods are reviewed that attempt to ensure that the integral Gibbs energy of a closed isothermal isobaric system is a global minimum to satisfy the necessary and sufficient conditions for thermodynamic equilibrium. In particular, the integral Gibbs energy function of a multicomponent system containing non-ideal phases may be highly non-linear and non-convex, which makes finding a global minimum a challenge. Consequently, a poor numerical approach may lead one to the false belief of equilibrium. Furthermore, confirming that one reaches a global minimum and that this is achieved with satisfactory computational performance becomes increasingly more challenging in systemsmore » containing many chemical elements and a correspondingly large number of species and phases. Several numerical methods that have been used for this specific purpose are reviewed with a benchmark study of three of the more promising methods using five case studies of varying complexity. A modification of the conventional Branch and Bound method is presented that is well suited to a wide array of thermodynamic applications, including complex phases with many constituents and sublattices, and ionic phases that must adhere to charge neutrality constraints. Also, a novel method is presented that efficiently solves the system of linear equations that exploits the unique structure of the Hessian matrix, which reduces the calculation from a O(N3) operation to a O(N) operation. As a result, this combined approach demonstrates efficiency, reliability and capabilities that are favorable for integration of thermodynamic computations into multi-physics codes with inherent performance considerations.« less
Piro, M. H. A.; Simunovic, S.
2016-03-17
Several global optimization methods are reviewed that attempt to ensure that the integral Gibbs energy of a closed isothermal isobaric system is a global minimum to satisfy the necessary and sufficient conditions for thermodynamic equilibrium. In particular, the integral Gibbs energy function of a multicomponent system containing non-ideal phases may be highly non-linear and non-convex, which makes finding a global minimum a challenge. Consequently, a poor numerical approach may lead one to the false belief of equilibrium. Furthermore, confirming that one reaches a global minimum and that this is achieved with satisfactory computational performance becomes increasingly more challenging in systems containing many chemical elements and a correspondingly large number of species and phases. Several numerical methods that have been used for this specific purpose are reviewed with a benchmark study of three of the more promising methods using five case studies of varying complexity. A modification of the conventional Branch and Bound method is presented that is well suited to a wide array of thermodynamic applications, including complex phases with many constituents and sublattices, and ionic phases that must adhere to charge neutrality constraints. Also, a novel method is presented that efficiently solves the system of linear equations that exploits the unique structure of the Hessian matrix, which reduces the calculation from a O(N^{3}) operation to a O(N) operation. As a result, this combined approach demonstrates efficiency, reliability and capabilities that are favorable for integration of thermodynamic computations into multi-physics codes with inherent performance considerations.
NASA Astrophysics Data System (ADS)
Libraro, Paola
The general electric propulsion orbit-raising maneuver of a spacecraft must contend with four main limiting factors: the longer time of flight, multiple eclipses prohibiting continuous thrusting, long exposure to radiation from the Van Allen belt and high power requirement of the electric engines. In order to optimize a low-thrust transfer with respect to these challenges, the choice of coordinates and corresponding equations of motion used to describe the kinematical and dynamical behavior of the satellite is of critical importance. This choice can potentially affect the numerical optimization process as well as limit the set of mission scenarios that can be investigated. To increase the ability to determine the feasible set of mission scenarios able to address the challenges of an all-electric orbit-raising, a set of equations free of any singularities is required to consider a completely arbitrary injection orbit. For this purpose a new quaternion-based formulation of a spacecraft translational dynamics that is globally nonsingular has been developed. The minimum-time low-thrust problem has been solved using the new set of equations of motion inside a direct optimization scheme in order to investigate optimal low-thrust trajectories over the full range of injection orbit inclinations between 0 and 90 degrees with particular focus on high-inclinations. The numerical results consider a specific mission scenario in order to analyze three key aspects of the problem: the effect of the initial guess on the shape and duration of the transfer, the effect of Earth oblateness on transfer time and the role played by, radiation damage and power degradation in all-electric minimum-time transfers. Finally trade-offs between mass and cost savings are introduced through a test case.
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, Isaac E.
2006-01-01
A new stochastic method for locating the global minimum of a multidimensional function inside a rectangular hyperbox is presented. A sampling technique is employed that makes use of the procedure known as grammatical evolution. The method can be considered as a "genetic" modification of the Controlled Random Search procedure due to Price. The user may code the objective function either in C++ or in Fortran 77. We offer a comparison of the new method with others of similar structure, by presenting results of computational experiments on a set of test functions. Program summaryTitle of program: GenPrice Catalogue identifier:ADWP Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADWP Program available from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: the tool is designed to be portable in all systems running the GNU C++ compiler Installation: University of Ioannina, Greece Programming language used: GNU-C++, GNU-C, GNU Fortran-77 Memory required to execute with typical data: 200 KB No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorized or parallelized?: no No. of lines in distributed program, including test data, etc.:13 135 No. of bytes in distributed program, including test data, etc.: 78 512 Distribution format: tar. gz Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a "least squares" type of objective, one may encounter many local minima that do not correspond to solutions, i.e. minima with values
Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet
NASA Technical Reports Server (NTRS)
Smith, Eric A.; Adams, James; Baptista, Pedro; Haddad, Ziad; Iguchi, Toshio; Im, Eastwood; Kummerow, Christian; Einaudi, Franco (Technical Monitor)
2001-01-01
Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight
Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet
NASA Technical Reports Server (NTRS)
Smith, Eric A.; Adams, James; Baptista, Pedro; Haddad, Ziad; Iguchi, Toshio; Im, Eastwood; Kummerow, Christian; Einaudi, Franco (Technical Monitor)
2001-01-01
Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Lightsey, E. Glenn; Markley, F. Landis
1998-01-01
In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.
GENOPT 2016: Design of a generalization-based challenge in global optimization
NASA Astrophysics Data System (ADS)
Battiti, Roberto; Sergeyev, Yaroslav; Brunato, Mauro; Kvasov, Dmitri
2016-10-01
While comparing results on benchmark functions is a widely used practice to demonstrate the competitiveness of global optimization algorithms, fixed benchmarks can lead to a negative data mining process. To avoid this negative effect, the GENOPT contest benchmarks can be used which are based on randomized function generators, designed for scientific experiments, with fixed statistical characteristics but individual variation of the generated instances. The generators are available to participants for off-line tests and online tuning schemes, but the final competition is based on random seeds communicated in the last phase through a cooperative process. A brief presentation and discussion of the methods and results obtained in the framework of the GENOPT contest are given in this contribution.
Saiz-Urra, Liane; Bustillo Pérez, Antonio J; Cruz-Monteagudo, Maykel; Pinedo-Rivilla, Cristina; Aleu, Josefina; Hernández-Galán, Rosario; Collado, Isidro G
2009-06-10
Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(4'-chlorophenyl)-2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOP-DESIRE methodology was used for this purpose providing reliable ranking models that can be used later.
Explanation of how to run the global local optimization code (GLO) to find surface heat flux
Aceves, S; Sahai, V; Stein, W
1999-03-01
From the evaluation[1] of the inverse techniques available, it was determined that the Global Local Optimization Code[2] can determine the surface heat flux using known experimental data at various points in the geometry. This code uses a whole domain approach in which an analysis code (such as TOPAZ2D or ABAQUS) can be run to get the appropriate data needed to minimize the heat flux function. This document is a compilation of our notes on how to run this code to find the surface heat flux. First, the code is described and the overall set-up procedure is reviewed. Then, creation of the configuration file is described. A specific configuration file is given with appropriate explanation. Using this information, the reader should be able to run GLO to find the surface heat flux.
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Nacelle Chine Installation Based on Wind-Tunnel Test Using Efficient Global Optimization
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Yokokawa, Yuzuru; Murayama, Mitsuhiro; Ito, Takeshi; Jeong, Shinkyu; Yamamoto, Kazuomi
Design exploration of a nacelle chine installation was carried out. The nacelle chine improves stall performance when deploying multi-element high-lift devices. This study proposes an efficient design process using a Kriging surrogate model to determine the nacelle chine installation point in wind-tunnel tests. The design exploration was conducted in a wind-tunnel using the JAXA high-lift aircraft model at the JAXA Large-scale Low-speed Wind Tunnel. The objective was to maximize the maximum lift. The chine installation points were designed on the engine nacelle in the axial and chord-wise direction, while the geometry of the chine was fixed. In the design process, efficient global optimization (EGO) which includes Kriging model and genetic algorithm (GA) was employed. This method makes it possible both to improve the accuracy of the response surface and to explore the global optimum efficiently. Detailed observations of flowfields using the Particle Image Velocimetry method confirmed the chine effect and design results.
Local search for optimal global map generation using mid-decadal landsat images
Khatib, L.; Gasch, J.; Morris, Robert; Covington, S.
2007-01-01
NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Corzo, Gerald; Solomatine, Dimitri
2007-05-01
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.
Sartelli, Massimo; Weber, Dieter G; Ruppé, Etienne; Bassetti, Matteo; Wright, Brian J; Ansaloni, Luca; Catena, Fausto; Coccolini, Federico; Abu-Zidan, Fikri M; Coimbra, Raul; Moore, Ernest E; Moore, Frederick A; Maier, Ronald V; De Waele, Jan J; Kirkpatrick, Andrew W; Griffiths, Ewen A; Eckmann, Christian; Brink, Adrian J; Mazuski, John E; May, Addison K; Sawyer, Rob G; Mertz, Dominik; Montravers, Philippe; Kumar, Anand; Roberts, Jason A; Vincent, Jean-Louis; Watkins, Richard R; Lowman, Warren; Spellberg, Brad; Abbott, Iain J; Adesunkanmi, Abdulrashid Kayode; Al-Dahir, Sara; Al-Hasan, Majdi N; Agresta, Ferdinando; Althani, Asma A; Ansari, Shamshul; Ansumana, Rashid; Augustin, Goran; Bala, Miklosh; Balogh, Zsolt J; Baraket, Oussama; Bhangu, Aneel; Beltrán, Marcelo A; Bernhard, Michael; Biffl, Walter L; Boermeester, Marja A; Brecher, Stephen M; Cherry-Bukowiec, Jill R; Buyne, Otmar R; Cainzos, Miguel A; Cairns, Kelly A; Camacho-Ortiz, Adrian; Chandy, Sujith J; Che Jusoh, Asri; Chichom-Mefire, Alain; Colijn, Caroline; Corcione, Francesco; Cui, Yunfeng; Curcio, Daniel; Delibegovic, Samir; Demetrashvili, Zaza; De Simone, Belinda; Dhingra, Sameer; Diaz, José J; Di Carlo, Isidoro; Dillip, Angel; Di Saverio, Salomone; Doyle, Michael P; Dorj, Gereltuya; Dogjani, Agron; Dupont, Hervé; Eachempati, Soumitra R; Enani, Mushira Abdulaziz; Egiev, Valery N; Elmangory, Mutasim M; Ferrada, Paula; Fitchett, Joseph R; Fraga, Gustavo P; Guessennd, Nathalie; Giamarellou, Helen; Ghnnam, Wagih; Gkiokas, George; Goldberg, Staphanie R; Gomes, Carlos Augusto; Gomi, Harumi; Guzmán-Blanco, Manuel; Haque, Mainul; Hansen, Sonja; Hecker, Andreas; Heizmann, Wolfgang R; Herzog, Torsten; Hodonou, Adrien Montcho; Hong, Suk-Kyung; Kafka-Ritsch, Reinhold; Kaplan, Lewis J; Kapoor, Garima; Karamarkovic, Aleksandar; Kees, Martin G; Kenig, Jakub; Kiguba, Ronald; Kim, Peter K; Kluger, Yoram; Khokha, Vladimir; Koike, Kaoru; Kok, Kenneth Y Y; Kong, Victory; Knox, Matthew C; Inaba, Kenji; Isik, Arda; Iskandar, Katia; Ivatury, Rao R; Labbate, Maurizio; Labricciosa, Francesco M; Laterre, Pierre-François; Latifi, Rifat; Lee, Jae Gil; Lee, Young Ran; Leone, Marc; Leppaniemi, Ari; Li, Yousheng; Liang, Stephen Y; Loho, Tonny; Maegele, Marc; Malama, Sydney; Marei, Hany E; Martin-Loeches, Ignacio; Marwah, Sanjay; Massele, Amos; McFarlane, Michael; Melo, Renato Bessa; Negoi, Ionut; Nicolau, David P; Nord, Carl Erik; Ofori-Asenso, Richard; Omari, AbdelKarim H; Ordonez, Carlos A; Ouadii, Mouaqit; Pereira Júnior, Gerson Alves; Piazza, Diego; Pupelis, Guntars; Rawson, Timothy Miles; Rems, Miran; Rizoli, Sandro; Rocha, Claudio; Sakakhushev, Boris; Sanchez-Garcia, Miguel; Sato, Norio; Segovia Lohse, Helmut A; Sganga, Gabriele; Siribumrungwong, Boonying; Shelat, Vishal G; Soreide, Kjetil; Soto, Rodolfo; Talving, Peep; Tilsed, Jonathan V; Timsit, Jean-Francois; Trueba, Gabriel; Trung, Ngo Tat; Ulrych, Jan; van Goor, Harry; Vereczkei, Andras; Vohra, Ravinder S; Wani, Imtiaz; Uhl, Waldemar; Xiao, Yonghong; Yuan, Kuo-Ching; Zachariah, Sanoop K; Zahar, Jean-Ralph; Zakrison, Tanya L; Corcione, Antonio; Melotti, Rita M; Viscoli, Claudio; Viale, Perluigi
2016-01-01
Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs.
How can we Optimize Global Satellite Observations of Glacier Velocity and Elevation Changes?
NASA Astrophysics Data System (ADS)
Willis, M. J.; Pritchard, M. E.; Zheng, W.
2015-12-01
We have started a global compilation of glacier surface elevation change rates measured by altimeters and differencing of Digital Elevation Models and glacier velocities measured by Synthetic Aperture Radar (SAR) and optical feature tracking as well as from Interferometric SAR (InSAR). Our goal is to compile statistics on recent ice flow velocities and surface elevation change rates near the fronts of all available glaciers using literature and our own data sets of the Russian Arctic, Patagonia, Alaska, Greenland and Antarctica, the Himalayas, and other locations. We quantify the percentage of the glaciers on the planet that can be regarded as fast flowing glaciers, with surface velocities of more than 50 meters per year, while also recording glaciers that have elevation change rates of more than 2 meters per year. We examine whether glaciers have significant interannual variations in velocities, or have accelerated or stagnated where time series of ice motions are available. We use glacier boundaries and identifiers from the Randolph Glacier Inventory. Our survey highlights glaciers that are likely to react quickly to changes in their mass accumulation rates. The study also identifies geographical areas where our knowledge of glacier dynamics remains poor. Our survey helps guide how frequently observations must be made in order to provide quality satellite-derived velocity and ice elevation observations at a variety of glacier thermal regimes, speeds and widths. Our objectives are to determine to what extent the joint NASA and Indian Space Research Organization Synthetic Aperture Radar mission (NISAR) will be able to provide global precision coverage of ice speed changes and to determine how to optimize observations from the global constellation of satellite missions to record important changes to glacier elevations and velocities worldwide.
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
Protein structure prediction using global optimization by basin-hopping with NMR shift restraints
NASA Astrophysics Data System (ADS)
Hoffmann, Falk; Strodel, Birgit
2013-01-01
Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10 000 basin-hopping steps. The effect of adding chemical shift restraints is that the α and β secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.
An ITK framework for deterministic global optimization for medical image registration
NASA Astrophysics Data System (ADS)
Dru, Florence; Wachowiak, Mark P.; Peters, Terry M.
2006-03-01
Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.
A Perturbation Based Chaotic System Exploiting the Quasi-Newton Method for Global Optimization
NASA Astrophysics Data System (ADS)
Tatsumi, Keiji; Tanino, Tetsuzo
The chaotic system has been exploited in metaheuristic methods of solving continuous global optimization problems. Recently, the gradient method with perturbation (GP) was proposed, which was derived from the steepest descent method for the problem with additional perturbation terms, and it was reported that chaotic metaheuristics with the GP have good performances of solving some benchmark problems. Moreover, the sufficient condition of its parameter values was theoretically shown under which its updating system is chaotic. However, the sufficient condition of its chaoticity and the width of strange attractor around each local minimum, which are important properties for exploiting the chaotic system in optimization, deeply depend on the eigenvalues of the Hessian matrix of the objective function at the local minimum. Thus, if the eigenvalues of different local minima are widely different from each other, or if it is different in different problems, such properties can cause the difficulty of selecting appropriate parameter values for an effective search. Therefore, in this paper, we propose modified GPs based on the quasi-Newton method instead of the steepest descent method, where their chaoticities and the width of strange attractor do not depend on the eigenvalue of the Hessian matrix at any local minimum due to the scale invariant of the quasi-Newton method. In addition, we empirically demonstrate that the parameter selection of the proposed methods is easier than the original GP, especially with respect to the step-size, and the chaotic metaheuristics with the proposed methods can find better solutions for some multimodal functions.
Shen, Bo; Abdelaziz, Omar; Shrestha, Som S
2017-01-01
Oak Ridge National laboratory (ORNL) recently conducted extensive laboratory, drop-in investigations for lower Global Warming Potential (GWP) refrigerants to replace R-22 and R-410A. ORNL studied propane, DR-3, ARM-20B, N-20B and R-444B as lower GWP refrigerant replacement for R-22 in a mini-split room air conditioner (RAC) originally designed for R-22; and, R-32, DR-55, ARM-71A, and L41-2, in a mini-split RAC designed for R-410A. We obtained laboratory testing results with very good energy balance and nominal measurement uncertainty. Drop-in studies are not enough to judge the overall performance of the alternative refrigerants since their thermodynamic and transport properties might favor different heat exchanger configurations, e.g. cross-flow, counter flow, etc. This study compares optimized performances of individual refrigerants using a physics-based system model tools. The DOE/ORNL Heat Pump Design Model (HPDM) was used to model the mini-split RACs by inputting detailed heat exchangers geometries, compressor displacement and efficiencies as well as other relevant system components. The RAC models were calibrated against the lab data for each individual refrigerant. The calibrated models were then used to conduct a design optimization for the cooling performance by varying the compressor displacement to match the required capacity, and changing the number of circuits, refrigerant flow direction, tube diameters, air flow rates in the condenser and evaporator at 100% and 50% cooling capacities. This paper compares the optimized performance results for all alternative refrigerants and highlights best candidates for R-22 and R-410A replacement.
NASA Astrophysics Data System (ADS)
Arteaga, Lionel; Pahlow, Markus; Oschlies, Andreas
2014-07-01
The widely used concept of constant "Redfield" phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions. 2014. American Geophysical Union. All Rights Reserved.
Recursive Ant Colony Global Optimization: a new technique for the inversion of geophysical data
NASA Astrophysics Data System (ADS)
Gupta, D. K.; Gupta, J. P.; Arora, Y.; Singh, U. K.
2011-12-01
We present a new method called Recursive Ant Colony Global Optimization (RACO) technique, a modified form of general ACO, which can be used to find the best solutions to inversion problems in geophysics. RACO simulates the social behaviour of ants to find the best path between the nest and the food source. A new term depth has been introduced, which controls the extent of recursion. A selective number of cities get qualified for the successive depth. The results of one depth are used to construct the models for the next depth and the range of values for each of the parameters is reduced without any change to the number of models. The three additional steps performed after each depth, are the pheromone tracking, pheromone updating and city selection. One of the advantages of RACO over ACO is that if a problem has multiple solutions, then pheromone accumulation will take place at more than one city thereby leading to formation of multiple nested ACO loops within the ACO loop of the previous depth. Also, while the convergence of ACO is almost linear, RACO shows exponential convergence and hence is faster than the ACO. RACO proves better over some other global optimization techniques, as it does not require any initial values to be assigned to the parameters function. The method has been tested on some mathematical functions, synthetic self-potential (SP) and synthetic gravity data. The obtained results reveal the efficiency and practicability of the method. The method is found to be efficient enough to solve the problems of SP and gravity anomalies due to a horizontal cylinder, a sphere, an inclined sheet and multiple idealized bodies buried inside the earth. These anomalies with and without noise were inverted using the RACO algorithm. The obtained results were compared with those obtained from the conventional methods and it was found that RACO results are more accurate. Finally this optimization technique was applied to real field data collected over the Surda
NASA Astrophysics Data System (ADS)
Vaziri Yazdi Pin, Mohammad
practices. Single criterion optimization algorithms using mathematical programming for globally optimal solutions have been developed for three objectives of cost, reliability, and the social/environmental impacts. Additional algorithms for inclusions of upgrade and optimal load assignment possibilities have been developed. Algorithms have been developed to handle the expansion as a multiobjective decision process. Typical data from both major investor owned and major municipal utilities operating in California USA, have been utilized to implement and test the algorithms on practical test cases. Results of the case studies and associated analyses indicate that the developed algorithms also perform efficiently in solving the multistage and multiobjective expansion problem.
Pivot method for global optimization: A study of structures and phase changes in water clusters
NASA Astrophysics Data System (ADS)
Nigra, Pablo Fernando
In this thesis, we have carried out a study of water clusters. The research work has been developed in two stages. In the first stage, we have investigated the properties of water clusters at zero temperature by means of global optimization. The clusters were modeled by using two well known pairwise potentials having distinct characteristics. One is the Matsuoka-Clementi-Yoshimine potential (MCY) that is an ab initio fitted function based on a rigid-molecule model, the other is the Sillinger-Rahman potential (SR) which is an empirical function based on a flexible-molecule model. The algorithm used for the global optimization of the clusters was the pivot method, which was developed in our group. The results have shown that, under certain conditions, the pivot method may yield optimized structures which are related to one another in such a way that they seem to form structural families. The structures in a family can be thought of as formed from the aggregation of single units. The particular types of structures we have found are quasi-one dimensional tubes built from stacking cyclic units such as tetramers, pentamers, and hexamers. The binding energies of these tubes form sequences that span smooth curves with clear asymptotic behavior; therefore, we have also studied the sequences applying the Bulirsch-Stoer (BST) algorithm to accelerate convergence. In the second stage of the research work, we have studied the thermodynamic properties of a typical water cluster at finite temperatures. The selected cluster was the water octamer which exhibits a definite solid-liquid phase change. The water octamer also has several low lying energy cubic structures with large energetic barriers that cause ergodicity breaking in regular Monte Carlo simulations. For that reason we have simulated the octamer using paralell tempering Monte Carlo combined with the multihistogram method. This has permited us to calculate the heat capacity from very low temperatures up to T = 230 K. We
Using R for Global Optimization of a Fully-distributed Hydrologic Model at Continental Scale
NASA Astrophysics Data System (ADS)
Zambrano-Bigiarini, M.; Zajac, Z.; Salamon, P.
2013-12-01
Nowadays hydrologic model simulations are widely used to better understand hydrologic processes and to predict extreme events such as floods and droughts. In particular, the spatially distributed LISFLOOD model is currently used for flood forecasting at Pan-European scale, within the European Flood Awareness System (EFAS). Several model parameters can not be directly measured, and they need to be estimated through calibration, in order to constrain simulated discharges to their observed counterparts. In this work we describe how the free software 'R' has been used as a single environment to pre-process hydro-meteorological data, to carry out global optimization, and to post-process calibration results in Europe. Historical daily discharge records were pre-processed for 4062 stream gauges, with different amount and distribution of data in each one of them. The hydroTSM, raster and sp R packages were used to select ca. 700 stations with an adequate spatio-temporal coverage. Selected stations span a wide range of hydro-climatic characteristics, from arid and ET-dominated watersheds in the Iberian Peninsula to snow-dominated watersheds in Scandinavia. Nine parameters were selected to be calibrated based on previous expert knowledge. Customized R scripts were used to extract observed time series for each catchment and to prepare the input files required to fully set up the calibration thereof. The hydroPSO package was then used to carry out a single-objective global optimization on each selected catchment, by using the Standard Particle Swarm 2011 (SPSO-2011) algorithm. Among the many goodness-of-fit measures available in the hydroGOF package, the Nash-Sutcliffe efficiency was used to drive the optimization. User-defined functions were developed for reading model outputs and passing them to the calibration engine. The long computational time required to finish the calibration at continental scale was partially alleviated by using 4 multi-core machines (with both GNU
NASA Astrophysics Data System (ADS)
Portnoy, David; Feuerbach, Robert; Heimberg, Jennifer
2011-10-01
Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the "threat" set of spectra
Climate, Agriculture, Energy and the Optimal Allocation of Global Land Use
NASA Astrophysics Data System (ADS)
Steinbuks, J.; Hertel, T. W.
2011-12-01
The allocation of the world's land resources over the course of the next century has become a pressing research question. Continuing population increases, improving, land-intensive diets amongst the poorest populations in the world, increasing production of biofuels and rapid urbanization in developing countries are all competing for land even as the world looks to land resources to supply more environmental services. The latter include biodiversity and natural lands, as well as forests and grasslands devoted to carbon sequestration. And all of this is taking place in the context of faster than expected climate change which is altering the biophysical environment for land-related activities. The goal of the paper is to determine the optimal profile for global land use in the context of growing commercial demands for food and forest products, increasing non-market demands for ecosystem services, and more stringent GHG mitigation targets. We then seek to assess how the uncertainty associated with the underlying biophysical and economic processes influences this optimal profile of land use, in light of potential irreversibility in these decisions. We develop a dynamic long-run, forward-looking partial equilibrium framework in which the societal objective function being maximized places value on food production, liquid fuels (including biofuels), timber production, forest carbon and biodiversity. Given the importance of land-based emissions to any GHG mitigation strategy, as well as the potential impacts of climate change itself on the productivity of land in agriculture, forestry and ecosystem services, we aim to identify the optimal allocation of the world's land resources, over the course of the next century, in the face of alternative GHG constraints. The forestry sector is characterized by multiple forest vintages which add considerable computational complexity in the context of this dynamic analysis. In order to solve this model efficiently, we have employed the
NASA Astrophysics Data System (ADS)
Zhang, X.; Cai, X.; Zhu, T.
2013-12-01
Biofuels is booming in recent years due to its potential contributions to energy sustainability, environmental improvement and economic opportunities. Production of biofuels not only competes for land and water with food production, but also directly pushes up food prices when crops such as maize and sugarcane are used as biofuels feedstock. Meanwhile, international trade of agricultural commodities exports and imports water and land resources in a virtual form among different regions, balances overall water and land demands and resource endowment, and provides a promising solution to the increasingly severe food-energy competition. This study investigates how to optimize water and land resources uses for overall welfare at global scale in the framework of 'virtual resources'. In contrast to partial equilibrium models that usually simulate trades year-by-year, this optimization model explores the ideal world where malnourishment is minimized with optimal resources uses and trade flows. Comparing the optimal production and trade patterns with historical data can provide meaningful implications regarding how to utilize water and land resources more efficiently and how the trade flows would be changed for overall welfare at global scale. Valuable insights are obtained in terms of the interactions among food, water and bioenergy systems. A global hydro-economic optimization model is developed, integrating agricultural production, market demands (food, feed, fuel and other), and resource and environmental constraints. Preliminary results show that with the 'free market' mechanism and land as well as water resources use optimization, the malnourished population can be reduced by as much as 65%, compared to the 2000 historical value. Expected results include: 1) optimal trade paths to achieve global malnourishment minimization, 2) how water and land resources constrain local supply, 3) how policy affects the trade pattern as well as resource uses. Furthermore, impacts of
2011-01-01
Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520
GOSIM: A multi-scale iterative multiple-point statistics algorithm with global optimization
NASA Astrophysics Data System (ADS)
Yang, Liang; Hou, Weisheng; Cui, Chanjie; Cui, Jie
2016-04-01
Most current multiple-point statistics (MPS) algorithms are based on a sequential simulation procedure, during which grid values are updated according to the local data events. Because the realization is updated only once during the sequential process, errors that occur while updating data events cannot be corrected. Error accumulation during simulations decreases the realization quality. Aimed at improving simulation quality, this study presents an MPS algorithm based on global optimization, called GOSIM. An objective function is defined for representing the dissimilarity between a realization and the TI in GOSIM, which is minimized by a multi-scale EM-like iterative method that contains an E-step and M-step in each iteration. The E-step searches for TI patterns that are most similar to the realization and match the conditioning data. A modified PatchMatch algorithm is used to accelerate the search process in E-step. M-step updates the realization based on the most similar patterns found in E-step and matches the global statistics of TI. During categorical data simulation, k-means clustering is used for transforming the obtained continuous realization into a categorical realization. The qualitative and quantitative comparison results of GOSIM, MS-CCSIM and SNESIM suggest that GOSIM has a better pattern reproduction ability for both unconditional and conditional simulations. A sensitivity analysis illustrates that pattern size significantly impacts the time costs and simulation quality. In conditional simulations, the weights of conditioning data should be as small as possible to maintain a good simulation quality. The study shows that big iteration numbers at coarser scales increase simulation quality and small iteration numbers at finer scales significantly save simulation time.
Optimal fertilizer nitrogen rates and yield-scaled global warming potential in drill seeded rice.
Adviento-Borbe, Maria Arlene; Pittelkow, Cameron M; Anders, Merle; van Kessel, Chris; Hill, James E; McClung, Anna M; Six, Johan; Linquist, Bruce A
2013-11-01
Drill seeded rice ( L.) is the dominant rice cultivation practice in the United States. Although drill seeded systems can lead to significant CH and NO emissions due to anaerobic and aerobic soil conditions, the relationship between high-yielding management practices, particularly fertilizer N management, and total global warming potential (GWP) remains unclear. We conducted three field experiments in California and Arkansas to test the hypothesis that by optimizing grain yield through N management, the lowest yield-scaled global warming potential (GWP = GWP Mg grain) is achieved. Each growing season, urea was applied at rates ranging from 0 to 224 kg N ha before the permanent flood. Emissions of CH and NO were measured daily to weekly during growing seasons and fallow periods. Annual CH emissions ranged from 9.3 to 193 kg CH-C ha yr across sites, and annual NO emissions averaged 1.3 kg NO-N ha yr. Relative to NO emissions, CH dominated growing season (82%) and annual (68%) GWP. The impacts of fertilizer N rates on GHG fluxes were confined to the growing season, with increasing N rate having little effect on CH emissions but contributing to greater NO emissions during nonflooded periods. The fallow period contributed between 7 and 39% of annual GWP across sites years. This finding illustrates the need to include fallow period measurements in annual emissions estimates. Growing season GWP ranged from 130 to 686 kg CO eq Mg season across sites and years. Fertilizer N rate had no significant effect on GWP; therefore, achieving the highest productivity is not at the cost of higher GWP.
Template based protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Lee, Sun Young; Kim, Jong Yun; Cheng, Qianyi; Manavalan, Balachandran; Joung, Jong Young; Heo, Seungryong; Lee, Juyong; Nam, Mikyung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
Efficiency of Pareto joint inversion of 2D geophysical data using global optimization methods
NASA Astrophysics Data System (ADS)
Miernik, Katarzyna; Bogacz, Adrian; Kozubal, Adam; Danek, Tomasz; Wojdyła, Marek
2016-04-01
Pareto joint inversion of two or more sets of data is a promising new tool of modern geophysical exploration. In the first stage of our investigation we created software enabling execution of forward solvers of two geophysical methods (2D magnetotelluric and gravity) as well as inversion with possibility of constraining solution with seismic data. In the algorithm solving MT forward solver Helmholtz's equations, finite element method and Dirichlet's boundary conditions were applied. Gravity forward solver was based on Talwani's algorithm. To limit dimensionality of solution space we decided to describe model as sets of polygons, using Sharp Boundary Interface (SBI) approach. The main inversion engine was created using Particle Swarm Optimization (PSO) algorithm adapted to handle two or more target functions and to prevent acceptance of solutions which are non - realistic or incompatible with Pareto scheme. Each inversion run generates single Pareto solution, which can be added to Pareto Front. The PSO inversion engine was parallelized using OpenMP standard, what enabled execution code for practically unlimited amount of threads at once. Thereby computing time of inversion process was significantly decreased. Furthermore, computing efficiency increases with number of PSO iterations. In this contribution we analyze the efficiency of created software solution taking under consideration details of chosen global optimization engine used as a main joint minimization engine. Additionally we study the scale of possible decrease of computational time caused by different methods of parallelization applied for both forward solvers and inversion algorithm. All tests were done for 2D magnetotelluric and gravity data based on real geological media. Obtained results show that even for relatively simple mid end computational infrastructure proposed solution of inversion problem can be applied in practice and used for real life problems of geophysical inversion and interpretation.
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2015-06-01
In this paper, the global optimization problem miny∈S F (y) with S being a hyperinterval in RN and F (y) satisfying the Lipschitz condition with an unknown Lipschitz constant is considered. It is supposed that the function F (y) can be multiextremal, non-differentiable, and given as a 'black-box'. To attack the problem, a new global optimization algorithm based on the following two ideas is proposed and studied both theoretically and numerically. First, the new algorithm uses numerical approximations to space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition. Second, the algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization. Convergence conditions of the resulting deterministic global optimization method are established. Numerical experiments carried out on several hundreds of test functions show quite a promising performance of the new algorithm in comparison with its direct competitors.
Optimal estimation of regional N2O emissions using a three-dimensional global model
NASA Astrophysics Data System (ADS)
Huang, J.; Golombek, A.; Prinn, R.
2004-12-01
In this study, we use the MATCH (Model of Atmospheric Transport and Chemistry) model and Kalman filtering techniques to optimally estimate N2O emissions from seven source regions around the globe. The MATCH model was used with NCEP assimilated winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from July 1st 1996 to December 31st 2000. The average concentrations of N2O in the lowest four layers of the model were then compared with the monthly mean observations from six national/global networks (AGAGE, CMDL (HATS), CMDL (CCGG), CSIRO, CSIR and NIES), at 48 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal variations. The Kalman filter was then used to solve for the time-averaged regional emissions of N2O for January 1st 1997 to June 30th 2000. The inversions assume that the model stratospheric destruction rates, which lead to a global N2O lifetime of 130 years, are correct. It also assumes normalized emission spatial distributions from each region based on previous studies. We conclude that the global N2O emission flux is about 16.2 TgN/yr, with {34.9±1.7%} from South America and Africa, {34.6±1.5%} from South Asia, {13.9±1.5%} from China/Japan/South East Asia, {8.0±1.9%} from all oceans, {6.4±1.1%} from North America and North and West Asia, {2.6±0.4%} from Europe, and {0.9±0.7%} from New Zealand and Australia. The errors here include the measurement standard deviation, calibration differences among the six groups, grid volume/measurement site mis-match errors estimated from the model, and a procedure to account approximately for the modeling errors.
Global space-group optimization problem: Finding the stablest crystal structure without constraints
NASA Astrophysics Data System (ADS)
Trimarchi, Giancarlo; Zunger, Alex
2007-03-01
Finding the most stable structure of a solid is one of the central problems in condensed matter physics. This entails finding both the lattice type (e.g., fcc, bcc, and orthorhombic) and (for compounds) the decoration of the lattice sites by atoms of types A , B , etc. (“configuration”). Most approaches to this problem either assumed that both lattice type and configuration are known, optimizing instead the cell volume and performing local relaxation. Other approaches assumed that the lattice type is known, searching for the minimum-energy decoration. We present here an approach to the global space-group optimization (GSGO) problem, i.e., the problem of predicting both the lattice structure and the atomic configuration of a crystalline solid. This search method is based on an evolutionary algorithm within which a population of crystal structures is evolved through mating and mutation operations, improving the population by substituting the highest total-energy structures with new ones. The crystal structures are not represented by bit strings as in conventional genetic algorithms. Instead, the evolutionary search is performed directly on the atomic positions and the unit-cell vectors after a similarity transformation is applied to bring structures of different unit-cell shapes to a common basis. Following this transformation, we can define a crossover operation that treats, on the same footing, structures with different unit-cell shapes. Once a new structure has been generated by mating or mutation, it is fully relaxed to the closest local total-energy minimum. We applied our procedure for the GSGO in the context of pseudopotential total-energy calculations to the semiconductor systems Si, SiC, and GaAs and to the metallic alloy AuPd with composition Au8Pd4 . Starting from random unit-cell vectors and random atomic positions, the present search procedure found for all semiconductor systems studied the correct lattice structure and configuration. In the case of
Del Rio, Beatriz G; Dieterich, Johannes M; Carter, Emily A
2017-08-08
The accuracy of local pseudopotentials (LPSs) is one of two major determinants of the fidelity of orbital-free density functional theory (OFDFT) simulations. We present a global optimization strategy for LPSs that enables OFDFT to reproduce solid and liquid properties obtained from Kohn-Sham DFT. Our optimization strategy can fit arbitrary properties from both solid and liquid phases, so the resulting globally optimized local pseudopotentials (goLPSs) can be used in solid and/or liquid-phase simulations depending on the fitting process. We show three test cases proving that we can (1) improve solid properties compared to our previous bulk-derived local pseudopotential generation scheme; (2) refine predicted liquid and solid properties by adding force matching data; and (3) generate a from-scratch, accurate goLPS from the local channel of a non-local pseudopotential. The proposed scheme therefore serves as a full and improved LPS construction protocol.
NASA Astrophysics Data System (ADS)
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
NASA Astrophysics Data System (ADS)
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-01
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-21
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
Lihoreau, Mathieu; Ings, Thomas C; Chittka, Lars; Reynolds, Andy M
2016-07-27
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m(3) enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
Top-down Constraints on Global N2O Emissions at Optimal Spatial and Temporal Resolution
NASA Astrophysics Data System (ADS)
Wells, K. C.; Millet, D. B.; Bousserez, N.; Henze, D. K.; Griffis, T. J.
2016-12-01
Terrestrial emissions of N2O exhibit strong spatial and temporal variability. Quantification of N2O surface fluxes, and the attribution of those fluxes to specific locations and mechanisms, thus requires observational constraints with high space-time resolution. This presents a challenge to top-down analyses because of the sparse coverage of measurement sites and weak variability in atmospheric N2O. In this talk we present a quantitative evaluation of the emission constraints provided by the current N2O observing network using the adjoint of GEOS-Chem and a new, efficient information content analysis technique. This technique allows the construction of a state vector based on the spatial and temporal information afforded by N2O measurements, rather than relying on an arbitrary aggregation scheme. We employ this technique in a global source inversion to derive top-down estimates of N2O emissions at this optimal resolution, and discuss the results in terms of the underlying processes that can be inferred.
Two-stage collaborative global optimization design model of the CHPG microgrid
NASA Astrophysics Data System (ADS)
Liao, Qingfen; Xu, Yeyan; Tang, Fei; Peng, Sicheng; Yang, Zheng
2017-06-01
With the continuous developing of technology and reducing of investment costs, renewable energy proportion in the power grid is becoming higher and higher because of the clean and environmental characteristics, which may need more larger-capacity energy storage devices, increasing the cost. A two-stage collaborative global optimization design model of the combined-heat-power-and-gas (abbreviated as CHPG) microgrid is proposed in this paper, to minimize the cost by using virtual storage without extending the existing storage system. P2G technology is used as virtual multi-energy storage in CHPG, which can coordinate the operation of electric energy network and natural gas network at the same time. Demand response is also one kind of good virtual storage, including economic guide for the DGs and heat pumps in demand side and priority scheduling of controllable loads. Two kinds of storage will coordinate to smooth the high-frequency fluctuations and low-frequency fluctuations of renewable energy respectively, and achieve a lower-cost operation scheme simultaneously. Finally, the feasibility and superiority of proposed design model is proved in a simulation of a CHPG microgrid.
Design of coded aperture arrays by means of a global optimization algorithm
NASA Astrophysics Data System (ADS)
Lang, Haitao; Liu, Liren; Yang, Qingguo
2006-08-01
Coded aperture imaging (CAI) has evolved as a standard technique for imaging high energy photon sources and has found numerous applications. Coded aperture arrays (CAAs) are the most important devices in the applications of CAI. In recent years, many approaches were presented to design optimum or near-optimum CAAs. Uniformly redundant arrays (URAs) are the most successful CAAs for their cyclic autocorrelation consisting of a sequence of delta functions on a flat sidelobe which can easily be subtracted when the object has been reconstructed. Unfortunately, the existing methods can only be used to design URAs with limited number of array sizes and fixed autocorrelative sidelobe-to-peak ratio. In this paper, we presented a method to design more flexible URAs by means of a global optimization algorithm named DIRECT. By our approaches, we obtain various types of URAs including the filled URAs which can be constructed by existing methods and the sparse URAs which never be constructed and mentioned by existing papers as far as we know.
Lahanas, M; Baltas, D; Giannouli, S
2003-03-07
We consider the problem of the global convergence of gradient-based optimization algorithms for interstitial high-dose-rate (HDR) brachytherapy dose optimization using variance-based objectives. Possible local minima could lead to only sub-optimal solutions. We perform a configuration space analysis using a representative set of the entire non-dominated solution space. A set of three prostate implants is used in this study. We compare the results obtained by conjugate gradient algorithms, two variable metric algorithms and fast-simulated annealing. For the variable metric algorithm BFGS from numerical recipes, large fluctuations are observed. The limited memory L-BFGS algorithm and the conjugate gradient algorithm FRPR are globally convergent. Local minima or degenerate states are not observed. We study the possibility of obtaining a representative set of non-dominated solutions using optimal solution rearrangement and a warm start mechanism. For the surface and volume dose variance and their derivatives, a method is proposed which significantly reduces the number of required operations. The optimization time, ignoring a preprocessing step, is independent of the number of sampling points in the planning target volume. Multiobjective dose optimization in HDR brachytherapy using L-BFGS and a new modified computation method for the objectives and derivatives has been accelerated, depending on the number of sampling points, by a factor in the range 10-100.
NASA Astrophysics Data System (ADS)
Lin, Y. S.; Medlyn, B. E.; Duursma, R.; Prentice, I. C.; Wang, H.
2014-12-01
Stomatal conductance (gs) is a key land surface attribute as it links transpiration, the dominant component of global land evapotranspiration and a key element of the global water cycle, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycles, a global scale database and an associated globally applicable model of gs that allow predictions of stomatal behaviour are lacking. We present a unique database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. We employed a model of optimal stomatal conductance to assess differences in stomatal behaviour, and estimated the model slope coefficient, g1, which is directly related to the marginal carbon cost of water, for each dataset. We found that g1 varies considerably among PFTs, with evergreen savanna trees having the largest g1 (least conservative water use), followed by C3 grasses and crops, angiosperm trees, gymnosperm trees, and C4 grasses. Amongst angiosperm trees, species with higher wood density had a higher marginal carbon cost of water, as predicted by the theory underpinning the optimal stomatal model. There was an interactive effect between temperature and moisture availability on g1: for wet environments, g1 was largest in high temperature environments, indicated by high mean annual temperature during the period when temperature above 0oC (Tm), but it did not vary with Tm across dry environments. We examine whether these differences in leaf-scale behaviour are reflected in ecosystem-scale differences in water-use efficiency. These findings provide a robust theoretical framework for understanding and predicting the behaviour of stomatal conductance across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of productivity and ecohydrological processes in a future changing climate.
Visualization of Global Disease Burden for the Optimization of Patient Management and Treatment.
Schlee, Winfried; Hall, Deborah A; Edvall, Niklas K; Langguth, Berthold; Canlon, Barbara; Cederroth, Christopher R
2017-01-01
The assessment and treatment of complex disorders is challenged by the multiple domains and instruments used to evaluate clinical outcome. With the large number of assessment tools typically used in complex disorders comes the challenge of obtaining an integrative view of disease status to further evaluate treatment outcome both at the individual level and at the group level. Radar plots appear as an attractive visual tool to display multivariate data on a two-dimensional graphical illustration. Here, we describe the use of radar plots for the visualization of disease characteristics applied in the context of tinnitus, a complex and heterogeneous condition, the treatment of which has shown mixed success. Data from two different cohorts, the Swedish Tinnitus Outreach Project (STOP) and the Tinnitus Research Initiative (TRI) database, were used. STOP is a population-based cohort where cross-sectional data from 1,223 non-tinnitus and 933 tinnitus subjects were analyzed. By contrast, the TRI contained data from 571 patients who underwent various treatments and whose Clinical Global Impression (CGI) score was accessible to infer treatment outcome. In the latter, 34,560 permutations were tested to evaluate whether a particular ordering of the instruments could reflect better the treatment outcome measured with the CGI. Radar plots confirmed that tinnitus subtypes such as occasional and chronic tinnitus from the STOP cohort could be strikingly different, and helped appreciate a gender bias in tinnitus severity. Radar plots with greater surface areas were consistent with greater burden, and enabled a rapid appreciation of the global distress associated with tinnitus in patients categorized according to tinnitus severity. Permutations in the arrangement of instruments allowed to identify a configuration with minimal variance and maximized surface difference between CGI groups from the TRI database, thus affording a means of optimally evaluating the outcomes in individual
A GPS-Based Pitot-Static Calibration Method Using Global Output-Error Optimization
NASA Technical Reports Server (NTRS)
Foster, John V.; Cunningham, Kevin
2010-01-01
Pressure-based airspeed and altitude measurements for aircraft typically require calibration of the installed system to account for pressure sensing errors such as those due to local flow field effects. In some cases, calibration is used to meet requirements such as those specified in Federal Aviation Regulation Part 25. Several methods are used for in-flight pitot-static calibration including tower fly-by, pacer aircraft, and trailing cone methods. In the 1990 s, the introduction of satellite-based positioning systems to the civilian market enabled new inflight calibration methods based on accurate ground speed measurements provided by Global Positioning Systems (GPS). Use of GPS for airspeed calibration has many advantages such as accuracy, ease of portability (e.g. hand-held) and the flexibility of operating in airspace without the limitations of test range boundaries or ground telemetry support. The current research was motivated by the need for a rapid and statistically accurate method for in-flight calibration of pitot-static systems for remotely piloted, dynamically-scaled research aircraft. Current calibration methods were deemed not practical for this application because of confined test range size and limited flight time available for each sortie. A method was developed that uses high data rate measurements of static and total pressure, and GPSbased ground speed measurements to compute the pressure errors over a range of airspeed. The novel application of this approach is the use of system identification methods that rapidly compute optimal pressure error models with defined confidence intervals in nearreal time. This method has been demonstrated in flight tests and has shown 2- bounds of approximately 0.2 kts with an order of magnitude reduction in test time over other methods. As part of this experiment, a unique database of wind measurements was acquired concurrently with the flight experiments, for the purpose of experimental validation of the
Cooperative Co-evolution with Formula-based Variable Grouping for Large-Scale Global Optimization.
Wang, Yuping; Liu, Haiyan; Wei, Fei; Zong, Tingting; Li, Xiaodong
2017-08-09
For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered as an effective strategy to decompose the problem into smaller subproblems, each of which can be then solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real world problems are white-box problems, i.e., the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can be then used to design an effective variable group method. In this paper, a formulabased grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations " + ", " - ", " × ", " ÷ " and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in non-separable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being inter-dependent. FBG can be applied to any white-box problem easily and can be integrated into a cooperative co-evolution framework. Based on FBG, a novel cooperative co-evolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this paper for decomposing a large-scale white-box problem into several smaller sub-problems and optimizing them
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient.
Developments of global greenhouse gas retrieval algorithm based on Optimal Estimation Method
NASA Astrophysics Data System (ADS)
Kim, W. V.; Kim, J.; Lee, H.; Jung, Y.; Boesch, H.
2013-12-01
After the industrial revolution, atmospheric carbon dioxide concentration increased drastically over the last 250 years. It is still increasing and over than 400ppm of carbon dioxide was measured at Mauna Loa observatory for the first time which value was considered as important milestone. Therefore, understanding the source, emission, transport and sink of global carbon dioxide is unprecedentedly important. Currently, Total Carbon Column Observing Network (TCCON) is operated to observe CO2 concentration by ground base instruments. However, the number of site is very few and concentrated to Europe and North America. Remote sensing of CO2 could supplement those limitations. Greenhouse Gases Observing SATellite (GOSAT) which was launched 2009 is measuring column density of CO2 and other satellites are planned to launch in a few years. GOSAT provide valuable measurement data but its low spatial resolution and poor success rate of retrieval due to aerosol and cloud, forced the results to cover less than half of the whole globe. To improve data availability, accurate aerosol information is necessary, especially for East Asia region where the aerosol concentration is higher than other region. For the first step, we are developing CO2 retrieval algorithm based on optimal estimation method with VLIDORT the vector discrete ordinate radiative transfer model. Proto type algorithm, developed from various combinations of state vectors to find best combination of state vectors, shows appropriate result and good agreement with TCCON measurements. To reduce calculation cost low-stream interpolation is applied for model simulation and the simulation time is drastically reduced. For the further study, GOSAT CO2 retrieval algorithm will be combined with accurate GOSAT-CAI aerosol retrieval algorithm to obtain more accurate result especially for East Asia.
Predicting stable stoichiometries of compounds via evolutionary global space-group optimization
NASA Astrophysics Data System (ADS)
Trimarchi, Giancarlo; Freeman, Arthur J.; Zunger, Alex
2009-09-01
Whereas the Daltonian atom-to-atom ratios in ordinary molecules are well understood via the traditional theory of valence, the naturally occurring stoichiometries in intermetallic compounds ApBq , as revealed by phase-diagram compilations, are often surprising. Even equal-valence elements A and B give rise to unequal (p,q) stoichiometries, e.g., the 1:2, 2:1, and 3:1 ratios in AlpScq . Moreover, sometimes different stoichiometries are associated with different lattice types and hence rather different physical properties. Here, we extend the fixed-composition global space-group optimization (GSGO) approach used to predict, via density-functional calculations, fixed-composition lattice types [G. Trimarchi and A. Zunger, J. Phys.: Condens. Matter 20, 295212 (2008)] to identify simultaneously all the minimum-energy lattice types throughout the composition range. Starting from randomly selected lattice vectors, atomic positions and stoichiometries, we construct the T=0 “convex hull” of energy vs composition. Rather than repeat a set of GSGO searches over a fixed list of stoichiometries, we minimize the distance to the convex hull. This approach is far more efficient than the former one as a single evolutionary search sequence simultaneously identifies the lowest-energy structures at each composition and among these it selects those that are ground states. For Al-Sc we correctly identify the stable stoichiometries and relative structure types: AlSc2-B82 , AlSc-B2, and Al2Sc-C15 in the Nat=6 periodic cells, and Al2Sc6-D019 , AlSc-B2, and Al3Sc-L10 in the Nat=8 periodic cells. This extended evolutionary GSGO algorithm represents a step toward a fully ab initio materials synthesis, where compounds are predicted starting from sole knowledge of the chemical species of the constituents.
Ukwatta, Eranga; Yuan, Jing; Rajchl, Martin; Fenster, Aaron
2012-01-01
Magnetic resonance (MR) imaging of carotid atherosclerosis biomarkers are increasingly being investigated for the risk assessment of vulnerable plaques. A fast and robust 3D segmentation of the carotid adventitia (AB) and lumen-intima (LIB) boundaries can greatly alleviate the measurement burden of generating quantitative imaging biomarkers in clinical research. In this paper, we propose a novel global optimization-based approach to segment the carotid AB and LIB from 3D T1-weighted black blood MR images, by simultaneously evolving two coupled surfaces with enforcement of anatomical consistency of the AB and LIB. We show that the evolution of two surfaces at each discrete time-frame can be optimized exactly and globally by means of convex relaxation. Our continuous max-flow based algorithm is implemented in GPUs to achieve high computational performance. The experiment results from 16 carotid MR images show that the algorithm obtained high agreement with manual segmentations and achieved high repeatability in segmentation.
Wu, Zhijun
1996-11-01
This paper discusses a generalization of the function transformation scheme for global energy minimization applied to the molecular conformation problem. A mathematical theory for the method as a special continuation approach to global optimization is established. We show that the method can transform a nonlinear objective function into a class of gradually deformed, but {open_quote}smoother{close_quote} or {open_quotes}easier{close_quote} functions. An optimization procedure can then be applied to the new functions successively, to trace their solutions back to the original function. Two types of transformation are defined: isotropic and anisotropic. We show that both transformations can be applied to a large class of nonlinear partially separable functions including energy functions for molecular conformation. Methods to compute the transformation for these functions are given.
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2016-06-01
In this paper the global optimization problem where the objective function is multiextremal and satisfying the Lipschitz condition over a hyperinterval is considered. An algorithm that uses Peano-type space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition is proposed. The algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization, as well. Convergence condition are given. Numerical experiments show quite a promising performance of the new technique.
NASA Astrophysics Data System (ADS)
Teo, Colin; Combes, Joshua; Wiseman, Howard M.
2014-10-01
It was first shown by Jacobs, in 2003, that the process of qubit state purification by continuous measurement of one observable can be enhanced, on average, by unitary feedback control. Here, we quantify this by the reduction in any one of the family of Rényi entropies {{S}α }, with 0\\lt α \\lt ∞ , at some terminal time, revealing the rich structure of stochastic quantum control even for this simple problem. We generalize Jacobs’ original argument, which was for the (unique) impurity measure with a linear evolution map under his protocol, by replacing linearity with convexity, thereby making it applicable to Rényi entropies {{S}α } for α in a finite interval about one. Even with this generalization, Jacobs’ argument fails to identify the surprising fact, which we prove by Bellman's principle of dynamic programming, that his protocol is globally optimal for all Rényi entropies whose decrease is locally maximized by that protocol. Also surprisingly, even though there is a range of Rényi entropies whose decrease is always locally maximized by the null-control protocol, that null-control protocol cannot be shown to be globally optimal in any instance. These results highlight the non-intuitive relation between local and global optimality in stochastic quantum control.
NASA Astrophysics Data System (ADS)
Bijani, Rodrigo; Lelièvre, Peter G.; Ponte-Neto, Cosme F.; Farquharson, Colin G.
2017-05-01
This paper is concerned with the applicability of Pareto Multi-Objective Global Optimization (PMOGO) algorithms for solving different types of geophysical inverse problems. The standard deterministic approach is to combine the multiple objective functions (i.e. data misfit, regularization and joint coupling terms) in a weighted-sum aggregate objective function and minimize using local (decent-based) smooth optimization methods. This approach has some disadvantages: (1) appropriate weights must be determined for the aggregate, (2) the objective functions must be differentiable and (3) local minima entrapment may occur. PMOGO algorithms can overcome these drawbacks but introduce increased computational effort. Previous work has demonstrated how PMOGO algorithms can overcome the first issue for single data set geophysical inversion, that is, the trade-off between data misfit and model regularization. However, joint inversion, which can involve many weights in the aggregate, has seen little study. The advantage of PMOGO algorithms for the other two issues has yet to be addressed in the context of geophysical inversion. In this paper, we implement a PMOGO genetic algorithm and apply it to physical-property-, lithology- and surface-geometry-based inverse problems to demonstrate the advantages of using a global optimization strategy. Lithological inversions work on a mesh but use integer model parameters representing rock unit identifiers instead of continuous physical properties. Surface geometry inversions change the geometry of wireframe surfaces that represent the contacts between discrete rock units. Despite the potentially high computational requirements of global optimization algorithms (compared to local), their application to realistically sized 2-D geophysical inverse problems is within reach of current capacity of standard computers. Furthermore, they open the door to geophysical inverse problems that could not otherwise be considered through traditional
NASA Astrophysics Data System (ADS)
Bijani, Rodrigo; Lelièvre, Peter G.; Ponte-Neto, Cosme F.; Farquharson, Colin G.
2017-02-01
This paper is concerned with the applicability of Pareto Multi-Objective Global Optimization (PMOGO) algorithms for solving different types of geophysical inverse problems. The standard deterministic approach is to combine the multiple objective functions (i.e. data misfit, regularization and joint coupling terms) in a weighted-sum aggregate objective function and minimize using local (decent-based) smooth optimization methods. This approach has some disadvantages: 1) appropriate weights must be determined for the aggregate, 2) the objective functions must be differentiable, and 3) local minima entrapment may occur. PMOGO algorithms can overcome these drawbacks but introduce increased computational effort. Previous work has demonstrated how PMOGO algorithms can overcome the first issue for single data set geophysical inversion, i.e. the tradeoff between data misfit and model regularization. However, joint inversion, which can involve many weights in the aggregate, has seen little study. The advantage of PMOGO algorithms for the other two issues has yet to be addressed in the context of geophysical inversion. In this paper, we implement a PMOGO genetic algorithm and apply it to physical property-, lithology- and surface geometry-based inverse problems to demonstrate the advantages of using a global optimization strategy. Lithological inversions work on a mesh but use integer model parameters representing rock unit identifiers instead of continuous physical properties. Surface geometry inversions change the geometry of wireframe surfaces that represent the contacts between discrete rock units. Despite the potentially high computational requirements of global optimization algorithms (compared to local), their application to realistically-sized 2D geophysical inverse problems is within reach of current capacity of standard computers. Furthermore, they open the door to geophysical inverse problems that could not otherwise be considered through traditional optimization
Korenromp, Eline L.; Glaziou, Philippe; Fitzpatrick, Christopher; Floyd, Katherine; Hosseini, Mehran; Raviglione, Mario; Atun, Rifat; Williams, Brian
2012-01-01
Background The Global Plan to Stop TB estimates funding required in low- and middle-income countries to achieve TB control targets set by the Stop TB Partnership within the context of the Millennium Development Goals. We estimate the contribution and impact of Global Fund investments under various scenarios of allocations across interventions and regions. Methodology/Principal Findings Using Global Plan assumptions on expected cases and mortality, we estimate treatment costs and mortality impact for diagnosis and treatment for drug-sensitive and multidrug-resistant TB (MDR-TB), including antiretroviral treatment (ART) during DOTS for HIV-co-infected patients, for four country groups, overall and for the Global Fund investments. In 2015, China and India account for 24% of funding need, Eastern Europe and Central Asia (EECA) for 33%, sub-Saharan Africa (SSA) for 20%, and other low- and middle-income countries for 24%. Scale-up of MDR-TB treatment, especially in EECA, drives an increasing global TB funding need – an essential investment to contain the mortality burden associated with MDR-TB and future disease costs. Funding needs rise fastest in SSA, reflecting increasing coverage need of improved TB/HIV management, which saves most lives per dollar spent in the short term. The Global Fund is expected to finance 8–12% of Global Plan implementation costs annually. Lives saved through Global Fund TB support within the available funding envelope could increase 37% if allocations shifted from current regional demand patterns to a prioritized scale-up of improved TB/HIV treatment and secondly DOTS, both mainly in Africa − with EECA region, which has disproportionately high per-patient costs, funded from alternative resources. Conclusions/Significance These findings, alongside country funding gaps, domestic funding and implementation capacity and equity considerations, should inform strategies and policies for international donors, national governments and disease
NASA Astrophysics Data System (ADS)
Wang, Z. H.; Deng, X. L.; Cao, K.; Wang, J.; Wu, L.
2017-09-01
A tungsten based reactor to grow 2-in. PVT AlN crystals by induction heating was designed. In order to investigate the effect of the hotzone structure layout on the temperature distribution in the growth chamber, a series of global quasi-steady numerical simulations with and without gas convection was performed using the FEMAG software. Simulation results show that the temperature gradient between the AlN powder sources and the deposition interface is influenced profoundly by the size of the induction heater and the crucible thickness. Also the tungsten heat shields have obvious effects on the global temperature distribution and heater power consumption during the growth process. However, the number of tungsten shield layers plays a trivial role on the temperature gradient between the ALN powder sources and the crucible top. Global heat transfer simulations show that the designed hotzone can provide an optimized and flexible environment for 2-in. AlN PVT growth.
Lithological and Surface Geometry Joint Inversions Using Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
surfaces are set to a priori values. The inversion is tasked with calculating the geometry of the contact surfaces instead of some piecewise distribution of properties in a mesh. Again, no coupling measure is required and joint inversion is simplified. Both of these inverse problems involve high nonlinearity and discontinuous or non-obtainable derivatives. They can also involve the existence of multiple minima. Hence, one can not apply the standard descent-based local minimization methods used to solve typical minimum-structure inversions. Instead, we are applying Pareto multi-objective global optimization (PMOGO) methods, which generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. While there are definite advantages to PMOGO joint inversion approaches, the methods come with significantly increased computational requirements. We are researching various strategies to ameliorate these computational issues including parallelization and problem dimension reduction.
NASA Astrophysics Data System (ADS)
Southall, Hugh L.; O'Donnell, Teresa H.; Derov, John S.
2010-04-01
EGO is an evolutionary, data-adaptive algorithm which can be useful for optimization problems with expensive cost functions. Many antenna design problems qualify since complex computational electromagnetics (CEM) simulations can take significant resources. This makes evolutionary algorithms such as genetic algorithms (GA) or particle swarm optimization (PSO) problematic since iterations of large populations are required. In this paper we discuss multiparameter optimization of a wideband, single-element antenna over a metamaterial ground plane and the interfacing of EGO (optimization) with a full-wave CEM simulation (cost function evaluation).
Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal
ERIC Educational Resources Information Center
Steinley, Douglas; Hubert, Lawrence
2008-01-01
This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…
Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal
ERIC Educational Resources Information Center
Steinley, Douglas; Hubert, Lawrence
2008-01-01
This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…
Pechak, Celia M; Thompson, Mary
2009-11-01
There is growing involvement by US clinicians, faculty members, and students in global health initiatives, including international service-learning (ISL). Limited research has been done to examine the profession's increasing global engagement, or the ISL phenomenon in particular, and no research has been done to determine best practices. This study was intended as an early step in the examination of the physical therapy profession's role and activities in the global health arena within and beyond academics. The purposes of this study were: (1) to identify and analyze the common structures and processes among established ISL programs within physical therapist education programs and (2) to develop a conceptual model of optimal ISL within physical therapist education programs. A descriptive, exploratory study was completed using grounded theory. Telephone interviews were completed with 14 faculty members who had been involved in international service, international learning, or ISL in physical therapist education programs. Interviews were transcribed, and transcriptions were analyzed using the grounded theory method. Four major themes emerged from the data: structure, reciprocity, relationship, and sustainability. A conceptual model of and a proposed definition for optimal ISL in physical therapist education were developed. Seven essential components of the conceptual model are: a partner that understands the role of physical therapy, community-identified needs, explicit service and learning objectives, reflection, preparation, risk management, and service and learning outcome measures. Essential consequences are positive effects on students and community. The conceptual model and definition of optimal ISL can be used to direct development of new ISL programs and to improve existing programs. In addition, they can offer substantive guidance to any physical therapist involved in global health initiatives.
NASA Astrophysics Data System (ADS)
Theos, F. V.; Lagaris, I. E.; Papageorgiou, D. G.
2004-05-01
We present two sequential and one parallel global optimization codes, that belong to the stochastic class, and an interface routine that enables the use of the Merlin/MCL environment as a non-interactive local optimizer. This interface proved extremely important, since it provides flexibility, effectiveness and robustness to the local search task that is in turn employed by the global procedures. We demonstrate the use of the parallel code to a molecular conformation problem. Program summaryTitle of program: PANMIN Catalogue identifier: ADSU Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSU Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: PANMIN is designed for UNIX machines. The parallel code runs on either shared memory architectures or on a distributed system. The code has been tested on a SUN Microsystems ENTERPRISE 450 with four CPUs, and on a 48-node cluster under Linux, with both the GNU g77 and the Portland group compilers. The parallel implementation is based on MPI and has been tested with LAM MPI and MPICH Installation: University of Ioannina, Greece Programming language used: Fortran-77 Memory required to execute with typical data: Approximately O( n2) words, where n is the number of variables No. of bits in a word: 64 No. of processors used: 1 or many Has the code been vectorised or parallelized?: Parallelized using MPI No. of bytes in distributed program, including test data, etc.: 147163 No. of lines in distributed program, including the test data, etc.: 14366 Distribution format: gzipped tar file Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be
Jarrar, Mu’taman; Rahman, Hamzah Abdul; Don, Mohammad Sobri
2016-01-01
Background and Objective: Demand for health care service has significantly increased, while the quality of healthcare and patient safety has become national and international priorities. This paper aims to identify the gaps and the current initiatives for optimizing the quality of care and patient safety in Malaysia. Design: Review of the current literature. Highly cited articles were used as the basis to retrieve and review the current initiatives for optimizing the quality of care and patient safety. The country health plan of Ministry of Health (MOH) Malaysia and the MOH Malaysia Annual Reports were reviewed. Results: The MOH has set four strategies for optimizing quality and sustaining quality of life. The 10th Malaysia Health Plan promotes the theme “1 Care for 1 Malaysia” in order to sustain the quality of care. Despite of these efforts, the total number of complaints received by the medico-legal section of the MOH Malaysia is increasing. The current global initiatives indicted that quality performance generally belong to three main categories: patient; staffing; and working environment related factors. Conclusions: There is no single intervention for optimizing quality of care to maintain patient safety. Multidimensional efforts and interventions are recommended in order to optimize the quality of care and patient safety in Malaysia. PMID:26755459
NASA Astrophysics Data System (ADS)
Alberti, Luca; Oberto, Gabriele; Pianosi, Francesca; Castelletti, Andrea
2013-04-01
Infiltration galleries and scavenger wells are often constructed to prevent saltwater intrusion in coastal aquifers. The optimal design of these infrastructures can be framed as a multi-objective optimization problem balancing availability of fresh water supply and installation/operation costs. High fidelity simulation models of the flow and transport processes can be used to link design parameters (e.g. wells location, size and pumping rates) to objective functions. However, the incorporation of these simulation models within an optimization-based planning framework is not straightforward because of the computational requirements of the model itself and the computational limitations of the optimization algorithms. In this study we investigate the potential for the Global Interactive Response Surface (GIRS) methodology to overcome these technical limitations. The GIRS methodology is used to recursively build a non-dynamic emulator of the process-based simulation model that maps design options into objectives values and can be used in place of the original model to more quickly explore the design space. The approach is used to plan infrastructural interventions for controlling saltwater intrusion and ensuring sustainable groundwater supply for Nauru, a Pacific island republic in Micronesia. GIRS is used to emulate a SEAWAT density driven groundwater flow-and-transport simulation model. Results show the potential applicability of the proposed approach for optimal planning of coastal aquifers.
Jarrar, Mu'taman; Abdul Rahman, Hamzah; Don, Mohammad Sobri
2015-10-20
Demand for health care service has significantly increased, while the quality of healthcare and patient safety has become national and international priorities. This paper aims to identify the gaps and the current initiatives for optimizing the quality of care and patient safety in Malaysia. Review of the current literature. Highly cited articles were used as the basis to retrieve and review the current initiatives for optimizing the quality of care and patient safety. The country health plan of Ministry of Health (MOH) Malaysia and the MOH Malaysia Annual Reports were reviewed. The MOH has set four strategies for optimizing quality and sustaining quality of life. The 10th Malaysia Health Plan promotes the theme "1 Care for 1 Malaysia" in order to sustain the quality of care. Despite of these efforts, the total number of complaints received by the medico-legal section of the MOH Malaysia is increasing. The current global initiatives indicted that quality performance generally belong to three main categories: patient; staffing; and working environment related factors. There is no single intervention for optimizing quality of care to maintain patient safety. Multidimensional efforts and interventions are recommended in order to optimize the quality of care and patient safety in Malaysia.
NASA Astrophysics Data System (ADS)
Tsoukalas, Ioannis; Kossieris, Panagiotis; Efstratiadis, Andreas; Makropoulos, Christos
2015-04-01
In water resources optimization problems, the calculation of the objective function usually presumes to first run a simulation model and then evaluate its outputs. In several cases, however, long simulation times may pose significant barriers to the optimization procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required by the problem's complexity. A promising novel strategy to address these shortcomings is the use of surrogate modelling techniques within global optimization algorithms. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modelling with the effectiveness and efficiency of the EAS method. The algorithm combines three different optimization approaches (evolutionary search, simulated annealing and the downhill simplex search scheme), in which key decisions are partially guided by numerical approximations of the objective function. The performance of the proposed algorithm is benchmarked against other surrogate-assisted algorithms, in both theoretical and practical applications (i.e. test functions and hydrological calibration problems, respectively), within a limited budget of trials (from 100 to 1000). Results reveal the significant potential of using SE-EAS in challenging optimization problems, involving time-consuming simulations.
A New Method for Global Optimization Based on Stochastic Differential Equations.
1984-12-01
energia potenziale in- tramolecolare mediante un nuovo metodo di minimizzazione globale (Search for minimum-intramolecular-potential pat- terns by means of...Optimizacion Global de Funciones, Universidad Nacional Autonoma de M~xico, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Report...con la sigla SPS (Suigar-Phosphate-Sugar). Per questo frammento Matsuoka, Tosi e Clementi calcoldrono, con un metodo quantomeccanico ab-ini tio(.4.i
Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2011-09-01
The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.
Randomized Search Methods for Solving Markov Decision Processes and Global Optimization
2006-01-01
arbitrary (bounded) function and updates at each iteration the current function into a new function that better approximates the optimal value...equation (2.4) can not be too “far away” from the optimal value function J∗, in the sense that max x∈X |Jπk(x)− J∗(x)| < 2ε α 1− α. The above error ...required for PI to find the optimal value function J∗ was 15 seconds, and the value of ‖J∗‖∞ is approximately 2.32e+03. Test results clearly indicate
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
On unified modeling, theory, and method for solving multi-scale global optimization problems
NASA Astrophysics Data System (ADS)
Gao, David Yang
2016-10-01
A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.
NASA Astrophysics Data System (ADS)
de Pascale, P.; Vasile, M.; Casotto, S.
The design of interplanetary trajectories requires the solution of an optimization problem, which has been traditionally solved by resorting to various local optimization techniques. All such approaches, apart from the specific method employed (direct or indirect), require an initial guess, which deeply influences the convergence to the optimal solution. The recent developments in low-thrust propulsion have widened the perspectives of exploration of the Solar System, while they have at the same time increased the difficulty related to the trajectory design process. Continuous thrust transfers, typically characterized by multiple spiraling arcs, have a broad number of design parameters and thanks to the flexibility offered by such engines, they typically turn out to be characterized by a multi-modal domain, with a consequent larger number of optimal solutions. Thus the definition of the first guesses is even more challenging, particularly for a broad search over the design parameters, and it requires an extensive investigation of the domain in order to locate the largest number of optimal candidate solutions and possibly the global optimal one. In this paper a tool for the preliminary definition of interplanetary transfers with coast-thrust arcs and multiple swing-bys is presented. Such goal is achieved combining a novel methodology for the description of low-thrust arcs, with a global optimization algorithm based on a hybridization of an evolutionary step and a deterministic step. Low thrust arcs are described in a 3D model in order to account the beneficial effects of low-thrust propulsion for a change of inclination, resorting to a new methodology based on an inverse method. The two-point boundary values problem (TPBVP) associated with a thrust arc is solved by imposing a proper parameterized evolution of the orbital parameters, by which, the acceleration required to follow the given trajectory with respect to the constraints set is obtained simply through
NASA Astrophysics Data System (ADS)
Alemany, Kristina
Electric propulsion has recently become a viable technology for spacecraft, enabling shorter flight times, fewer required planetary gravity assists, larger payloads, and/or smaller launch vehicles. With the maturation of this technology, however, comes a new set of challenges in the area of trajectory design. Because low-thrust trajectory optimization has historically required long run-times and significant user-manipulation, mission design has relied on expert-based knowledge for selecting departure and arrival dates, times of flight, and/or target bodies and gravitational swing-bys. These choices are generally based on known configurations that have worked well in previous analyses or simply on trial and error. At the conceptual design level, however, the ability to explore the full extent of the design space is imperative to locating the best solutions in terms of mass and/or flight times. Beginning in 2005, the Global Trajectory Optimization Competition posed a series of difficult mission design problems, all requiring low-thrust propulsion and visiting one or more asteroids. These problems all had large ranges on the continuous variables---launch date, time of flight, and asteroid stay times (when applicable)---as well as being characterized by millions or even billions of possible asteroid sequences. Even with recent advances in low-thrust trajectory optimization, full enumeration of these problems was not possible within the stringent time limits of the competition. This investigation develops a systematic methodology for determining a broad suite of good solutions to the combinatorial, low-thrust, asteroid tour problem. The target application is for conceptual design, where broad exploration of the design space is critical, with the goal being to rapidly identify a reasonable number of promising solutions for future analysis. The proposed methodology has two steps. The first step applies a three-level heuristic sequence developed from the physics of the
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
NASA Astrophysics Data System (ADS)
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
NASA Astrophysics Data System (ADS)
Ait moussa, Abdellah; Jassemnejad, Bahaeddin
2014-05-01
Nanocomposites with high-aspect ratio fillers attract enormous attention because of the superior physical properties of the composite over the parent matrix. Nanocomposites with functionalized graphene as fillers did not produce the high thermal conductivity expected due to the high interfacial thermal resistance between the functional groups and graphene flakes. We report here a robust and efficient technique that identifies the configuration of the functionalities for improved thermal conductivity. The method combines linearization of the interatomic interactions, calculation, and optimization of the thermal conductivity using the globalized and bounded Nelder-Mead algorithm.
Multi-objective global optimization of a butterfly valve using genetic algorithms.
Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio
2016-07-01
A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Optimizing Army Special Forces Leaders in a Global Counter-Insurgent Network
2007-03-01
network , topographically, is a system in which everyone is connected to everyone else with the authority to move information or goods in any direction.23...ARMY SPECIAL FORCES LEADERS IN A GLOBAL COUNTER-INSURGENT NETWORK by Joshua H. Walker Eric J. Deal March 2007 Thesis Advisor: John...Arquilla Second Readers: Peter Gustaitis Wayne Hughes THIS PAGE INTENTIONALLY LEFT BLANK i REPORT
Assured Optimism in a Scottish Girls' School: Habitus and the (Re)production of Global Privilege
ERIC Educational Resources Information Center
Forbes, Joan; Lingard, Bob
2015-01-01
This paper examines how high levels of social-cultural connectedness and academic excellence, inflected by gender and social class, constitute a particular school habitus of "assured optimism" at an elite Scottish girls' school. In Bourdieuian terms, Dalrymple is a "forcing ground" for the "intense cultivation" of a…
NASA Astrophysics Data System (ADS)
Chen, Xi; Diez, Matteo; Kandasamy, Manivannan; Zhang, Zhiguo; Campana, Emilio F.; Stern, Frederick
2015-04-01
Advances in high-fidelity shape optimization for industrial problems are presented, based on geometric variability assessment and design-space dimensionality reduction by Karhunen-Loève expansion, metamodels and deterministic particle swarm optimization (PSO). Hull-form optimization is performed for resistance reduction of the high-speed Delft catamaran, advancing in calm water at a given speed, and free to sink and trim. Two feasible sets (A and B) are assessed, using different geometric constraints. Dimensionality reduction for 95% confidence is applied to high-dimensional free-form deformation. Metamodels are trained by design of experiments with URANS; multiple deterministic PSOs achieve a resistance reduction of 9.63% for A and 6.89% for B. Deterministic PSO is found to be effective and efficient, as shown by comparison with stochastic PSO. The optimum for A has the best overall performance over a wide range of speed. Compared with earlier optimization, the present studies provide an additional resistance reduction of 6.6% at 1/10 of the computational cost.
Assured Optimism in a Scottish Girls' School: Habitus and the (Re)production of Global Privilege
ERIC Educational Resources Information Center
Forbes, Joan; Lingard, Bob
2015-01-01
This paper examines how high levels of social-cultural connectedness and academic excellence, inflected by gender and social class, constitute a particular school habitus of "assured optimism" at an elite Scottish girls' school. In Bourdieuian terms, Dalrymple is a "forcing ground" for the "intense cultivation" of a…
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints
NASA Technical Reports Server (NTRS)
Hinckley, David; Englander, Jacob; Hitt, Darren
2015-01-01
Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.
Daily Time Step Refinement of Optimized Flood Control Rule Curves for a Global Warming Scenario
NASA Astrophysics Data System (ADS)
Lee, S.; Fitzgerald, C.; Hamlet, A. F.; Burges, S. J.
2009-12-01
Pacific Northwest temperatures have warmed by 0.8 °C since 1920 and are predicted to further increase in the 21st century. Simulated streamflow timing shifts associated with climate change have been found in past research to degrade water resources system performance in the Columbia River Basin when using existing system operating policies. To adapt to these hydrologic changes, optimized flood control operating rule curves were developed in a previous study using a hybrid optimization-simulation approach which rebalanced flood control and reservoir refill at a monthly time step. For the climate change scenario, use of the optimized flood control curves restored reservoir refill capability without increasing flood risk. Here we extend the earlier studies using a detailed daily time step simulation model applied over a somewhat smaller portion of the domain (encompassing Libby, Duncan, and Corra Linn dams, and Kootenai Lake) to evaluate and refine the optimized flood control curves derived from monthly time step analysis. Moving from a monthly to daily analysis, we found that the timing of flood control evacuation needed adjustment to avoid unintended outcomes affecting Kootenai Lake. We refined the flood rule curves derived from monthly analysis by creating a more gradual evacuation schedule, but kept the timing and magnitude of maximum evacuation the same as in the monthly analysis. After these refinements, the performance at monthly time scales reported in our previous study proved robust at daily time scales. Due to a decrease in July storage deficits, additional benefits such as more revenue from hydropower generation and more July and August outflow for fish augmentation were observed when the optimized flood control curves were used for the climate change scenario.
Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael
2009-09-01
There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.
Vinding, Mads S.; Guérin, Bastien; Vosegaard, Thomas; Nielsen, Niels Chr.
2016-01-01
Purpose To present a constrained optimal-control (OC) framework for designing large-flip-angle parallel-transmit (pTx) pulses satisfying hardware peak-power as well as regulatory local and global specific-absorption-rate (SAR) limits. The application is 2D and 3D spatial-selective 90° and 180° pulses. Theory and Methods The OC gradient-ascent-pulse-engineering method with exact gradients and the limited-memory Broyden-Fletcher-Goldfarb-Shanno method is proposed. Local SAR is constrained by the virtual-observation-points method. Two numerical models facilitated the optimizations, a torso at 3 T and a head at 7 T, both in eight-channel pTx coils and acceleration-factors up to 4. Results The proposed approach yielded excellent flip-angle distributions. Enforcing the local-SAR constraint, as opposed to peak power alone, reduced the local SAR 7 and 5-fold with the 2D torso excitation and inversion pulse, respectively. The root-mean-square errors of the magnetization profiles increased less than 5% with the acceleration factor of 4. Conclusion A local and global SAR, and peak-power constrained OC large-flip-angle pTx pulse design was presented, and numerically validated for 2D and 3D spatial-selective 90° and 180° pulses at 3 T and 7 T. PMID:26715084
NASA Astrophysics Data System (ADS)
Kumar, S.; Singh, A.; Dhar, A.
2017-08-01
The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.
Vinding, Mads S; Guérin, Bastien; Vosegaard, Thomas; Nielsen, Niels Chr
2017-01-01
To present a constrained optimal-control (OC) framework for designing large-flip-angle parallel-transmit (pTx) pulses satisfying hardware peak-power as well as regulatory local and global specific-absorption-rate (SAR) limits. The application is 2D and 3D spatial-selective 90° and 180° pulses. The OC gradient-ascent-pulse-engineering method with exact gradients and the limited-memory Broyden-Fletcher-Goldfarb-Shanno method is proposed. Local SAR is constrained by the virtual-observation-points method. Two numerical models facilitated the optimizations, a torso at 3 T and a head at 7 T, both in eight-channel pTx coils and acceleration-factors up to 4. The proposed approach yielded excellent flip-angle distributions. Enforcing the local-SAR constraint, as opposed to peak power alone, reduced the local SAR 7 and 5-fold with the 2D torso excitation and inversion pulse, respectively. The root-mean-square errors of the magnetization profiles increased less than 5% with the acceleration factor of 4. A local and global SAR, and peak-power constrained OC large-flip-angle pTx pulse design was presented, and numerically validated for 2D and 3D spatial-selective 90° and 180° pulses at 3 T and 7 T. Magn Reson Med 77:374-384, 2017. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
2015-09-24
ABSTRACT Supported by this grant, the PI and his group have successfully solved a series of challenging problems in computer science, global...Professor of Industrial Engineering, North Carolina State University. 3. Dr. Layne Watson, Professor of Computer Science, Virginia Tech. 4...a series of challenging problems have been solved, such as the well- known NP-hard Max-Cut problem in computer science, sensor location problem in
Engagement Models for Optimized Utilization of Global Technologies for Trusted Applications
2016-03-31
reduced availability of US suppliers thereby limiting potential Trusted sources. Concurrent with this shift, consumer market pressures are now driving...Government (USG) “low-volume requirements have little influence on the commercial market ” [1] due to minimal purchasing power within the context of...the global semiconductor market . As a result, the vast majority of semiconductor suppliers support commercial handling only. This results in fewer
A global earthquake discrimination scheme to optimize ground-motion prediction equation selection
Garcia, Daniel; Wald, David J.; Hearne, Michael
2012-01-01
We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.
Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio
2014-05-10
Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
2014-01-01
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957
NASA Technical Reports Server (NTRS)
Englander, Arnold C.; Englander, Jacob A.
2017-01-01
Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.
NASA Astrophysics Data System (ADS)
Sperna Weiland, F. C.; Tisseuil, C.; Dürr, H. H.; Vrac, M.; van Beek, L. P. H.
2011-07-01
Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET time series from CFSR reanalysis data are compared: Penman-Monteith, Priestley-Taylor and original and modified versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. The lowest root mean squared differences and the least significant deviations (95 % significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for the cell specific modified Blaney-Criddle equation. However, results show that this modified form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation did not outperform the other methods. In arid regions (e.g., Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g., Orange, Murray and Zambezi). Furthermore, the Penman-Monteith equation has a high data demand and the equation is sensitive to input data inaccuracy. Therefore, we preferred the modified form of the Hargreaves equation, which globally gave reference PET values comparable to CRU derived values. Although it is a relative efficient empirical equation, like Blaney-Criddle, the equation considers multiple spatial varying meteorological variables and consequently performs well for different climate conditions. In the modified form of the Hargreaves equation the multiplication factor is uniformly increased from 0.0023 to 0.0031 to overcome the global underestimation
AMIGO, a toolbox for advanced model identification in systems biology using global optimization
Balsa-Canto, Eva; Banga, Julio R.
2011-01-01
Motivation: Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation. These parameters must be estimated by fitting the model to experimental data. This estimation problem is very challenging due to the non-linear character of the dynamics, the large number of parameters and the frequently poor information content of the experimental data (poor practical identifiability). The design of optimal (more informative) experiments is an associated problem of the highest interest. Results: This work presents AMIGO, a toolbox which facilitates parametric identification by means of advanced numerical techniques which cover the full iterative identification procedure putting especial emphasis on robust methods for parameter estimation and practical identifiability analyses, plus flexible capabilities for optimal experimental design. Availability: The toolbox and the corresponding documentation may be downloaded from: http://www.iim.csic.es/~amigo Contact: ebalsa@iim.csic.es PMID:21685047
The Optimize Heart Failure Care Program: Initial lessons from global implementation.
Cowie, Martin R; Lopatin, Yuri M; Saldarriaga, Clara; Fonseca, Cândida; Sim, David; Magaña, Jose Antonio; Albuquerque, Denilson; Trivi, Marcelo; Moncada, Gustavo; González Castillo, Baldomero A; Sánchez, Mario Osvaldo Speranza; Chung, Edward
2017-02-12
Hospitalization for heart failure (HF) places a major burden on healthcare services worldwide, and is a strong predictor of increased mortality especially in the first three months after discharge. Though undesirable, hospitalization is an opportunity to optimize HF therapy and advise clinicians and patients about the importance of continued adherence to HF medication and regular monitoring. The Optimize Heart Failure Care Program (www.optimize-hf.com), which has been implemented in 45 countries, is designed to improve outcomes following HF hospitalization through inexpensive initiatives to improve prescription of appropriate drug therapies, patient education and engagement, and post-discharge planning. It includes best practice clinical protocols for local adaptation, pre- and post-discharge checklists, and 'My HF Passport', a printed and smart phone application to improve patient understanding of HF and encourage involvement in care and treatment adherence. Early experience of the Program suggests that factors leading to successful implementation include support from HF specialists or 'local leaders', regular educational meetings for participating healthcare professionals, multidisciplinary collaboration, and full integration of pre- and post-hospital discharge checklists across care services. The Program is helping to raise awareness of HF and generate useful data on current practice. It is showing how good evidence-based care can be achieved through the use of simple clinician and patient-focused tools. Preliminary results suggest that optimization of HF pharmacological therapy is achievable through the Program, with little new investment. Further data collection will lead to a greater understanding of the impact of the Program on HF care and key indicators of success.
Global Binary Optimization on Graphs for Classification of High Dimensional Data
2014-09-01
convex because the binary side constraints (16) are non- convex . We show that the binary constraints can be replaced by their convex hull [0, 1] to...high dimen- sional data into two classes. It combines recent convex optimization methods from imaging with recent graph based variational models for data...seg- mentation. Two convex splitting algorithms are proposed, where graph-based PDE techniques are used to solve some of the subproblems. It is shown
Use of a generalized fisher equation for global optimization in chemical kinetics.
Villaverde, Alejandro F; Ross, John; Morán, Federico; Balsa-Canto, Eva; Banga, Julio R
2011-08-04
A new approach for parameter estimation in chemical kinetics has been recently proposed (Ross et al. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777). It makes use of an optimization criterion based on a Generalized Fisher Equation (GFE). Its utility has been demonstrated with two reaction mechanisms, the chlorite-iodide and Oregonator, which are computationally stiff systems. In this Article, the performance of the GFE-based algorithm is compared to that obtained from minimization of the squared distances between the observed and predicted concentrations obtained by solving the corresponding initial value problem (we call this latter approach "traditional" for simplicity). Comparison of the proposed GFE-based optimization method with the "traditional" one has revealed their differences in performance. This difference can be seen as a trade-off between speed (which favors GFE) and accuracy (which favors the traditional method). The chlorite-iodide and Oregonator systems are again chosen as case studies. An identifiability analysis is performed for both of them, followed by an optimal experimental design based on the Fisher Information Matrix (FIM). This allows to identify and overcome most of the previously encountered identifiability issues, improving the estimation accuracy. With the new data, obtained from optimally designed experiments, it is now possible to estimate effectively more parameters than with the previous data. This result, which holds for both GFE-based and traditional methods, stresses the importance of an appropriate experimental design. Finally, a new hybrid method that combines advantages from the GFE and traditional approaches is presented.
NASA Astrophysics Data System (ADS)
Zoric, Nenad; Livshits, Irina; Dilworth, Don; Okishev, Sergey
2017-02-01
This paper describes a method for designing an ultraviolet (UV) projection lens for microlithography. Our approach for meeting this objective is to use a starting design automatically obtained by the DSEARCH feature in the SYNOPSYS™ lens design program. We describe the steps for getting a desired starting point for the projection lens and discuss optimization problems unique to this system, where the two parts of the projection lens are designed independently.
NASA Astrophysics Data System (ADS)
Chevrot, Sébastien; Martin, Roland; Komatitsch, Dimitri
2012-12-01
Wavelets are extremely powerful to compress the information contained in finite-frequency sensitivity kernels and tomographic models. This interesting property opens the perspective of reducing the size of global tomographic inverse problems by one to two orders of magnitude. However, introducing wavelets into global tomographic problems raises the problem of computing fast wavelet transforms in spherical geometry. Using a Cartesian cubed sphere mapping, which grids the surface of the sphere with six blocks or 'chunks', we define a new algorithm to implement fast wavelet transforms with the lifting scheme. This algorithm is simple and flexible, and can handle any family of discrete orthogonal or bi-orthogonal wavelets. Since wavelet coefficients are local in space and scale, aliasing effects resulting from a parametrization with global functions such as spherical harmonics are avoided. The sparsity of tomographic models expanded in wavelet bases implies that it is possible to exploit the power of compressed sensing to retrieve Earth's internal structures optimally. This approach involves minimizing a combination of a ℓ2 norm for data residuals and a ℓ1 norm for model wavelet coefficients, which can be achieved through relatively minor modifications of the algorithms that are currently used to solve the tomographic inverse problem.
Mann, Stefan A; Imtiaz, Mohammad; Winbo, Annika; Rydberg, Annika; Perry, Matthew D; Couderc, Jean-Philippe; Polonsky, Bronislava; McNitt, Scott; Zareba, Wojciech; Hill, Adam P; Vandenberg, Jamie I
2016-11-01
In-silico models of human cardiac electrophysiology are now being considered for prediction of cardiotoxicity as part of the preclinical assessment phase of all new drugs. We ask the question whether any of the available models are actually fit for this purpose. We tested three models of the human ventricular action potential, the O'hara-Rudy (ORD11), the Grandi-Bers (GB10) and the Ten Tusscher (TT06) models. We extracted clinical QT data for LQTS1 and LQTS2 patients with nonsense mutations that would be predicted to cause 50% loss of function in IKs and IKr respectively. We also obtained clinical QT data for LQTS3 patients. We then used a global optimization approach to improve the existing in silico models so that they reproduced all three clinical data sets more closely. We also examined the effects of adrenergic stimulation in the different LQTS subsets. All models, in their original form, produce markedly different and unrealistic predictions of QT prolongation for LQTS1, 2 and 3. After global optimization of the maximum conductances for membrane channels, all models have similar current densities during the action potential, despite differences in kinetic properties of the channels in the different models, and more closely reproduce the prolongation of repolarization seen in all LQTS subtypes. In-silico models of cardiac electrophysiology have the potential to be tremendously useful in complementing traditional preclinical drug testing studies. However, our results demonstrate they should be carefully validated and optimized to clinical data before they can be used for this purpose.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Laguzet, Laetitia; Turinici, Gabriel
2015-05-01
This work focuses on optimal vaccination policies for an Susceptible-Infected-Recovered (SIR) model; the impact of the disease is minimized with respect to the vaccination strategy. The problem is formulated as an optimal control problem and we show that the value function is the unique viscosity solution of an Hamilton-Jacobi-Bellman (HJB) equation. This allows to find the best vaccination policy. At odds with existing literature, it is seen that the value function is not always smooth (sometimes only Lipschitz) and the optimal vaccination policies are not unique. Moreover we rigorously analyze the situation when vaccination can be modeled as instantaneous (with respect to the time evolution of the epidemic) and identify the global optimum solutions. Numerical applications illustrate the theoretical results. In addition the pertussis vaccination in adults is considered from two perspectives: first the maximization of DALY averted in presence of vaccine side-effects; then the impact of the herd immunity on the cost-effectiveness analysis is discussed on a concrete example.
Optimizing Global Coronal Magnetic Field Models Using Image-Based Constraints
NASA Technical Reports Server (NTRS)
Jones-Mecholsky, Shaela I.; Davila, Joseph M.; Uritskiy, Vadim
2016-01-01
The coronal magnetic field directly or indirectly affects a majority of the phenomena studied in the heliosphere. It provides energy for coronal heating, controls the release of coronal mass ejections, and drives heliospheric and magnetospheric activity, yet the coronal magnetic field itself has proven difficult to measure. This difficulty has prompted a decades-long effort to develop accurate, timely, models of the field, an effort that continues today. We have developed a method for improving global coronal magnetic field models by incorporating the type of morphological constraints that could be derived from coronal images. Here we report promising initial tests of this approach on two theoretical problems, and discuss opportunities for application.
Optimizing Global Coronal Magnetic Field Models Using Image-Based Constraints
NASA Technical Reports Server (NTRS)
Jones-Mecholsky, Shaela I.; Davila, Joseph M.; Uritskiy, Vadim
2016-01-01
The coronal magnetic field directly or indirectly affects a majority of the phenomena studied in the heliosphere. It provides energy for coronal heating, controls the release of coronal mass ejections, and drives heliospheric and magnetospheric activity, yet the coronal magnetic field itself has proven difficult to measure. This difficulty has prompted a decades-long effort to develop accurate, timely, models of the field, an effort that continues today. We have developed a method for improving global coronal magnetic field models by incorporating the type of morphological constraints that could be derived from coronal images. Here we report promising initial tests of this approach on two theoretical problems, and discuss opportunities for application.
Global near real-time precipitation estimates by optimally blending gauge, satellite, and model data
NASA Astrophysics Data System (ADS)
Beck, H.; De Roo, A. P. J.; Pappenberger, F.; van Dijk, A.; Levizzani, V.; Wood, E. F.; Huffman, G. J.
2016-12-01
Accurate and timely precipitation data are essential for many scientific and operational applications. Yet, a fully global near real-time (NRT) product simultaneously providing good performance in densely-gauged, tropical convection-, and snow-dominated regions is still lacking. We developed a NRT variant of the retrospective Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. MSWEP-NRT provides gap-free, fully global precipitation estimates with three-hourly temporal and 0.25° spatial resolution, by merging seven NRT data sources; one based solely on gauge observations (CPC Unified), four on satellite remote sensing (CMORPH, GSMaP, IMERG, and TMPA 3B42RT), and two on weather forecast models (GDAS and JRA-55). The product has a latency of approximately four hours. To account for latency differences among data sources and for potential disruptions in input data availability, MSWEP-NRT data less than seven days old are progressively upgraded to include any new data as they become available. To ensure the reliability necessary for operational use, the product is produced at two independent locations and distributed using two independent data providers. MSWEP-NRT can be used to more or less seamlessly extend the retrospective MSWEP dataset until the present, since both use the same bias correction factors and similar merging techniques. The product is accessible via THREDDS and FTP at www.gloh2o.org.
Optimization of semi-global stereo matching for hardware module implementation
NASA Astrophysics Data System (ADS)
Roszkowski, Mikołaj
2014-11-01
Stereo vision is one of the most intensively studied areas in the field of computer vision. It allows the creation of a 3D model of a scene given two images of the scene taken with optical cameras. Although the number of stereo algorithms keeps increasing, not many are suitable candidates for hardware implementations that could guarantee real-time processing in embedded systems. One of such algorithms is semi-global matching, which seems to balance well the quality of the disparity map and computational complexity. However, it still has quite high memory requirements, which can be a problem if the low-cost FPGAs are to be used. This is because they often suffer from a low external DRAM memory throughput. In this article, a few methods to reduce both the semi-global matching algorithm complexity and memory usage, and thus required bandwidth, are proposed. First of all, it is shown that a simple pyramid matching scheme can be used to efficiently reduce the number of disparities checked per pixel. Secondly, a method of dividing the image into independent blocks is proposed, which allows the reduction of the amount of memories required by the algorithm. Finally the exact requirements for the bandwidth and the size of the on-chip memories are given.
Optimal integer resolution for attitude determination using global positioning system signals
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Markley, F. Landis; Lightsey, E. Glenn
1998-01-01
In this paper, a new motion-based algorithm for GPS integer ambiguity resolution is derived. The first step of this algorithm converts the reference sightline vectors into body frame vectors. This is accomplished by an optimal vectorized transformation of the phase difference measurements. The result of this transformation leads to the conversion of the integer ambiguities to vectorized biases. This essentially converts the problem to the familiar magnetometer-bias determination problem, for which an optimal and efficient solution exists. Also, the formulation in this paper is re-derived to provide a sequential estimate, so that a suitable stopping condition can be found during the vehicle motion. The advantages of the new algorithm include: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can sequentially estimate the ambiguities during the vehicle motion. The only disadvantage of the new algorithm is that it requires at least three non-coplanar baselines. The performance of the new algorithm is tested on a dynamic hardware simulator.
Li, Xingyuan; He, Zhili; Zhou, Jizhong
2005-10-30
The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based on gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.
Towards continuous global measurements and optimal emission estimates of NF3
NASA Astrophysics Data System (ADS)
Arnold, T.; Muhle, J.; Salameh, P.; Harth, C.; Ivy, D. J.; Weiss, R. F.
2011-12-01
We present an analytical method for the continuous in situ measurement of nitrogen trifluoride (NF3) - an anthropogenic gas with a global warming potential of ~16800 over a 100 year time horizon. NF3 is not included in national reporting emissions inventories under the United Nations Framework Convention on Climate Change (UNFCCC). However, it is a rapidly emerging greenhouse gas due to emission from a growing number of manufacturing facilities with increasing output and modern end-use applications, namely in microcircuit etching, and in production of flat panel displays and thin-film photovoltaic cells. Despite success in measuring the most volatile long lived halogenated species such as CF4, the Medusa preconcentration GC/MS system of Miller et al. (2008) is unable to detect NF3 under remote operation. Using altered techniques of gas separation and chromatography after initial preconcentration, we are now able to make continuous atmospheric measurements of NF3 with average precisions < 1.5% (1 s.d.) for modern background air samples. Most notably, the suite of gases previously measured by Medusa (the significant halogenated species listed under both the Montreal and Kyoto Protocols), can also be quantified from the same sample. Our technique was used to extend the most recent atmospheric measurements into 2011 and complete the background Southern Hemispheric trend over the past three decades using samples from the Cape Grim Air Archive. Using these latest results and those from Weiss et al. (2008) we present optimised annual emission estimates using a 2D atmospheric transport model (AGAGE 12-box model) and an inverse method (Rigby et al., 2011). We calculate emissions during 2010 of 7.6 +/- 1.3 kt (equivalent to 13 million metric tons of CO2), which is estimated to be around 6% of the total NF3 produced. Emission factors are shown to have reduced over the last decade; however, rising production and end-use have caused the average global atmospheric concentration
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie
2016-11-01
By defining two open-time impulse points, the optimization of a two-impulse, open-time terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit is proposed in this paper. The purpose of optimization is to minimize the velocity increment for a terminal elliptic-reference-orbit rendezvous and docking. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The non-linear Tschauner- Hempel(TH) equations of the state transitions for a terminal elliptic target orbit are transformed form time domain to target orbital true anomaly domain. Their homogenous solutions and approximate state transition matrix for the control with a short true anomaly interval can be used to avoid interval integration. The interval branch and bound optimization algorithm is introduced for solving the presented rendezvous and docking optimization problem and optimizing two open-time impulse points and thruster pulse amplitudes, which systematically eliminates parts of the control and open-time input spaces that do not satisfy the path and final time state constraints. Several numerical examples are undertaken to validate the interval optimization algorithm. The results indicate that the sufficiently narrow spaces containing the global optimization solution for the open-time two-impulse terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit can be obtained by the interval algorithm (IA). Combining the gradient-based method, the global optimization solution for the discontinuous nonconvex optimization problem in the specifically remained search space can be found. Interval analysis is shown to be a useful tool and preponderant in the discontinuous nonconvex optimization problem of the terminal rendezvous and
OPTIMIZING GLOBAL CORONAL MAGNETIC FIELD MODELS USING IMAGE-BASED CONSTRAINTS
Jones, Shaela I.; Davila, Joseph M.; Uritsky, Vadim
2016-04-01
The coronal magnetic field directly or indirectly affects a majority of the phenomena studied in the heliosphere. It provides energy for coronal heating, controls the release of coronal mass ejections, and drives heliospheric and magnetospheric activity, yet the coronal magnetic field itself has proven difficult to measure. This difficulty has prompted a decades-long effort to develop accurate, timely, models of the field—an effort that continues today. We have developed a method for improving global coronal magnetic field models by incorporating the type of morphological constraints that could be derived from coronal images. Here we report promising initial tests of this approach on two theoretical problems, and discuss opportunities for application.
On global optimization using an estimate of Lipschitz constant and simplicial partition
NASA Astrophysics Data System (ADS)
Gimbutas, Albertas; Žilinskas, Antanas
2016-10-01
A new algorithm is proposed for finding the global minimum of a multi-variate black-box Lipschitz function with an unknown Lipschitz constant. The feasible region is initially partitioned into simplices; in the subsequent iteration, the most suitable simplices are selected and bisected via the middle point of the longest edge. The suitability of a simplex for bisection is evaluated by minimizing of a surrogate function which mimics the lower bound for the considered objective function over that simplex. The surrogate function is defined using an estimate of the Lipschitz constant and the objective function values at the vertices of a simplex. The novelty of the algorithm is the sophisticated method of estimating the Lipschitz constant, and the appropriate method to minimize the surrogate function. The proposed algorithm was tested using 600 random test problems of different complexity, showing competitive results with two popular advanced algorithms which are based on similar assumptions.
NASA Astrophysics Data System (ADS)
Rahman, Tuan A. Z.; Jalil, N. A. Abdul; As'arry, A.; Raja Ahmad, R. K.
2017-06-01
Support vector machine (SVM) has been known as one-state-of-the-art pattern recognition method. However, the SVM performance is particularly influenced byits parameter selection. This paper presents the parameter optimization of an SVM classifier using chaos-enhanced stochastic fractal search (SFS) algorithm to classify conditions of a ball bearing. The vibration data for normal and damaged conditions of the ball bearing system obtained from the Case Western Reserve University Bearing Data Centre. Features based on time and frequency domains were generated to characterize the ball bearing conditions. The performance of chaos-enhanced SFS algorithms in comparison to their predecessor algorithm is evaluated. In conclusion, the injection of chaotic maps into SFS algorithm improved its convergence speed and searching accuracy based on the statistical results of CEC 2015 benchmark test suites and their application to ball bearing fault diagnosis.
Towards a globally optimized crop distribution: Integrating water use, nutrition, and economic value
NASA Astrophysics Data System (ADS)
Davis, K. F.; Seveso, A.; Rulli, M. C.; D'Odorico, P.
2016-12-01
Human demand for crop production is expected to increase substantially in the coming decades as a result of population growth, richer diets and biofuel use. In order for food production to keep pace, unprecedented amounts of resources - water, fertilizers, energy - will be required. This has led to calls for `sustainable intensification' in which yields are increased on existing croplands while seeking to minimize impacts on water and other agricultural resources. Recent studies have quantified aspects of this, showing that there is a large potential to improve crop yields and increase harvest frequencies to better meet human demand. Though promising, both solutions would necessitate large additional inputs of water and fertilizer in order to be achieved under current technologies. However, the question of whether the current distribution of crops is, in fact, the best for realizing sustainable production has not been considered to date. To this end, we ask: Is it possible to increase crop production and economic value while minimizing water demand by simply growing crops where soil and climate conditions are best suited? Here we use maps of yields and evapotranspiration for 14 major food crops to identify differences between current crop distributions and where they can most suitably be planted. By redistributing crops across currently cultivated lands, we determine the potential improvements in calorie (+12%) and protein (+51%) production, economic output (+41%) and water demand (-5%). This approach can also incorporate the impact of future climate on cropland suitability, and as such, be used to provide optimized cropping patterns under climate change. Thus, our study provides a novel tool towards achieving sustainable intensification that can be used to recommend optimal crop distributions in the face of a changing climate while simultaneously accounting for food security, freshwater resources, and livelihoods.
Contact-assisted protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Cheng, Qianyi; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.
Nan, Feng; Moghadasi, Mohammad; Vakili, Pirooz; Vajda, Sandor; Kozakov, Dima; Ch. Paschalidis, Ioannis
2015-01-01
We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods. PMID:25914440
NASA Astrophysics Data System (ADS)
Lang, Haitao; Liu, Liren; Yang, Qingguo
2007-10-01
When noises considerations are made, nonredundant arrays (NRAs) are endowed with many advantages which other arrays e.g., uniformly redundant arrays (URAs) do not possess in applications of coded aperture imaging. However, lower aperture opening ratio limits the applications of NRA in practice. In this paper, we present a computer searching method based on a global optimization algorithm named DIRECT to design NRAs. Compared with the existing NRAs e.g., Golay's NRAs, which are well known and widely used in various applications, NRAs found by our method have higher aperture opening ratio and auto correlation compression ratio. These advantages make our aperture arrays be very useful for practical applications especially for which of aperture size are limited. Here, we also present some aperture arrays we found. These aperture arrays have an interesting property that they belong to both NRA and URA.
Global Time Tomography of Finite Frequency Waves with Optimized Tetrahedral Grids.
NASA Astrophysics Data System (ADS)
Montelli, R.; Montelli, R.; Nolet, G.; Dahlen, F. A.; Masters, G.; Hung, S.
2001-12-01
Besides true velocity heterogeneities, tomographic images reflect the effect of data errors, model parametrization, linearization, uncertainties involved with the solution of the forward problem and the greatly inadequate sampling of the earth by seismic rays. These influences cannot be easily separated and often produce artefacts in the final image with amplitudes comparable to those of the velocity heterogeneities. In practice, the tomographer uses some form of damping of the ill-resolved aspects of the model to get a unique solution and reduce the influence of the errors. However damping is not fully adequate, and may reveal a strong influence of the ray path coverage in tomographic images. If some cells are ill determinated regularization techniques may lead to heterogeneity because these cells are damped towards zero. Thus we want a uniform resolution of the parameters in our model. This can be obtained by using an irregular grid with variable length scales. We have introduced an irregular parametrization of the velocity structure by using a Delaunay triangulation. Extensively work on error analysis of tomographic images together with mesh optimization has shown that both resolution and ray density can provide the critical informations needed to re-design grids. However, criteria based on resolution are preferred in the presence of narrow ray beams coming from the same direction. This can be understood if we realise that resolution is not only determined by the number of rays crossing a region, but also by their azimutal coverage. We shall discuss various strategies for grid optimization. In general the computation of the travel times is restricted to ray theory, the infinite frequency approximation of the elastodynamic equation of motion. This simplifies the mathematic and is therefore widely applied in seismic tomography. But ray theory does not account for scattering, wavefront healing and other diffraction effects that render the traveltime of a finite
NASA Astrophysics Data System (ADS)
Shoemaker, C. A.; Pang, M.; Akhtar, T.; Bindel, D.
2016-12-01
New parallel surrogate global optimization algorithms are developed and applied to objective functions that are expensive simulations (possibly with multiple local minima). The algorithms can be applied to most geophysical simulations, including those with nonlinear partial differential equations. The optimization does not require simulations be parallelized. Asynchronous (and synchronous) parallel execution is available in the optimization toolbox "pySOT". The parallel algorithms are modified from serial to eliminate fine grained parallelism. The optimization is computed with open source software pySOT, a Surrogate Global Optimization Toolbox that allows user to pick the type of surrogate (or ensembles), the search procedure on surrogate, and the type of parallelism (synchronous or asynchronous). pySOT also allows the user to develop new algorithms by modifying parts of the code. In the applications here, the objective function takes up to 30 minutes for one simulation, and serial optimization can take over 200 hours. Results from Yellowstone (NSF) and NCSS (Singapore) supercomputers are given for groundwater contaminant hydrology simulations with applications to model parameter estimation and decontamination management. All results are compared with alternatives. The first results are for optimization of pumping at many wells to reduce cost for decontamination of groundwater at a superfund site. The optimization runs with up to 128 processors. Superlinear speed up is obtained for up to 16 processors, and efficiency with 64 processors is over 80%. Each evaluation of the objective function requires the solution of nonlinear partial differential equations to describe the impact of spatially distributed pumping and model parameters on model predictions for the spatial and temporal distribution of groundwater contaminants. The second application uses an asynchronous parallel global optimization for groundwater quality model calibration. The time for a single objective
Guiding automated NMR structure determination using a global optimization metric, the NMR DP score
Huang, Yuanpeng Janet; Mao, Binchen; Xu, Fei; Montelione, Gaetano
2016-01-01
ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases 15N-1H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include 1) using a global metric of structural accuracy, the Discriminating Power (DP) score, for guiding model selection during the iterative NOE interpretation process, and 2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta. PMID:26081575
Semantic Segmentation of Forest Stands of Pure Species as a Global Optimization Problem
NASA Astrophysics Data System (ADS)
Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V.
2017-05-01
Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.
NASA Astrophysics Data System (ADS)
Selva, D.
2014-10-01
Requirements from the different disciplines of the Earth sciences on satellite missions have become considerably more stringent in the past decade, while budgets in space organizations have not increased to support the implementation of new systems meeting these requirements. At the same time, new technologies such as optical communications, electrical propulsion, nanosatellite technology, and new commercial agents and models such as hosted payloads are now available. The technical and programmatic environment is thus ideal to conduct architectural studies that look with renewed breadth and adequate depth to the myriad of new possible architectures for Earth Observing Systems. Such studies are challenging tasks, since they require formidable amounts of data and expert knowledge in order to be conducted. Indeed, trade-offs between hundreds or thousands of requirements from different disciplines need to be considered, and millions of combinations of instrument technologies and orbits are possible. This paper presents a framework and tool to support the exploration of such large architectural tradespaces. The framework can be seen as a model-based, executable science traceability matrix that can be used to compare the relative value of millions of different possible architectures. It is demonstrated with an operational climate-centric case study. Ultimately, this framework can be used to assess opportunities for international collaboration and look at architectures for a global Earth observing system, including space, air, and ground assets.
NASA Astrophysics Data System (ADS)
Wang, Hong; Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Yang, Yongguo
2017-09-01
This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950-2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.
Tavakoli, Behnoosh; Zhu, Quing
2013-01-01
Ultrasound-guided diffuse optical tomography (DOT) is a promising method for characterizing malignant and benign lesions in the female breast. We introduce a new two-step algorithm for DOT inversion in which the optical parameters are estimated with the global optimization method, genetic algorithm. The estimation result is applied as an initial guess to the conjugate gradient (CG) optimization method to obtain the absorption and scattering distributions simultaneously. Simulations and phantom experiments have shown that the maximum absorption and reduced scattering coefficients are reconstructed with less than 10% and 25% errors, respectively. This is in contrast with the CG method alone, which generates about 20% error for the absorption coefficient and does not accurately recover the scattering distribution. A new measure of scattering contrast has been introduced to characterize benign and malignant breast lesions. The results of 16 clinical cases reconstructed with the two-step method demonstrates that, on average, the absorption coefficient and scattering contrast of malignant lesions are about 1.8 and 3.32 times higher than the benign cases, respectively.
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662
NASA Astrophysics Data System (ADS)
Cai, X.; Zhang, X.; Zhu, T.
2014-12-01
Global food security is constrained by local and regional land and water availability, as well as other agricultural input limitations and inappropriate national and global regulations. In a theoretical context, this study assumes that optimal water and land uses in local food production to maximize food security and social welfare at the global level can be driven by global trade. It follows the context of "virtual resources trade", i.e., utilizing international trade of agricultural commodities to reduce dependency on local resources, and achieves land and water savings in the world. An optimization model based on the partial equilibrium of agriculture is developed for the analysis, including local commodity production and land and water resources constraints, demand by country, and global food market. Through the model, the marginal values (MVs) of social welfare for water and land at the level of so-called food production units (i.e., sub-basins with similar agricultural production conditions) are derived and mapped in the world. In this personation, we will introduce the model structure, explain the meaning of MVs at the local level and their distribution around the world, and discuss the policy implications for global communities to enhance global food security. In particular, we will examine the economic values of water and land under different world targets of food security (e.g., number of malnourished population or children in a future year). In addition, we will also discuss the opportunities on data to improve such global modeling exercises.
NASA Astrophysics Data System (ADS)
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi
2016-04-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi
2017-01-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as −0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. PMID:27008349
Moment-tensor solutions estimated using optimal filter theory: Global seismicity, 2001
Sipkin, S.A.; Bufe, C.G.; Zirbes, M.D.
2003-01-01
This paper is the 12th in a series published yearly containing moment-tensor solutions computed at the US Geological Survey using an algorithm based on the theory of optimal filter design (Sipkin, 1982 and Sipkin, 1986b). An inversion has been attempted for all earthquakes with a magnitude, mb or MS, of 5.5 or greater. Previous listings include solutions for earthquakes that occurred from 1981 to 2000 (Sipkin, 1986b; Sipkin and Needham, 1989, Sipkin and Needham, 1991, Sipkin and Needham, 1992, Sipkin and Needham, 1993, Sipkin and Needham, 1994a and Sipkin and Needham, 1994b; Sipkin and Zirbes, 1996 and Sipkin and Zirbes, 1997; Sipkin et al., 1998, Sipkin et al., 1999, Sipkin et al., 2000a, Sipkin et al., 2000b and Sipkin et al., 2002).The entire USGS moment-tensor catalog can be obtained via anonymous FTP at ftp://ghtftp.cr.usgs.gov. After logging on, change directory to “momten”. This directory contains two compressed ASCII files that contain the finalized solutions, “mt.lis.Z” and “fmech.lis.Z”. “mt.lis.Z” contains the elements of the moment tensors along with detailed event information; “fmech.lis.Z” contains the decompositions into the principal axes and best double-couples. The fast moment-tensor solutions for more recent events that have not yet been finalized and added to the catalog, are gathered by month in the files “jan01.lis.Z”, etc. “fmech.doc.Z” describes the various fields.
Pleban, Dariusz
2014-01-01
This paper describes the results of a study aimed at developing a tool for optimizing the location of machinery and workstations. A global index of acoustic assessment of machines was developed for this purpose. This index and a genetic algorithm were used in a computer tool for predicting noise emission of machines as well as optimizing the location of machines and workstations in industrial rooms. The results of laboratory and simulation tests demonstrate that the developed global index and the genetic algorithm support measures aimed at noise reduction at workstations.
NASA Astrophysics Data System (ADS)
Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel
2017-04-01
Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent
NASA Astrophysics Data System (ADS)
Simpson, J. J.; Taflove, A.
2005-12-01
We report a finite-difference time-domain (FDTD) computational solution of Maxwell's equations [1] that models the possibility of detecting and characterizing ionospheric disturbances above seismic regions. Specifically, we study anomalies in Schumann resonance spectra in the extremely low frequency (ELF) range below 30 Hz as observed in Japan caused by a hypothetical cylindrical ionospheric disturbance above Taiwan. We consider excitation of the global Earth-ionosphere waveguide by lightning in three major thunderstorm regions of the world: Southeast Asia, South America (Amazon region), and Africa. Furthermore, we investigate varying geometries and characteristics of the ionospheric disturbance above Taiwan. The FDTD technique used in this study enables a direct, full-vector, three-dimensional (3-D) time-domain Maxwell's equations calculation of round-the-world ELF propagation accounting for arbitrary horizontal as well as vertical geometrical and electrical inhomogeneities and anisotropies of the excitation, ionosphere, lithosphere, and oceans. Our entire-Earth model grids the annular lithosphere-atmosphere volume within 100 km of sea level, and contains over 6,500,000 grid-points (63 km laterally between adjacent grid points, 5 km radial resolution). We use our recently developed spherical geodesic gridding technique having a spatial discretization best described as resembling the surface of a soccer ball [2]. The grid is comprised entirely of hexagonal cells except for a small fixed number of pentagonal cells needed for completion. Grid-cell areas and locations are optimized to yield a smoothly varying area difference between adjacent cells, thereby maximizing numerical convergence. We compare our calculated results with measured data prior to the Chi-Chi earthquake in Taiwan as reported by Hayakawa et. al. [3]. Acknowledgement This work was suggested by Dr. Masashi Hayakawa, University of Electro-Communications, Chofugaoka, Chofu Tokyo. References [1] A
Ong, M L; Ng, E Y K
2005-12-01
In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.
NASA Astrophysics Data System (ADS)
Carroll, Rosemary W. H.; Pohll, Greg M.; Earman, Sam; Hershey, Ronald L.
2007-10-01
SummaryAs part of a larger study to estimate groundwater recharge volumes in the area of the eastern Nevada Test Site (NTS), [Campana, M.E., 1975. Finite-state models of transport phenomena in hydrologic systems, PhD Dissertation: University of Arizona, Tucson] Discrete-state compartment model (DSCM) was re-coded to simulate steady-state groundwater concentrations of a conservative tracer. It was then dynamically linked with the shuffled complex evolution (SCE) optimization algorithm [Duan, Q., Soroosh, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28(4), 1015-1031] in which both flow direction and magnitude were adjusted to minimize errors in predicted tracer concentrations. Code validation on a simple four-celled model showed the algorithm consistent in model predictions and capable of reproducing expected cell outflows with relatively little error. The DSCM-SCE code was then applied to a 15-basin (cell) eastern NTS model developed for the DSCM. Auto-calibration of the NTS model was run given two modeling scenarios, (a) assuming known groundwater flow directions and solving only for magnitudes and, (b) solving for groundwater flow directions and magnitudes. The SCE is a fairly robust algorithm, unlike simulated annealing or modified Gauss-Newton approaches. The DSCM-SCE improves upon its original counterpart by being more user-friendly and by auto-calibrating complex models in minutes to hours. While the DSCM-SCE can provide numerical support to a working hypothesis, it can not definitively define a flow system based solely on δD values given few hydrogeologic constraints on boundary conditions and cell-to-cell interactions.
NASA Astrophysics Data System (ADS)
Wei, Zeng Xin; Li, Guo Yin; Qi, Li Qun
2008-12-01
We propose two algorithms for nonconvex unconstrained optimization problems that employ Polak-Ribiere-Polyak conjugate gradient formula and new inexact line search techniques. We show that the new algorithms converge globally if the function to be minimized has Lipschitz continuous gradients. Preliminary numerical results show that the proposed methods for particularly chosen line search conditions are very promising.
NASA Astrophysics Data System (ADS)
Zhang, N.; Chen, F. Y.; Wu, X. Q.
2015-07-01
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is -3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells.
NASA Astrophysics Data System (ADS)
Bos, Brent J.; Howard, Joseph M.; Young, Philip J.; Gracey, Renee; Seals, Lenward T.; Ohl, Raymond G.
2012-09-01
During cryogenic vacuum testing of the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM), the global alignment of the ISIM with respect to the designed interface of the JWST optical telescope element (OTE) will be measured through a series of optical characterization tests. These tests will determine the locations and orientations of the JWST science instrument projected focal surfaces and entrance pupils with respect to their corresponding OTE optical interfaces. Thermal, finite element and optical modeling will then be used to predict the on-orbit optical performance of the observatory. If any optical performance non-compliances are identified, the ISIM will be adjusted to improve its performance. If this becomes necessary, ISIM has a variety of adjustments that can be made. The lengths of the six kinematic mount struts that attach the ISIM to the OTE can be modified and five science instrument focus positions and two pupil positions can be individually adjusted as well. In order to understand how to manipulate the ISIM’s degrees of freedom properly and to prepare for the ISIM flight model testing, we have completed a series of optical-mechanical analyses to develop and identify the best approaches for bringing a non-compliant ISIM Element back into compliance. During this work several unknown misalignment scenarios were produced and the simulated optical performance metrics were input into various mathematical modeling and optimization tools to determine how the ISIM degrees of freedom should be adjusted to provide the best overall optical performance.
NASA Astrophysics Data System (ADS)
Tatsumi, Keiji
2017-06-01
Recently, the gradient method with perturbation (GP) was proposed for metaheuristic methods of solving continuous global optimization problems. Its updating system based on the steepest descent method is chaotic because of its perturbations along the standard basis vectors, which can strengthen the diversification of search. The sufficient condition for its chaoticity was theoretically shown, which implies that two kinds of influence degrees of the perturbations in the updating system should be greater than some constants. In this paper, we extend the updating system of the GP into a more general one for metaheuristic methods, which does not necessarily require the descent direction of the objective function, and which can have perturbations along appropriate orthogonal basis vectors for each problem. Furthermore, since the condition for the chaoticity shown in the previous work is too restricted for practical use, we theoretically show a weaker sufficient condition for the extended system, which is derived by varying the constant lower bounds for the two kinds of influence degrees.
Zhang, N; Chen, F Y; Wu, X Q
2015-07-07
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is -3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells.
Keresztes, Janos C; John Koshel, R; D'huys, Karlien; De Ketelaere, Bart; Audenaert, Jan; Goos, Peter; Saeys, Wouter
2016-12-26
A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.
Zhang, N.; Chen, F. Y.; Wu, X.Q.
2015-01-01
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is −3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells. PMID:26148904
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan.
NASA Astrophysics Data System (ADS)
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-04-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm3 g- 1). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils.
NASA Astrophysics Data System (ADS)
Biswas, A.
2016-12-01
A Very Fast Simulated Annealing (VFSA) global optimization code is produced for elucidation of magnetic data over various idealized bodies for mineral investigation. The way of uncertainty in the interpretation is additionally analyzed in the present study. This strategy fits the watched information exceptionally well by some straightforward geometrically body in the confined class of Sphere, horizontal cylinder, thin dyke and sheet type models. The consequences of VFSA improvement uncover that different parameters demonstrate various identical arrangements when state of the objective body is not known and shape factor "q" is additionally advanced together with other model parameters. The study uncovers that amplitude coefficient k is firmly subject to shape factor. This demonstrates there is multi-model sort vulnerability between these two model parameters. Be that as it may, the assessed estimations of shape factor from different VFSA runs without a doubt show whether the subsurface structure is sphere, horizontal cylinder, and dyke or sheet type structure. Thus, the precise shape element (2.5 for sphere, 2.0 for horizontal cylinder and 1.0 for dyke and sheet) is settled and improvement procedure is rehashed. Next, altering the shape factor and investigation of uncertainty as well as scatter-plots demonstrates a very much characterized uni-model characteristics. The mean model figured in the wake of settling the shape factor gives the highest dependable results. Inversion of noise-free and noisy synthetic data information and additionally field information shows the adequacy of the methodology. The procedure has been carefully and practically connected to five genuine field cases with the nearness of mineralized bodies covered at various profundities in the subsurface and complex geological settings. The method can be to a great degree appropriate for mineral investigation, where the attractive information is seen because of mineral body established in the
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-01-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm(3) g(-1)). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils.
NASA Astrophysics Data System (ADS)
Rossi, Giulia; Ferrando, Riccardo; Rapallo, Arnaldo; Fortunelli, Alessandro; Curley, Benjamin C.; Lloyd, Lesley D.; Johnston, Roy L.
2005-05-01
Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.
Rossi, Giulia; Ferrando, Riccardo; Rapallo, Arnaldo; Fortunelli, Alessandro; Curley, Benjamin C; Lloyd, Lesley D; Johnston, Roy L
2005-05-15
Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.
NASA Astrophysics Data System (ADS)
Knapp, Julia L. A.; Cirpka, Olaf A.
2017-06-01
The complexity of hyporheic flow paths requires reach-scale models of solute transport in streams that are flexible in their representation of the hyporheic passage. We use a model that couples advective-dispersive in-stream transport to hyporheic exchange with a shape-free distribution of hyporheic travel times. The model also accounts for two-site sorption and transformation of reactive solutes. The coefficients of the model are determined by fitting concurrent stream-tracer tests of conservative (fluorescein) and reactive (resazurin/resorufin) compounds. The flexibility of the shape-free models give rise to multiple local minima of the objective function in parameter estimation, thus requiring global-search algorithms, which is hindered by the large number of parameter values to be estimated. We present a local-in-global optimization approach, in which we use a Markov-Chain Monte Carlo method as global-search method to estimate a set of in-stream and hyporheic parameters. Nested therein, we infer the shape-free distribution of hyporheic travel times by a local Gauss-Newton method. The overall approach is independent of the initial guess and provides the joint posterior distribution of all parameters. We apply the described local-in-global optimization method to recorded tracer breakthrough curves of three consecutive stream sections, and infer section-wise hydraulic parameter distributions to analyze how hyporheic exchange processes differ between the stream sections.
Godfrey, Valerie; Howie, Jacqueline; Dennis, Helen; Crowther, Daniel; Struthers, Allan; Goddard, Catharine; Feuerstein, Giora; Lang, Chim; Miele, Gino
2011-01-01
Background Technical advances in the collection of clinical material, such as laser capture microdissection and cell sorting, provide the advantage of yielding more refined and homogenous populations of cells. However, these attractive advantages are counter balanced by the significant difficultly in obtaining adequate nucleic acid yields to allow transcriptomic analyses. Established technologies are available to carry out global transcriptomics using nanograms of input RNA, however, many clinical samples of low cell content would be expected to yield RNA within the picogram range. To fully exploit these clinical samples the challenge of isolating adequate RNA yield directly and generating sufficient microarray probes for global transcriptional profiling from this low level RNA input has been addressed in the current report. We have established an optimised RNA isolation workflow specifically designed to yield maximal RNA from minimal cell numbers. This procedure obtained RNA yield sufficient for carrying out global transcriptional profiling from vascular endothelial cell biopsies, clinical material not previously amenable to global transcriptomic approaches. In addition, by assessing the performance of two linear isothermal probe generation methods at decreasing input levels of good quality RNA we demonstrated robust detection of a class of low abundance transcripts (GPCRs) at input levels within the picogram range, a lower level of RNA input (50 pg) than previously reported for global transcriptional profiling and report the ability to interrogate the transcriptome from only 10 pg of input RNA. By exploiting an optimal RNA isolation workflow specifically for samples of low cell content, and linear isothermal RNA amplification methods for low level RNA input we were able to perform global transcriptomics on valuable and potentially informative clinically derived vascular endothelial biopsies here for the first time. These workflows provide the ability to robustly
Kang, Heesuk; Hollister, Scott J; La Marca, Frank; Park, Paul; Lin, Chia-Ying
2013-10-01
Biodegradable cages have received increasing attention for their use in spinal procedures involving interbody fusion to resolve complications associated with the use of nondegradable cages, such as stress shielding and long-term foreign body reaction. However, the relatively weak initial material strength compared to permanent materials and subsequent reduction due to degradation may be problematic. To design a porous biodegradable interbody fusion cage for a preclinical large animal study that can withstand physiological loads while possessing sufficient interconnected porosity for bony bridging and fusion, we developed a multiscale topology optimization technique. Topology optimization at the macroscopic scale provides optimal structural layout that ensures mechanical strength, while optimally designed microstructures, which replace the macroscopic material layout, ensure maximum permeability. Optimally designed cages were fabricated using solid, freeform fabrication of poly(ε-caprolactone) mixed with hydroxyapatite. Compression tests revealed that the yield strength of optimized fusion cages was two times that of typical human lumbar spine loads. Computational analysis further confirmed the mechanical integrity within the human lumbar spine, although the pore structure locally underwent higher stress than yield stress. This optimization technique may be utilized to balance the complex requirements of load-bearing, stress shielding, and interconnected porosity when using biodegradable materials for fusion cages.
NASA Astrophysics Data System (ADS)
Auluck, S. K. H.
2014-12-01
Dense plasma focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool that can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited (Auluck 2013 Phys. Plasmas 20 112501) Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather-type plasma focus fitted to thousands of automated computations, which enables the construction of such a design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a four-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy of the drive parameter over eight decades in capacitor bank energy. The optimized geometry of plasma focus normalized to the anode radius is shown to be independent of voltage, while the optimized anode radius is shown to be related to capacitor bank inductance.
NASA Astrophysics Data System (ADS)
Biswas, A.; Sharma, S. P.
2012-12-01
Self-Potential anomaly is an important geophysical technique that measures the electrical potential due natural source of current in the Earth's subsurface. An inclined sheet type model is a very familiar structure associated with mineralization, fault plane, groundwater flow and many other geological features which exhibits self potential anomaly. A number of linearized and global inversion approaches have been developed for the interpretation of SP anomaly over different structures for various purposes. Mathematical expression to compute the forward response over a two-dimensional dipping sheet type structures can be described in three different ways using five variables in each case. Complexities in the inversion using three different forward approaches are different. Interpretation of self-potential anomaly using very fast simulated annealing global optimization has been developed in the present study which yielded a new insight about the uncertainty and equivalence in model parameters. Interpretation of the measured data yields the location of the causative body, depth to the top, extension, dip and quality of the causative body. In the present study, a comparative performance of three different forward approaches in the interpretation of self-potential anomaly is performed to assess the efficacy of the each approach in resolving the possible ambiguity. Even though each forward formulation yields the same forward response but optimization of different sets of variable using different forward problems poses different kinds of ambiguity in the interpretation. Performance of the three approaches in optimization has been compared and it is observed that out of three methods, one approach is best and suitable for this kind of study. Our VFSA approach has been tested on synthetic, noisy and field data for three different methods to show the efficacy and suitability of the best method. It is important to use the forward problem in the optimization that yields the
Graf, Peter A.; Billups, Stephen
2017-07-24
Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less
Tagami, Takashi; Kuwamoto, Kentaro; Watanabe, Akihiro; Unemoto, Kyoko; Yokobori, Shoji; Matsumoto, Gaku; Yokota, Hiroyuki
2014-06-01
Limited evidence supports the use of hemodynamic variables that correlate with delayed cerebral ischemia or pulmonary edema after aneurysmal subarachnoid hemorrhage. The aim of this study was to identify those hemodynamic variables that are associated with delayed cerebral ischemia and pulmonary edema after subarachnoid hemorrhage. A multicenter prospective cohort study. Nine university hospitals in Japan. A total of 180 patients with aneurysmal subarachnoid hemorrhage. None. Patients were prospectively monitored using a transpulmonary thermodilution system in the 14 days following subarachnoid hemorrhage. Delayed cerebral ischemia was developed in 35 patients (19.4%) and severe pulmonary edema was developed in 47 patients (26.1%). Using the Cox proportional hazards model, the mean global end-diastolic volume index (normal range, 680-800 mL/m) was the independent factor associated with the occurrence of delayed cerebral ischemia (hazard ratio, 0.74; 95% CI, 0.60-0.93; p = 0.008). Significant differences in global end-diastolic volume index were detected between the delayed cerebral ischemia and non-delayed cerebral ischemia groups (783 ± 25 mL/m vs 870 ± 14 mL/m; p = 0.007). The global end-diastolic volume index threshold that best correlated with delayed cerebral ischemia was less than 822 mL/m, as determined by receiver operating characteristic curves. Analysis of the Cox proportional hazards model indicated that the mean global end-diastolic volume index was the independent factor that associated with the occurrence of pulmonary edema (hazard ratio, 1.31; 95% CI, 1.02-1.71; p = 0.03). Furthermore, a significant positive correlation was identified between global end-diastolic volume index and extravascular lung water (r = 0.46; p < 0.001). The global end-diastolic volume index threshold that best correlated with severe pulmonary edema was greater than 921 mL/m. Our findings suggest that global end-diastolic volume index impacts both delayed cerebral ischemia
Pivot method for global optimization: a study of water clusters (H 2O) N with 2⩽ N⩽33
NASA Astrophysics Data System (ADS)
Nigra, Pablo; Kais, Sabre
1999-05-01
By means of the pivot method, an optimization work on water clusters (H 2O) N, with 2⩽ N⩽33, is carried out using an ab initio rigid molecule model, the Matsuoka-Clementi-Yoshimine potential, and an empirical flexible molecule model, the Stillinger-Rahman potential. The results show that, under certain conditions, the pivot method algorithm is likely to yield optimized structures that are related to one another in such a manner that they form families. The structures in a family can be thought of as formed from the aggregation of single units to some specific structures. In addition, the sequences present an apparent asymptotic behavior.
NASA Astrophysics Data System (ADS)
Gressent, A.; Muhle, J.; Rigby, M. L.; Lunt, M. F.; Ganesan, A.; Prinn, R. G.; Krummel, P. B.; Fraser, P. J.; Steele, P.; Weiss, R. F.; Harth, C. M.; O'Doherty, S.; Young, D.; Park, S.; Li, S.; Yao, B.; Reimann, S.; Vollmer, M. K.; Maione, M.; Arduini, I.; Lunder, C. R.
2016-12-01
Sulfuryl fluoride (SO2F2) is used increasingly as a fumigant to replace methyl bromide (CH3Br), which was regulated under the Montreal Protocol (1986). Mühle et al., J. Geophys. Res., 2009) showed that SO2F2 had been accumulating in the global atmosphere with a growth rate of 5±1% per year from 1978 to 2007. They also determined, using the 2D AGAGE box model, that SO2F2 has a total atmospheric lifetime of 36±11 years mainly driven by the oceanic uptake. In addition, the global warming potential of SO2F2 has been estimated to be ≈4780 for a 100-year time horizon (Papadimitriou et al., J. Phys. Chem., 2008), which is similar to the CFC-11 (CCl3F) GWP. Thus it is a potent greenhouse gas and its emissions are expected to continue to increase in the future. Here we report the first estimations of the SO2F2 emissions and its ocean sink from January 2006 to the end of 2015 on both the global scale using a 3D Eulerian chemical transport model (MOZART-4) solving a Main Chain Monte Carlo (MCMC) inversion, and on the regional scale using a 3D Lagrangian dispersion model (NAME) via the reversible-jump trans-dimensional MCMC approach (Lunt et al., Geosci. Model Dev., 2016). The mole fractions calculated on the global scale are used as boundary conditions for emission calculations over the NAME regions in North America, Europe, East Asia and Australia. For this 10-year inversion we use observations from the AGAGE (Advanced Global Atmospheric Gases Experiment) starting with six stations in 2006, which are La Jolla (California), Mace Head (Ireland), Cape Grim (Australia), Ragged Point (Barbados), Trinidad Head (California) and Cape Matatula (Samoa). We then add observations from Gosan (South Korea) in 2007, Jungfraujoch (Switzerland) in 2008, Shandiangzi (China) and Ny-Alesund (Norway) in 2010, and Monte Cimone (Italy) in 2011, reducing the uncertainty associated with the regions located close to these stations. Results are compared to (i) the total global SO2F2 emissions
Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas
2014-01-01
Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
NASA Astrophysics Data System (ADS)
Fu, Rong-Huan; Zhang, Xing
2016-09-01
Supercritical carbon dioxide operated in a Brayton cycle offers a numerous of potential advantages for a power generation system, and a lot of thermodynamics analyses have been conducted to increase its efficiency. Because there are a lot of heat-absorbing and heat-lossing subprocesses in a practical thermodynamic cycle and they are implemented by heat exchangers, it will increase the gross efficiency of the whole power generation system to optimize the system combining thermodynamics and heat transfer theory. This paper analyzes the influence of the performance of heat exchangers on the actual efficiency of an ideal Brayton cycle with a simple configuration, and proposes a new method to optimize the power generation system, which aims at the minimum energy consumption. Although the method is operated only for the ideal working fluid in this paper, its merits compared to that only with thermodynamic analysis are fully shown.
2013-08-01
entails better modeling of the vehicle usage which will allow us to estimate the likelihood of a vehicle being available to connect to the microgrid ...vehicle platform itself, but to external systems and platforms. All power systems can be connected wirelessly, or through a microgrid . Therefore...restoration and contingency operations. Because a microgrid is designed for a period of time, our optimization problem considers factors such as cost
Xu, Guan; Hao, Zhaobing; Li, Xiaotao; Su, Jian; Liu, Huanping; Sun, Lina
2015-01-01
Equation construction of a laser plane demonstrates a remarkable importance for vision measurement systems based on the structured light. Here we create a simple 1D target with a cone at the bottom and a checkered pattern on the top to calibrate the equation of the laser plane in the view field of a camera. A group of 2D coordinates of the intersection points are extracted from the images with the 1D target at different positions. The objective function is constructed to optimize the coefficients of the laser plane by minimizing the difference between the distance from the feature point to the the origin point and the length of the 1D target. The projective lines of the optimized laser plane on the 3D calibration board overlap the real intersection lines in the experimental images. Finally, the comparison work about the influences of the non-Gaussian noise and point number is investigated experimentally. The experiments show that the method of the distance optimal object from the feature point to the origin point provides an accurate and robust calibration for the laser plane in structured light measurement. PMID:26149292
NASA Astrophysics Data System (ADS)
Wei, Ruiying; Guo, Boling; Li, Yin
2017-09-01
The Cauchy problem for the three-dimensional compressible magneto-micropolar fluid equations is considered. Existence of global-in-time smooth solutions is established under the condition that the initial data are small perturbations of some given constant state. Moreover, we obtain the time decay rates of the higher-order spatial derivatives of the solution by combining the Lp-Lq estimates for the linearized equations and the Fourier splitting method, if the initial perturbation is small in H3-norm and bounded in L1-norm.
Glenton, Claire; Lewin, Simon; Gülmezoglu, Ahmet Metin
2016-07-18
In 2012, the World Health Organization (WHO) published recommendations on the use of optimization or "task-shifting" strategies for key, effective maternal and newborn interventions (the OptimizeMNH guidance). When making recommendations about complex health system interventions such as task-shifting, information about the feasibility and acceptability of interventions can be as important as information about their effectiveness. However, these issues are usually not addressed with the same rigour. This paper describes our use of several innovative strategies to broaden the range of evidence used to develop the OptimizeMNH guidance. In this guidance, we systematically included evidence regarding the acceptability and feasibility of relevant task-shifting interventions, primarily using qualitative evidence syntheses and multi-country case study syntheses; we used an approach to assess confidence in findings from qualitative evidence syntheses (the Grading of Recommendations, Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach); we used a structured evidence-to-decision framework for health systems (the DECIDE framework) to help the guidance panel members move from the different types of evidence to recommendations. The systematic inclusion of a broader range of evidence, and the use of new guideline development tools, had a number of impacts. Firstly, this broader range of evidence provided relevant information about the feasibility and acceptability of interventions considered in the guidance as well as information about key implementation considerations. However, inclusion of this evidence required more time, resources and skills. Secondly, the GRADE-CERQual approach provided a method for indicating to panel members how much confidence they should place in the findings from the qualitative evidence syntheses and so helped panel members to use this qualitative evidence appropriately. Thirdly
NASA Astrophysics Data System (ADS)
Denning, A.; Lokupitiya, R. S.; Zupanski, D.; Kawa, S. R.; Baker, D. F.; Doney, S. C.; Gurney, K. R.
2009-12-01
We present a system to analyze GOSAT/Tanso data using a combination of existing models of CO2 exchanges due to hourly photosynthesis and respiration, daily air-sea gas exchange, biomass burning, Fossil Fuel Emissions, and atmospheric transport. This comprehensive system allows direct comparison to the observed record of both in-situ and remotely sensed atmospheric CO2 at hourly timescales. We have previously demonstrated that a lower-resolution version of the system has good skill at replicating diurnal, synoptic, and seasonal variations over vegetated land surfaces. The system is driven by meteorological output from the NASA Goddard EOS Data Assimilation System, version 5. Surface weather from the system drives calculations of terrestrial ecosystem metabolism (radiation, precipitation, humidity, temperature) and air-sea gas exchange (wind), with other input data coming from satellite data products (e.g., fPAR and LAI from MODIS, and ocean color from SeaWiFS and MODIS). The analysis system is evaluated using synthetic data on a 2 x 2.5 degree (lat x lon) global grid. Synthetic data are sampled in cloud-free columns along the GOSAT orbital ephemeris and used to estimate multiplicative biases to component fluxes by Ensemble Data Assimilation. The system is quite successful at retrieving mechanistic estimates of spatial patterns of surface carbon fluxes on monthly and annual timescales over land, but is less skillful over the oceans.
Luthra, Suman A; Hodge, Ian M; Pikal, Michael J
2008-09-01
The purpose of this research was to investigate the effect of annealing on the molecular mobility in lyophilized glasses using differential scanning calorimetry (DSC) and isothermal microcalorimetry (IMC) techniques. A second objective that emerged was a systematic study of the unusual pre-T(g) thermal events that were observed during DSC warming scans after annealing. Aspartame lyophilized with three different excipients; sucrose, trehalose and poly vinyl pyrrolidone (PVP) was studied. The aim of this work was to quantify the decrease in mobility in amorphous lyophilized aspartame formulations upon systematic postlyophilization annealing. DSC scans of aspartame:sucrose formulation (T(g) = 73 degrees C) showed the presence of a pre-T(g) endotherm which disappeared upon annealing. Aspartame:trehalose (T(g) = 112 degrees C) and aspartame:PVP (T(g) = 100 degrees C) showed a broad exotherm before T(g) and annealing caused appearance of endothermic peaks before T(g). This work also employed IMC to measure the global molecular mobility represented by structural relaxation time (tau(beta)) in both un-annealed and annealed formulations. The effect of annealing on the enthalpy relaxation of lyophilized glasses, as measured by DSC and IMC, was consistent with the behavior predicted using the Tool-Narayanaswamy-Moynihan (TNM) phenomenology (Luthra et al., 2007, in press). The results show that the systems annealed at T(g) -15 degrees C to T(g) -20 degrees C have the lowest molecular mobility.
Salceda, Susana; Barican, Arnaldo; Buscaino, Jacklyn; Goldman, Bruce; Klevenberg, Jim; Kuhn, Melissa; Lehto, Dennis; Lin, Frank; Nguyen, Phong; Park, Charles; Pearson, Francesca; Pittaro, Rick; Salodkar, Sayali; Schueren, Robert; Smith, Corey; Troup, Charles; Tsou, Dean; Vangbo, Mattias; Wunderle, Justus; King, David
2017-05-01
The RapidHIT(®) ID is a fully automated sample-to-answer system for short tandem repeat (STR)-based human identification. The RapidHIT ID has been optimized for use in decentralized environments and processes presumed single source DNA samples, generating Combined DNA Index System (CODIS)-compatible DNA profiles in less than 90min. The system is easy to use, requiring less than one minute of hands-on time. Profiles are reviewed using centralized linking software, RapidLINK™ (IntegenX, Pleasanton, CA), a software tool designed to collate DNA profiles from single or multiple RapidHIT ID systems at different geographic locations. The RapidHIT ID has been designed to employ GlobalFiler(®) Express and AmpFLSTR(®) NGMSElect™, Thermo Fisher Scientific (Waltham, MA) STR chemistries. The Developmental Validation studies were performed using GlobalFiler(®) Express with single source reference samples according to Scientific Working Group for DNA Analysis Methods guidelines. These results show that multiple RapidHIT ID systems networked with RapidLINK software form a highly reliable system for wide-scale deployment in locations such as police booking stations and border crossings enabling real-time testing of arrestees, potential human trafficking victims, and other instances where rapid turnaround is essential.
Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza
2013-02-07
Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.
Barry, Jeremy A.; Muddiman, David C.
2013-01-01
Design of experiments (DOE) is a systematic and cost-effective approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response can be measured in few experiments. Herein, we describe the use of statistical DOE to improve a few of the analytical figures of merit of the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source for mass spectrometry. In a typical experiment, bovine cytochrome c (~12 kDa) was ionized via electrospray, and equine cytochrome c (~12 kDa) was desorbed and ionized by IR-MALDESI such that the ratio of equine:bovine was used as a measure of the ionization efficiency of IR-MALDESI. This response was used to rank the importance of seven source parameters including flow rate, laser fluence, laser repetition rate, ESI emitter to mass spectrometer inlet distance, sample stage height, sample plate voltage, and the sample to mass spectrometer inlet distance. A screening fractional factorial DOE was conducted to designate which of the seven parameters induced the greatest amount of change in the response. These important parameters (flow rate, stage height, sample to mass spectrometer inlet distance, and laser fluence) were then studied at higher resolution using a full factorial DOE to obtain the globally optimized combination of parameter settings. The optimum combination of settings was then compared with our previously determined settings to quantify the degree of improvement in detection limit. The limit of detection for the optimized conditions was approximately 10 attomoles compared with 100 femtomoles for the previous settings, which corresponds to a four order of magnitude improvement in the detection limit of equine cytochrome c. PMID:22095501
Optimization of the GOSAT global observation from space with region-by-region target-mode operations
NASA Astrophysics Data System (ADS)
kuze, A.; Suto, H.; Shiomi, K.; Kawakami, S.; Nakajima, M.
2013-12-01
observations, by uploading the pointing angles from the ground every day, TANSO-FTS can target a maximum of about 1,000 points per day. Dithering over fractional clouds area and targeting coast and islands avoiding bay and channels can increase yield rate. GOSAT has a UV band (380nm) in TANSO-CAI to observe dark land and FTS-SWIR bands has been acquiring two linear polarizations simultaneously. Multi-angle observations with forward, nadir and backward viewing with two axis pointing mirror will distinguish aerosol scattering from surface reflection and reduce aerosol related errors. The optimized target mode allocation are now considered. We will add the classification information of the target such as validation site, mega cities, volcano in the future Level 1B product to identify high bias possibility in XCO2. In addition, the geo-location information after the best estimate pointing-offset correction will be added. Lastly, after optimizing the observation locations, consistency between different gains, target brightness and aerosol optical thickness has to be confirmed. Validation other than TCCON site is also discussed.
Lagunas-Allué, L; Sanz-Asensio, J; Martínez-Soria, M T
2010-10-01
In this work, a simple and low-cost method based on matrix solid-phase dispersion (MSPD) and gas chromatography to determine eight multi-class pesticides such as vinclozolin, dichlofluanid, penconazol, captan, quinoxyfen, fluquinconazol, boscalid, and pyraclostrobin in grapes is described. Fungicide residues were identified and quantified using gas chromatography-mass spectrometry in selected ion monitoring mode (GC-MS, SIM). The experimental variables that affect the MSPD method, such as the amount of solid phase, solvent nature and elution volume were optimized using an experimental design. The best results were obtained using 0.5 g of grapes, 1.0 g of silica as clean-up sorbent, 1.50 g of C(18) as bonded phase and 10 mL of dichloromethane/ethyl acetate (1:1, v/v) as eluting solvent. Significant matrix effects observed for most of the pesticides tested were eliminated using matrix-matched standards. The pesticide recoveries in grapes samples were better than 80% except for captan. Intra-laboratory precision in terms of Horwitz ratio of the pesticides evaluated was below 0.5, suggesting ruggedness of the method. The quantification limits of the pesticides were in the range of 3.4-8.7 microg kg(-1), which were lower than the maximum residue limits (MRLs) of the pesticides in grapes samples established by the European legislation. Decision limits (CCalpha) and detection capability (CCbeta) have been calculated. The expanded uncertainties at two levels of concentration were <20% for all analytes.
NASA Astrophysics Data System (ADS)
Rodiet, Christophe; Remy, Benjamin; Degiovanni, Alain
2016-05-01
In this paper, it is shown how to select the optimal wavelengths minimizing the relative error and the standard deviation of the temperature. Furthermore, it is shown that the optimal wavelengths in mono-spectral and bi-spectral methods (for a Planck's law) can be determined by laws analogous to the displacement Wien's law. The simplicity of these laws can thus allow real-time selection of optimal wavelengths for a control/optimization of industrial processes, for example. A more general methodology to obtain the optimal wavelengths selection in a multi-spectral method (taking into account the spectral variations of the global transfer function including the emissivity variations) for temperature measurement of surfaces exhibiting non-uniform emissivity, is also presented. This latter can then find an interest in glass furnaces temperature measurement with spatiotemporal non-uniformities of emissivity, the control of biomass pyrolysis, the surface temperature measurement of buildings or heating devices, for example. The goal consists of minimizing the standard deviation of the estimated temperature (optimal design experiment). For the multi-spectral method, two cases will be treated: optimal global and optimal constrained wavelengths selection (to the spectral range of the detector, for example). The estimated temperature results obtained by different models and for different number of parameters and wavelengths are compared. These different points are treated from theoretical, numerical and experimental points of view.
Liew, Steven; Signorini, Massimo; Vieira Braz, André; Fagien, Steven; Swift, Arthur; De Boulle, Koenraad L.; Raspaldo, Hervé; Trindade de Almeida, Ada R.; Monheit, Gary
2016-01-01
Background: Combination of fillers and botulinum toxin for aesthetic applications is increasingly popular. Patient demographics continue to diversify, and include an expanding population receiving maintenance treatments over decades. Methods: A multinational panel of plastic surgeons and dermatologists convened the Global Aesthetics Consensus Group to develop updated guidelines with a worldwide perspective for hyaluronic acid fillers and botulinum toxin. This publication considers strategies for combined treatments, and how patient diversity influences treatment planning and outcomes. Results: Global Aesthetics Consensus Group recommendations reflect increased use of combined treatments in the lower and upper face, and some midface regions. A fully patient-tailored approach considers physiologic and chronologic age, ethnically associated facial morphotypes, and aesthetic ideals based on sex and culture. Lower toxin dosing, to modulate rather than paralyze muscles, is indicated where volume deficits influence muscular activity. Combination of toxin with fillers is appropriate for several indications addressed previously with toxin alone. New scientific data regarding hyaluronic acid fillers foster an evidence-based approach to selection of products and injection techniques. Focus on aesthetic units, rather than isolated rhytides, optimizes results from toxin and fillers. It also informs longitudinal treatment planning, and analysis of toxin nonresponders. Conclusions: The emerging objective of injectable treatment is facial harmonization rather than rejuvenation. Combined treatment is now a standard of care. Its use will increase further as we refine the concept that aspects of aging are intimately related, and that successful treatment entails identifying and addressing the primary causes of each. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V. PMID:27119917
Shen, Tie; Rui, Bin; Zhou, Hong; Zhang, Ximing; Yi, Yin; Wen, Han; Zheng, Haoran; Wu, Jihui; Shi, Yunyu
2013-01-27
The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E
NASA Astrophysics Data System (ADS)
Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel
2016-04-01
The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences.
Carter, Patrick M.; Desmond, Jeffery S.; Akanbobnaab, Christopher; Oteng, Rockefeller A.; Rominski, Sarah; Barsan, William G.; Cunningham, Rebecca
2012-01-01
Background Although many global health programs focus on providing clinical care or medical education, improving clinical operations can have a significant effect on patient care delivery, especially in developing health systems without high-level operations management. Lean manufacturing techniques have been effective in decreasing emergency department (ED) length of stay, patient waiting times, numbers of patients leaving without being seen, and door-to-balloon times for ST-elevation myocardial infarction in developed health systems; but use of Lean in low to middle income countries with developing emergency medicine systems has not been well characterized. Objectives To describe the application of Lean manufacturing techniques to improve clinical operations at Komfo Anokye Teaching Hospital in Ghana and to identify key lessons learned to aid future global EM initiatives. Methods A three-week Lean improvement program focused on the hospital admissions process at Komfo Anokye Teaching Hospital was completed by a 14-person team in six stages: problem definition, scope of project planning, value stream mapping, root cause analysis, future state planning, and implementation planning. Results The authors identified eight lessons learned during our use of Lean to optimize the operations of an ED in a global health setting: 1) the Lean process aided in building a partnership with Ghanaian colleagues; 2) obtaining and maintaining senior institutional support is necessary and challenging; 3) addressing power differences among the team to obtain feedback from all team members is critical to successful Lean analysis; 4) choosing a manageable initial project is critical to influence long-term Lean use in a new environment; 5) data intensive Lean tools can be adapted and are effective in a less resourced health system; 6) several Lean tools focused on team problem solving techniques worked well in a low resource system without modification; 7) using Lean highlighted that
Carter, Patrick M; Desmond, Jeffery S; Akanbobnaab, Christopher; Oteng, Rockefeller A; Rominski, Sarah D; Barsan, William G; Cunningham, Rebecca M
2012-03-01
Although many global health programs focus on providing clinical care or medical education, improving clinical operations can have a significant effect on patient care delivery, especially in developing health systems without high-level operations management. Lean manufacturing techniques have been effective in decreasing emergency department (ED) length of stay, patient waiting times, numbers of patients leaving without being seen, and door-to-balloon times for ST-elevation myocardial infarction in developed health systems, but use of Lean in low to middle income countries with developing emergency medicine (EM) systems has not been well characterized. To describe the application of Lean manufacturing techniques to improve clinical operations at Komfo Anokye Teaching Hospital (KATH) in Ghana and to identify key lessons learned to aid future global EM initiatives. A 3-week Lean improvement program focused on the hospital admissions process at KATH was completed by a 14-person team in six stages: problem definition, scope of project planning, value stream mapping, root cause analysis, future state planning, and implementation planning. The authors identified eight lessons learned during our use of Lean to optimize the operations of an ED in a global health setting: 1) the Lean process aided in building a partnership with Ghanaian colleagues; 2) obtaining and maintaining senior institutional support is necessary and challenging; 3) addressing power differences among the team to obtain feedback from all team members is critical to successful Lean analysis; 4) choosing a manageable initial project is critical to influence long-term Lean use in a new environment; 5) data intensive Lean tools can be adapted and are effective in a less resourced health system; 6) several Lean tools focused on team problem-solving techniques worked well in a low-resource system without modification; 7) using Lean highlighted that important changes do not require an influx of resources; and
NASA Astrophysics Data System (ADS)
Schlundt, Cornelia; Sus, Oliver; Stapelberg, Stefan; Stengel, Martin; Hollmann, Rainer; Poulsen, Caroline
2015-04-01
In 2010 the ESA Climate Change Initiative (CCI) Cloud project was started along with 12 other CCI projects covering atmospheric, oceanic and terrestrial ECV data products. The main goal is the generation of satellite-based climate data records that meet the challenging requirements of the Global Climate Observing System (GCOS). The objective target within the ESA Cloud_cci project is the investigation of synergetic capabilities of past, existing and upcoming European and American satellite missions and thus, the generation of long-term coherent cloud property datasets covering 33 years. The cloud properties considered are cloud mask, cloud top estimates, cloud optical thickness, cloud effective radius and post processed parameters such as cloud liquid and ice water path. In this presentation we will discuss the benefit of using an optimal estimation retrieval framework, which provides consistence among the retrieved cloud variables and pixel-based uncertainty estimates based on different passive instruments such as AVHRR, MODIS and AATSR. We will summarize the results of the first phase of the project along with further developments and improvements in the retrieval scheme and hence the quality of the cloud products carried out in the second phase of the project. Moreover, we will show exemplary results of comprehensive validation with other well established satellite data records, surface observations and cloud climatologies (e.g., PATMOS-X, ISCCP, CLARA-A2, MODIS collection 6). These inter-comparison results will show the strengths and weaknesses of the Cloud_cci datasets.
Invasion Percolation and Global Optimization
NASA Astrophysics Data System (ADS)
Barabási, Albert-László
1996-05-01
Invasion bond percolation (IBP) is mapped exactly into Prim's algorithm for finding the shortest spanning tree of a weighted random graph. Exploring this mapping, which is valid for arbitrary dimensions and lattices, we introduce a new IBP model that belongs to the same universality class as IBP and generates the minimal energy tree spanning the IBP cluster.
Jin Chen
2009-12-07
Efficient and robust Variable Relaxation Solver, based on pseudo-transient continuation, is developed to solve nonlinear anisotropic thermal conduction arising from fusion plasma simulations. By adding first and/or second order artificial time derivatives to the system, this type of method advances the resulting time-dependent nonlinear PDEs to steady state, which is the solution to be sought. In this process, only the stiffness matrix itself is involved so that the numerical complexity and errors can be greatly reduced. In fact, this work is an extension of integrating efficient linear elliptic solvers for fusion simulation on Cray XIE. Two schemes are derived in this work, first and second order Variable Relaxations. Four factors are observed to be critical for efficiency and preservation of solution's symmetric structure arising from periodic boundary condition: refining meshes in different coordinate directions, initializing nonlinear process, varying time steps in both temporal and spatial directions, and accurately generating nonlinear stiffness matrix. First finer mesh scale should be taken in strong transport direction; Next the system is carefully initialized by the solution with linear conductivity; Third, time step and relaxation factor are vertex-based varied and optimized at each time step; Finally, the nonlinear stiffness matrix is updated by just scaling corresponding linear one with the vector generated from nonlinear thermal conductivity.
Melby, Melissa K; Loh, Lawrence C; Evert, Jessica; Prater, Christopher; Lin, Henry; Khan, Omar A
2016-05-01
Increasing demand for global health education in medical training has driven the growth of educational programs predicated on a model of short-term medical service abroad. Almost two-thirds of matriculating medical students expect to participate in a global health experience during medical school, continuing into residency and early careers. Despite positive intent, such short-term experiences in global health (STEGHs) may exacerbate global health inequities and even cause harm. Growing out of the "medical missions" tradition, contemporary participation continues to evolve. Ethical concerns and other disciplinary approaches, such as public health and anthropology, can be incorpo rated to increase effectiveness and sustainability, and to shift the culture of STEGHs from focusing on trainees and their home institutions to also considering benefits in host communities and nurtur ing partnerships. The authors propose four core principles to guide ethical development of educational STEGHs: (1) skills building in cross-cultural effective ness and cultural humility, (2) bidirectional participatory relationships, (3) local capacity building, and (4) long-term sustainability. Application of these principles highlights the need for assessment of STEGHs: data collection that allows transparent compar isons, standards of quality, bidirectionality of agreements, defined curricula, and ethics that meet both host and sending countries' standards and needs. To capture the enormous potential of STEGHs, a paradigm shift in the culture of STEGHs is needed to ensure that these experiences balance training level, personal competencies, medical and cross-cultural ethics, and educational objectives to minimize harm and maximize benefits for all involved.
ERIC Educational Resources Information Center
Schwartz, Robert
2012-01-01
This issue of ETS Policy Notes (Vol. 20, No. 3) provides highlights from the Salzburg Global Seminar in December 2011. The seminar focused on bettering the educational and life prospects of students up to age 18 worldwide. [This article was written with the assistance of Beth Brody.
NASA Technical Reports Server (NTRS)
Robertson, Franklin; Goodman, Steven J.; Christy, John R.; Fitzjarrald, Daniel E.; Chou, Shi-Hung; Crosson, William; Wang, Shouping; Ramirez, Jorge
1993-01-01
This research is the MSFC component of a joint MSFC/Pennsylvania State University Eos Interdisciplinary Investigation on the global water cycle extension across the earth sciences. The primary long-term objective of this investigation is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates change on both global and regional scales. Significant accomplishments in the past year are presented and include the following: (1) water vapor variability; (2) multi-phase water analysis; (3) global modeling; and (4) optimal precipitation and stream flow analysis and hydrologic processes.
Yang, Zili
2009-03-19
In the duration of this project, we finished the main tasks set up in the initial proposal. These tasks include: collecting needed data of regional aerosol emissions (mainly SO2); building the RICES model; conducting preliminary simulation runs on some policy scenarios. We established a unified and transparent IA modeling platform that connecting climate change and local air pollution. The RICES model is the pioneering IA model that treats climate change and local air pollution as correlated global and local stock externalities.
Multidisciplinary optimization
Dennis, J.; Lewis, R.M.; Cramer, E.J.; Frank, P.M.; Shubin, G.R.
1994-12-31
This talk will use aeroelastic design and reservoir characterization as examples to introduce some approaches to MDO, or Multidisciplinary Optimization. This problem arises especially in engineering design, where it is considered of paramount importance in today`s competitive global business climate. It is interesting to an optimizer because the constraints involve coupled dissimilar systems of parameterized partial differential equations each arising from a different discipline, like structural analysis, computational fluid dynamics, etc. Usually, these constraints are accessible only through pde solvers rather than through algebraic residual calculations as we are used to having. Thus, just finding a multidisciplinary feasible point is a daunting task. Many such problems have discrete variable disciplines, multiple objectives, and other challenging features. After discussing some interesting practical features of the design problem, we will give some standard ways to formulate the problem as well as some novel ways that lend themselves to divide-and-conquer parallelism.
1984-08-01
Variances: Sets A and B2 53- 6.8 Comparison of Anomaly Degree Variances: Sets A and B1 54 -vi - . . . .. . . . . . . . . .. . .. List of Tables 3.1 CPU Time ...sin ’)] (1.1) r n=2 m=O r nm nm nm where GM is the gravitational constant times the earth’s mass, a is the equatorial radius of the best-fitting...problems. His tests were carried out with global set of 5°x 50 anomalies. He estimated CPU time of about two hours, on an Amdahl 470 V/6-I computer
NASA Astrophysics Data System (ADS)
Sperna Weiland, F. C.; Tisseuil, C.; Dürr, H. H.; Vrac, M.; van Beek, L. P. H.
2012-03-01
Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and
Ibinson, James W; Vogt, Keith M; Taylor, Kevin B; Dua, Shiv B; Becker, Christopher J; Loggia, Marco; Wasan, Ajay D
2015-12-01
The insula is uniquely located between the temporal and parietal cortices, making it anatomically well-positioned to act as an integrating center between the sensory and affective domains for the processing of painful stimulation. This can be studied through resting-state functional connectivity (fcMRI) imaging; however, the lack of a clear methodology for the analysis of fcMRI complicates the interpretation of these data during acute pain. Detected connectivity changes may reflect actual alterations in low-frequency synchronous neuronal activity related to pain, may be due to changes in global cerebral blood flow or the superimposed task-induced neuronal activity. The primary goal of this study was to investigate the effects of global signal regression (GSR) and task paradigm regression (TPR) on the changes in functional connectivity of the left (contralateral) insula in healthy subjects at rest and during acute painful electric nerve stimulation of the right hand. The use of GSR reduced the size and statistical significance of connectivity clusters and created negative correlation coefficients for some connectivity clusters. TPR with cyclic stimulation gave task versus rest connectivity differences similar to those with a constant task, suggesting that analysis which includes TPR is more accurately reflective of low-frequency neuronal activity. Both GSR and TPR have been inconsistently applied to fcMRI analysis. Based on these results, investigators need to consider the impact GSR and TPR have on connectivity during task performance when attempting to synthesize the literature.
NASA Astrophysics Data System (ADS)
Hemmings, J. C. P.; Challenor, P. G.; Yool, A.
2015-03-01
Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to capture the dominant biogeochemical dynamics of a complex biological system. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established. The feasibility of establishing such a relationship is investigated for an intermediate complexity biogeochemistry model (MEDUSA) coupled with a widely used global ocean model (NEMO). A site-based mechanistic emulator is constructed for surface chlorophyll output from this target model as a function of model parameters. The emulator comprises an array of 1-D simulators and a statistical quantification of the uncertainty in their predictions. The unknown parameter-dependent biogeochemical environment, in terms of initial tracer concentrations and lateral flux information required by the simulators, is a significant source of uncertainty. It is approximated by a mean environment derived from a small ensemble of 3-D simulations representing variability of the target model behaviour over the parameter space of interest. The performance of two alternative uncertainty quantification schemes is examined: a direct method based on comparisons between simulator output and a sample of known target model "truths" and an indirect method that is only partially reliant on knowledge of the target model output. In general, chlorophyll records at a
Häfele, W; Sassin, W
1979-05-01
A global energy system is conceptualized and analyzed, the energy distributor sub-system of the worldwide supranational system. Its many interconnections are examined and traced back to their source to determine the major elements of this global energy system. Long-term trends are emphasized. The analysis begins with a discussion of the local systems that resulted from the deployment of technology in the mid-nineteenth century, continues with a description of the global system based on oil that has existed for the past two decades, and ends with a scenario implying that an energy transition will occur in the future in which use of coal, nuclear, and solar energy will predominate. A major problem for the future will be the management of this energy transition. The optimal use of global resources and the efficient management of this transition will require a stable and persistent global order.
NASA Technical Reports Server (NTRS)
Taylor, Brian R.
2012-01-01
A novel, efficient air data calibration method is proposed for aircraft with limited envelopes. This method uses output-error optimization on three-dimensional inertial velocities to estimate calibration and wind parameters. Calibration parameters are based on assumed calibration models for static pressure, angle of attack, and flank angle. Estimated wind parameters are the north, east, and down components. The only assumptions needed for this method are that the inertial velocities and Euler angles are accurate, the calibration models are correct, and that the steady-state component of wind is constant throughout the maneuver. A two-minute maneuver was designed to excite the aircraft over the range of air data calibration parameters and de-correlate the angle-of-attack bias from the vertical component of wind. Simulation of the X-48B (The Boeing Company, Chicago, Illinois) aircraft was used to validate the method, ultimately using data derived from wind-tunnel testing to simulate the un-calibrated air data measurements. Results from the simulation were accurate and robust to turbulence levels comparable to those observed in flight. Future experiments are planned to evaluate the proposed air data calibration in a flight environment.
Rapallo, Arnaldo; Rossi, Giulia; Ferrando, Riccardo; Fortunelli, Alessandro; Curley, Benjamin C; Lloyd, Lesley D; Tarbuck, Gary M; Johnston, Roy L
2005-05-15
A genetic algorithm approach is applied to the optimization of the potential energy of a wide range of binary metallic nanoclusters, Ag-Cu, Ag-Ni, Au-Cu, Ag-Pd, Ag-Au, and Pd-Pt, modeled by a semiempirical potential. The aim of this work is to single out the driving forces that make different structural motifs the most favorable at different sizes and chemical compositions. Paper I is devoted to the analysis of size-mismatched systems, namely, Ag-Cu, Ag-Ni, and Au-Cu clusters. In Ag-Cu and Ag-Ni clusters, the large size mismatch and the tendency of Ag to segregate at the surface of Cu and Ni lead to the location of core-shell polyicosahedral minimum structures. Particularly stable polyicosahedral clusters are located at size N = 34 (at the composition with 27 Ag atoms) and N = 38 (at the composition with 32 and 30 Ag atoms). In Ag-Ni clusters, Ag32Ni13 is also shown to be a good energetic configuration. For Au-Cu clusters, these core-shell polyicosahedra are less common, because size mismatch is not reinforced by a strong tendency to segregation of Au at the surface of Cu, and Au atoms are not well accommodated upon the strained polyicosahedral surface.
NASA Astrophysics Data System (ADS)
Rapallo, Arnaldo; Rossi, Giulia; Ferrando, Riccardo; Fortunelli, Alessandro; Curley, Benjamin C.; Lloyd, Lesley D.; Tarbuck, Gary M.; Johnston, Roy L.
2005-05-01
A genetic algorithm approach is applied to the optimization of the potential energy of a wide range of binary metallic nanoclusters, Ag-Cu, Ag-Ni, Au-Cu, Ag-Pd, Ag-Au, and Pd-Pt, modeled by a semiempirical potential. The aim of this work is to single out the driving forces that make different structural motifs the most favorable at different sizes and chemical compositions. Paper I is devoted to the analysis of size-mismatched systems, namely, Ag-Cu, Ag-Ni, and Au-Cu clusters. In Ag-Cu and Ag-Ni clusters, the large size mismatch and the tendency of Ag to segregate at the surface of Cu and Ni lead to the location of core-shell polyicosahedral minimum structures. Particularly stable polyicosahedral clusters are located at size N =34 (at the composition with 27 Ag atoms) and N =38 (at the composition with 32 and 30 Ag atoms). In Ag-Ni clusters, Ag32Ni13 is also shown to be a good energetic configuration. For Au-Cu clusters, these core-shell polyicosahedra are less common, because size mismatch is not reinforced by a strong tendency to segregation of Au at the surface of Cu, and Au atoms are not well accommodated upon the strained polyicosahedral surface.
Optimally Stopped Optimization
NASA Astrophysics Data System (ADS)
Vinci, Walter; Lidar, Daniel A.
2016-11-01
We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark simulated annealing on a class of maximum-2-satisfiability (MAX2SAT) problems. We also compare the performance of a D-Wave 2X quantum annealer to the Hamze-Freitas-Selby (HFS) solver, a specialized classical heuristic algorithm designed for low-tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N =1098 variables, the D-Wave device is 2 orders of magnitude faster than the HFS solver, and, modulo known caveats related to suboptimal annealing times, exhibits identical scaling with problem size.
Neuroanatomy and Global Neuroscience.
DeFelipe, Javier
2017-07-05
Our brains are like a dense forest-a complex, seemingly impenetrable terrain of interacting cells mediating cognition and behavior. However, we should view the challenge of understanding the brain with optimism, provided that we choose appropriate strategies for the development of global neuroscience. Copyright © 2017 Elsevier Inc. All rights reserved.
Sassen, D. S.; Peterson, J. E.
2010-03-15
.g. Bautu et al., 2006). In the technique of algebraic reconstruction tomography (ART), which is used herein for the travel time inversion (Peterson et al., 1985), a small relaxation parameter will smooth imaging artifacts caused by data errors at the expense of resolution and contrast (Figure 2). However, large data errors such as unaccounted well deviations cannot be adequately suppressed through inversion weighting schemes. Previously, problems with tomograms were treated manually. However, in large data sets and/or networks of data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Mislocation of the transmitter and receiver stations of GPR cross-well tomography data sets can lead to serious imaging artifacts if not accounted for prior to inversion. Previously, problems with tomograms have been treated manually prior to inversion. In large data sets and/or networks of tomographic data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Our approach is to use cross-well data quality checks and a simplified model of borehole deviation with particle swarm optimization (PSO) to automatically correct for source and receiver locations prior to tomographic inversion. We present a simple model of well deviation, which is designed to minimize potential corruption of actual data trends. We also provide quantitative quality control measures based on minimizing correlations between take-off angle and apparent velocity, and a quality check on the continuity of velocity between adjacent wells. This methodology is shown to be accurate and robust for simple 2-D synthetic test cases. Plus, we demonstrate the method on actual field data where it is compared to deviation logs. This study shows the promise for automatic correction of well deviations in GPR tomographic data. Analysis of synthetic data shows that very precise estimates of well deviation can be made for small deviations, even in the
... Global Health Security HIV & Tuberculosis Global Health Protection Malaria & Parasitic Diseases Immunization Other Diseases & Threats Travelers' Health ... Seasonal changes in climate may muddle results of malaria interventions in Africa Medical Xpress September 28, 2017 ...
Recursive Optimization of Digital Circuits
1990-12-14
the increasing availability of Computir-Aided Design (CAD) tools and new Artificial Intelligence (AI) techniques, has caused research into auto- 1-1...digital logic design and testing, artificial intelligence, and combinatorics can be expressed as a sequence of operations on Boolean functions. (20) Before...optimization and global optimization-(22). 3.3.1 Local Optimization 3.3.1.1 The Use of Artificial Intelligence. Before we investigate local optimization
ERIC Educational Resources Information Center
Berkley, June, Ed.
1982-01-01
The articles in this collection deal with various methods of global education--education to prepare students to function as understanding and informed citizens of the world. Topics discussed in the 26 articles include: (1) the necessity of global education; (2) global education in the elementary school language arts curriculum; (3) science fiction…
ERIC Educational Resources Information Center
1997
This document contains four papers from a symposium on global human resource development (HRD). "Globalization of Human Resource Management (HRM) in Government: A Cross-Cultural Perspective" (Pan Suk Kim) relates HRM to national cultures and addresses its specific functional aspects with a unique dimension in a global organization.…
ERIC Educational Resources Information Center
Longstreet, Wilma S., Ed.
1988-01-01
This issue contains an introduction ("The Promise and Perplexity of Globalism," by W. Longstreet) and seven articles dedicated to exploring the meaning of global education for today's schools. "Global Education: An Overview" (J. Becker) develops possible definitions, identifies objectives and skills, and addresses questions and…
ERIC Educational Resources Information Center
1997
This document contains four papers from a symposium on global human resource development (HRD). "Globalization of Human Resource Management (HRM) in Government: A Cross-Cultural Perspective" (Pan Suk Kim) relates HRM to national cultures and addresses its specific functional aspects with a unique dimension in a global organization.…
Global interrupt and barrier networks
Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.
2008-10-28
A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.
Qian, Guoliang; Zhang, Yanbing; Zhou, Yijin; Liu, Chunhui; Zhao, Yancun; Song, Zhiwei; Fan, Jiaqin; Hu, Baishi; Liu, Fengquan
2012-09-01
Xanthomonas oryzae pv. oryzicola causes bacterial leaf streak in rice, a destructive disease worldwide. In this study, six putative hypothetical secreted proteins, which were absent in X. oryzae pv. oryzae, were detected from X. oryzae pv. oryzicola strain BLS256. Disruption-based mutagenesis study revealed that one of them, Xoc_15235, named as extracellular polysaccharide and virulence-related gene (epv), was required for the optimal virulence in host rice but not for the induction of a hypersensitive reaction in nonhost tobacco. Sequence analysis revealed that epv was highly conserved in Xanthomonas spp. (except X. oryzae pv. oryzae). In-frame deletion of epv in X. oryzae pv. oryzicola dramatically impaired pathogen virulence and extracellular polysaccharide (EPS) production, one of the important known virulence-associated functions in Xanthomonas spp. Quantitative real-time reverse-transcription polymerase chain reaction showed that expression of both gumB (a gene encoding exopolysaccharide xanthan biosynthesis export protein) and a known virulence-related gene, pgk (encoding phosphoglycerate kinase), were obviously reduced in the epv-deletion mutant compared with the wild-type strain Rs105. In addition, we observed that epv was positively regulated by both diffusible signal factor and global regulator Clp in X. oryzae pv. oryzicola. Taken together, the novel roles and genetics of epv of X. oryzae pv. oryzicola in the EPS production and virulence were investigated for the first time.
NASA Astrophysics Data System (ADS)
Tajuddin, Wan Ahmad
1994-02-01
Ease in finding the configuration at the global energy minimum in a symmetric neural network is important for combinatorial optimization problems. We carry out a comprehensive survey of available strategies for seeking global minima by comparing their performances in the binary representation problem. We recall our previous comparison of steepest descent with analog dynamics, genetic hill-climbing, simulated diffusion, simulated annealing, threshold accepting and simulated tunneling. To this, we add comparisons to other strategies including taboo search and one with field-ordered updating.
NASA Technical Reports Server (NTRS)
Robertson, Franklin R.; Christy, John R.; Goodman, Steven J.; Miller, Tim L.; Fitzjarrald, Dan; Lapenta, Bill; Wang, Shouping
1991-01-01
The primary objective is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates changes on both global and regional scales. The following subject areas are covered: (1) water vapor variability; (2) multi-phase water analysis; (3) diabatic heating; (4) MSU (Microwave Sounding Unit) temperature analysis; (5) Optimal precipitation and streamflow analysis; (6) CCM (Community Climate Model) hydrological cycle; (7) CCM1 climate sensitivity to lower boundary forcing; and (8) mesoscale modeling of atmosphere/surface interaction.
Geometric Computational Mechanics and Optimal Control
2011-12-02
methods. Further methods that depend on global optimization problems are in development and preliminary versions of these results, many of which...de la Sociedad Espanola de Matimatica Aplicada (SeMA), 50, 2010, pp 61-81. K. Flaßkamp, S. Ober-Blöbaum, M. Kobilarov, Solving optimal control...continuous setting. Consequently, globally optimal methods for computing optimal trajectories for vehicles with complex dynamics were developed. The
Decentralized Control Using Global Optimization (DCGO) (Preprint)
2007-03-01
simulation environment using BAE System’s proprietary M2CS (multi-vehicle mission control system) planner running in version 1.3 of the Boeing OEP...copies of M2CS are allowed to create plans for a SEAD mission. The coordination between the planners is handled using either an ideal communication
Decentralized Control Using Global Optimization (DCGO) (Postprint)
2007-03-01
protocol was used in a simulation environment using BAE System’s proprietary M2CS (multi-vehicle mission control system) planner running in version 1.3...system delays. vehicles utilizing identical copies of M2CS are allowed to create plans for a SEAD mission. The coordination between the planners is handled
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
Algorithms for bilevel optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.
Atmospheric Science Data Center
2013-04-19
... cover from one day to another. The lower panel is a composite in which red, green, and blue radiances from MISR's 70-degree ... In relatively clear ocean areas, the oblique-angle composite is generally brighter than its nadir counterpart due to enhanced ... Mar 2002 Images: Global Composite location: Global Images thumbnail: ...
Global warming, global research, and global governing
Preining, O.
1997-12-31
The anticipated dangers of Global Warming can be mitigated by reducing atmospheric greenhouse gas concentrations, especially CO{sub 2}. To reach acceptable, constant levels within the next couple of centuries it might be necessary to accept stabilization levels higher than present ones, The annual CO{sub 2} emissions must be reduced far below today`s values. This is a very important result of the models discussed in the 1995 IPCC report. However, any even very modest scenario for the future must take into account a substantial increase in the world population which might double during the 21st century, There is a considerable emission reduction potential of the industrialized world due to efficiency increase, However, the demand for energy services by the growing world population will, inspite of the availability of alternative energy resources, possibly lead to a net increase in fossil fuel consumption. If the climate models are right, and the science community believes they are, we will experience a global warming of the order of a couple of degrees over the next century; we have to live with it. To be prepared for the future it is essential for us to use new research techniques embracing not only the familiar fields of hard sciences but also social, educational, ethical and economic aspects, We must find a way to build up the essential intellectual capacities needed to deal with these kinds of general problems within all nations and all societies. But this is not Although, we also have to find the necessary dynamical and highly flexible structures for a global governing using tools such as the environmental regime. The first step was the Framework Convention On Climate Change, UN 1992; for resolution of questions regarding implementations the Conference of the Parties was established.
Global Partnership in Global Earth Observations
NASA Astrophysics Data System (ADS)
Smirnov, A.; Obersteiner, M.
2007-12-01
The emergence of a global partnership on earth observations will crucially drive the configuration of future observing systems and consequently shape how socio-economic benefits are generated. In this paper we take a game-theoretical approach to model cooperation on building global earth observation systems. We consider several societies whose economies are subject to shocks mimicking major natural disasters. Economies operate optimally and lead to the best possible expected value for the social welfares in the future. In order to increase its welfare even more society can make a decision to invest into a global alerting system which lowers the risk of disasters. We start our investigation from a single-society case and show conditions under which benefits of such investment can be reaped. The propensity to invest increases with economic affluence and degree of vulnerability to natural disasters. We find that for poor and/or less vulnerable countries it is better to forbear from investment. If to consider a situation of multiple societies a strategic gaming situation emerges motivated by the fact that every society will benefit from a global system regardless of whether they invested or not. Our analysis of possible equilibrium solutions shows that similar to the formation of trading blocks (e.g. EU, NAFTA) only in the case of similar societies we will observe cooperation behavior (when all invest) and otherwise we will observe free-riding. This insight, that we might face a prisoners dilemma problem in the formation of a GEOSS, has important implications for the GEO process.
Terascale Optimal PDE Simulations
David Keyes
2009-07-28
The Terascale Optimal PDE Solvers (TOPS) Integrated Software Infrastructure Center (ISIC) was created to develop and implement algorithms and support scientific investigations performed by DOE-sponsored researchers. These simulations often involve the solution of partial differential equations (PDEs) on terascale computers. The TOPS Center researched, developed and deployed an integrated toolkit of open-source, optimal complexity solvers for the nonlinear partial differential equations that arise in many DOE application areas, including fusion, accelerator design, global climate change and reactive chemistry. The algorithms created as part of this project were also designed to reduce current computational bottlenecks by orders of magnitude on terascale computers, enabling scientific simulation on a scale heretofore impossible.
NASA Technical Reports Server (NTRS)
Laird, Philip
1992-01-01
We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.
NASA Astrophysics Data System (ADS)
Lindberg Christensen, Lars; Russo, P.
2009-05-01
IYA2009 is a global collaboration between almost 140 nations and more than 50 international organisations sharing the same vision. Besides the common brand, mission, vision and goals, IAU established eleven cornerstones programmes to support the different IYA2009 stakeholder to organize events, activities under a common umbrella. These are global activities centred on specific themes and are aligned with IYA2009's main goals. Whether it is the support and promotion of women in astronomy, the preservation of dark-sky sites around the world or educating and explaining the workings of the Universe to millions, the eleven Cornerstones are key elements in the success of IYA2009. However, the process of implementing global projects across cultural boundaries is challenging and needs central coordination to preserve the pre-established goals. During this talk we will examine the ups and downs of coordinating such a project and present an overview of the principal achievements for the Cornerstones so far.
Vaccines: Shaping global health.
Pagliusi, Sonia; Ting, Ching-Chia; Lobos, Fernando
2017-03-14
The Developing Countries Vaccine Manufacturers' Network (DCVMN) gathered leaders in immunization programs, vaccine manufacturing, representatives of the Argentinean Health Authorities and Pan American Health Organization, among other global health stakeholders, for its 17th Annual General Meeting in Buenos Aires, to reflect on how vaccines are shaping global health. Polio eradication and elimination of measles and rubella from the Americas is a result of successful collaboration, made possible by timely supply of affordable vaccines. After decades of intense competition for high-value markets, collaboration with developing countries has become critical, and involvement of multiple manufacturers as well as public- and private-sector investments are essential, for developing new vaccines against emerging infectious diseases. The recent Zika virus outbreak and the accelerated Ebola vaccine development exemplify the need for international partnerships to combat infectious diseases. A new player, Coalition for Epidemic Preparedness Innovations (CEPI) has made its entrance in the global health community, aiming to stimulate research preparedness against emerging infections. Face-to-face panel discussions facilitated the dialogue around challenges, such as risks of viability to vaccine development and regulatory convergence, to improve access to sustainable vaccine supply. It was discussed that joint efforts to optimizing regulatory pathways in developing countries, reducing registration time by up to 50%, are required. Outbreaks of emerging infections and the global Polio eradication and containment challenges are reminders of the importance of vaccines' access, and of the importance of new public-private partnerships. Copyright © 2017.
NASA Astrophysics Data System (ADS)
Huang, X.; Hu, K.