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.
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)
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
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
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.
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.
Continuous Optimization on Constraint Manifolds
NASA Technical Reports Server (NTRS)
Dean, Edwin B.
1988-01-01
This paper demonstrates continuous optimization on the differentiable manifold formed by continuous constraint functions. The first order tensor geodesic differential equation is solved on the manifold in both numerical and closed analytic form for simple nonlinear programs. Advantages and disadvantages with respect to conventional optimization techniques are discussed.
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…
Dengue: a continuing global threat
Guzman, Maria G.; Halstead, Scott B.; Artsob, Harvey; Buchy, Philippe; Farrar, Jeremy; Gubler, Duane J.; Hunsperger, Elizabeth; Kroeger, Axel; Margolis, Harold S.; Martínez, Eric; Nathan, Michael B.; Pelegrino, Jose Luis; Simmons, Cameron; Yoksan, Sutee; Peeling, Rosanna W.
2014-01-01
Dengue fever and dengue haemorrhagic fever are important arthropod-borne viral diseases. Each year, there are ~50 million dengue infections and ~500,000 individuals are hospitalized with dengue haemorrhagic fever, mainly in Southeast Asia, the Pacific and the Americas. Illness is produced by any of the four dengue virus serotypes. A global strategy aimed at increasing the capacity for surveillance and outbreak response, changing behaviours and reducing the disease burden using integrated vector management in conjunction with early and accurate diagnosis has been advocated. Antiviral drugs and vaccines that are currently under development could also make an important contribution to dengue control in the future. PMID:21079655
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.
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.
Firefly Mating Algorithm for Continuous Optimization Problems
Ritthipakdee, Amarita; Premasathian, Nol; Jitkongchuen, Duangjai
2017-01-01
This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima. PMID:28808442
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.
Building a global business continuity programme.
Lazcano, Michael
2014-01-01
Business continuity programmes provide an important function within organisations, especially when aligned with and supportive of the organisation's goals, objectives and organisational culture. Continuity programmes for large, complex international organisations, unlike those for compact national companies, are more difficult to design, build, implement and maintain. Programmes for international organisations require attention to structural design, support across organisational leadership and hierarchy, seamless integration with the organisation's culture, measured success and demonstrated value. This paper details practical, but sometimes overlooked considerations for building successful global business continuity programmes.
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'.
Optimization of Secondary Concentrators with the Continuous Information Entropy Strategy
NASA Astrophysics Data System (ADS)
Schmidt, Tobias Christian; Ries, Harald
2010-10-01
In this contribution, a method for global optimization of noisy functions, the Continuous Information Entropy Strategy (CIES), is explained and its applicability for the optimization of solar concentrators is shown. The CIES is efficient because all decisions made during optimizations are based on criteria that are derived from the concept of information entropy. Two secondary concentrators have been optimized with the CIES. The optimized secondary concentrators convert circular light distributions of round focal spots to square light distributions to match with the shape of square PV cells. The secondary concentrators are highly efficient and have geometrical concentration ratios of 2.25 and 8 respectively. Part of this material has been published in: T. C. Schmidt, "Information Entropy-Based Decision Making in Optimization", Ph.D. Thesis, Philipps University Marburg, 2010.
Optimization of Continuous Maintenance Availability Scheduling
2014-09-01
disparity between the USS Emory S . Land and the USS Frank Cable ( Pickett 2013). The Emory S . Land is stationed in Diego Garcia and primarily supports...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE OPTIMIZATION OF CONTINUOUS MAINTENANCE AVAILABILITY SCHEDULING 5. FUNDING NUMBERS 6. AUTHOR( S ...Cyrus K. Anderson 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943–5000 8. PERFORMING
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.
A novel metaheuristic for continuous optimization problems: Virus optimization algorithm
NASA Astrophysics Data System (ADS)
Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue
2016-01-01
A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.
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 Optimization in Continuous-Flow FTNMR
NASA Astrophysics Data System (ADS)
Sudmeier, James L.; Günther, Ulrich L.; Albert, Klaus; Bachovchin, William W.
Equations simulating the steady-state magnetization of liquids in continuous-flow FTNMR are derived using a classical vector model, assuming plug flow. These equations are applied to calculation of ( S/ N) t, the relative signal/noise per unit time of any nucleus undergoing any degree of Overhauser enhancement either in the detection cell or upstream, or both, and to optimization of experimental conditions, including pulse repetition time Trep, pulse angle β, and flow rate. Ideal parameters include a pulse angle of 90° and a Trepvalue equal to sample residence time in the NMR detection cell. Optimal flow rates are directly proportional to the premagnetization volume (the portion of sample equilibrated with the magnetic field prior to detection) and inversely proportional to spin-lattice relaxation times T1. Optimal premagnetization times are smaller than previously assumed, varying from about 1.1 to 1.9 T1values. ( S/ N) tfor static FTNMR is discussed in some detail, and a new graphical method is presented for its optimization. Flow advantage, the ( S/ N) tof optimized flow FTNMR experiments compared to that of static FTNMR in a given detection cell, is proportional to the square root of the ratio of premagnetization to detection cell volumes, and virtually independent of[formula]where[formula]is the apparent transverse-relaxation time. The theory is applied to examples from recent literature, including dynamic electron-nuclear polarization, and the literature is critically reviewed. The analysis shows that claims by previous authors of recycled flow FTNMR by itself leading to increased ( S/ N) tfor slowly relaxing resonances are misleading, owing to underdetermination of ( S/ N) tin static measurements and failure to account for greater sample sizes required in flow experiments. For monitoring and control of chemical processes, the theory presented here enables the first rational basis for the design of a flow FTNMR apparatus and for the selection of acquisition
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.
Throughput Optimization of Continuous Biopharmaceutical Manufacturing Facilities.
Garcia, Fernando A; Vandiver, Michael W
2017-01-01
In order to operate profitably under different product demand scenarios, biopharmaceutical companies must design their facilities with mass output flexibility in mind. Traditional biologics manufacturing technologies pose operational challenges in this regard due to their high costs and slow equipment turnaround times, restricting the types of products and mass quantities that can be processed. Modern plant design, however, has facilitated the development of lean and efficient bioprocessing facilities through footprint reduction and adoption of disposable and continuous manufacturing technologies. These development efforts have proven to be crucial in seeking to drastically reduce the high costs typically associated with the manufacturing of recombinant proteins. In this work, mathematical modeling is used to optimize annual production schedules for a single-product commercial facility operating with a continuous upstream and discrete batch downstream platform. Utilizing cell culture duration and volumetric productivity as process variables in the model, and annual plant throughput as the optimization objective, 3-D surface plots are created to understand the effect of process and facility design on expected mass output. The model shows that once a plant has been fully debottlenecked it is capable of processing well over a metric ton of product per year. Moreover, the analysis helped to uncover a major limiting constraint on plant performance, the stability of the neutralized viral inactivated pool, which may indicate that this should be a focus of attention during future process development efforts.LAY ABSTRACT: Biopharmaceutical process modeling can be used to design and optimize manufacturing facilities and help companies achieve a predetermined set of goals. One way to perform optimization is by making the most efficient use of process equipment in order to minimize the expenditure of capital, labor and plant resources. To that end, this paper introduces a
Enlightening Globalization: An Opportunity for Continuing Education
ERIC Educational Resources Information Center
Reimers, Fernando
2009-01-01
Globalization presents a new social context for educational institutions from elementary schools to universities. In response to this new context, schools and universities are slowly changing their ways. These changes range from altering the curriculum so that students understand the process of globalization itself, or developing competencies…
Optimal signal processing for continuous qubit readout
NASA Astrophysics Data System (ADS)
Ng, Shilin; Tsang, Mankei
2014-08-01
The measurement of a quantum two-level system, or a qubit in modern terminology, often involves an electromagnetic field that interacts with the qubit, before the field is measured continuously and the qubit state is inferred from the noisy field measurement. During the measurement, the qubit may undergo spontaneous transitions, further obscuring the initial qubit state from the observer. Taking advantage of some well-known techniques in stochastic detection theory, here we propose a signal processing protocol that can infer the initial qubit state optimally from the measurement in the presence of noise and qubit dynamics. Assuming continuous quantum-nondemolition measurements with Gaussian or Poissonian noise and a classical Markov model for the qubit, we derive analytic solutions to the protocol in some special cases of interest using Itō calculus. Our method is applicable to multihypothesis testing for robust qubit readout and relevant to experiments on qubits in superconducting microwave circuits, trapped ions, nitrogen-vacancy centers in diamond, semiconductor quantum dots, or phosphorus donors in silicon.
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.
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 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.
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)
Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu
2017-05-24
In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.
Local and Global Comparison of Continuous Functions
Edelsbrunner, H; Harer, J; Natarajan, V; Pascucci, V
2004-12-16
We introduce local and global comparison measures for a collection of k {<=} d real-valued smooth functions on a common d-dimensional Riemannian manifold. For k = d = 2 we relate the measures to the set of critical points of one function restricted to the level sets of the other. The definition of the measures extends to piecewise linear functions for which they are easy to compute. The computation of the measures forms the centerpiece of a software tool which we use to study scientific datasets.
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.
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.
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 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.
Global Grazing Systems: Their Continuing Importance in Meeting Global Demand
NASA Astrophysics Data System (ADS)
Davis, K. F.; D'Odorico, P.
2014-12-01
Animal production exerts significant demand on land, water and food resources and is an extensive means by which humans modify natural systems. Demand for animal source foods has more than tripled over the past 50 years due to population growth and dietary change. To meet this demand, livestock intensification (e.g. concentrated animal feeding operations) has increased and with it the water, nitrogen and carbon footprints of animal production. However, grass-fed systems continue to contribute significantly to overall animal production. To date, little is known about the contributions of grass- and grain-fed systems to animal calorie production, how this has changed through time and to what extent these two systems are sensitive to climate. Using a calorie-based approach we hypothesize that grain-fed systems are increasing in importance (with serious implications for water and nutrient demand) and that rangeland productivity is correlated with rainfall. Our findings show that grass-fed systems made up the majority of animal calorie production since 1960 years but that the relative contribution of grain-fed system has increased (from 27% to 49%). This rapid transition towards grain-fed animal production is largely a result of changing diets demand, as we found the growth of grass-fed production only kept pace with population growth. On a regional scale, we find that Asia has been the major contributor to the increase in grass-fed animal calorie production and that Africa has undergone the most drastic transition from grass-fed to grain-fed dependence. Finally, as expected we see a positive relationship between rangeland productivity and precipitation and a shift from dairy- to meat-dominated production going from drier to wetter climates. This study represents a new means of analyzing the food security of animal products and an important step in understanding the historic trends of animal production, their relation to climate, their prospects for the future and their
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.
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.
Bolus vs. continuous feeding to optimize anabolism in neonates
USDA-ARS?s Scientific Manuscript database
Neonates with feeding difficulties can be fed by orogastric tube, using either continuous or bolus delivery. This review reports on recent findings that bolus is advantageous compared to continuous feeding in supporting optimal protein anabolism. Whether bolus or continuous feeding is more beneficia...
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
NASA Astrophysics Data System (ADS)
Gao, Wei
2016-05-01
The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.
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 Symmetries, Volume Independence, and Continuity in Quantum Field Theories.
Sulejmanpasic, Tin
2017-01-06
We discuss quantum field theories with global SU(N) and O(N) symmetries for which temporal direction is compactified on a circle of size L with periodicity of fields up to a global symmetry transformation, i.e., twisted boundary conditions. Such boundary conditions correspond to an insertion of the global symmetry operator in the partition function. We argue in general and prove in particular for CP(N-1) and O(N) nonlinear sigma models that large-N volume independence holds. Further we show that the CP(N-1) theory is free from the Affleck phase transition confirming the Ünsal-Dunne continuity conjecture.
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.
Continuously Optimized Reliable Energy (CORE) Microgrid: Models & Tools (Fact Sheet)
Not Available
2013-07-01
This brochure describes Continuously Optimized Reliable Energy (CORE), a trademarked process NREL employs to produce conceptual microgrid designs. This systems-based process enables designs to be optimized for economic value, energy surety, and sustainability. Capabilities NREL offers in support of microgrid design are explained.
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.
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.
Global distribution and properties of continuing current in lightning
NASA Astrophysics Data System (ADS)
Bitzer, Phillip M.
2017-01-01
Continuing current is a process in lightning in which the current in a conducting channel can flow for much longer than in a typical lightning discharge. The phenomenon can be characterized by the continuous optical emission that accompanies the current flow. Using the Lightning Imaging Sensor (LIS), lightning with continuing current is identified on a global scale. Lightning that contains optical emission over at least five consecutive LIS frames, roughly 7-9 ms, are classified as continuing current flashes. This differs from typical lightning discharges that produce optical emission for one or two consecutive frames. Of the flashes detected by LIS, 11.2% contain continuing current. These flashes optically radiate over a larger footprint and have a longer duration than ones that do not. The spatial distribution of these flashes indicates that regions of high lightning activity may not be correlated with a high likelihood of continuing current flashes. Further, oceanic and winter lightning are shown to have a higher proportion of continuing current flashes. Finally, 25-40% of flashes identified by LIS to have continuing current have only an intracloud pulse detected by the National Lightning Detection Network (NLDN), with no cloud-to-ground strokes detected.
A quantitative method for optimized placement of continuous air monitors.
Whicker, Jeffrey J; Rodgers, John C; Moxley, John S
2003-11-01
Alarming continuous air monitors (CAMs) are a critical component for worker protection in facilities that handle large amounts of hazardous materials. In nuclear facilities, continuous air monitors alarm when levels of airborne radioactive materials exceed alarm thresholds, thus prompting workers to exit the room to reduce inhalation exposures. To maintain a high level of worker protection, continuous air monitors are required to detect radioactive aerosol clouds quickly and with good sensitivity. This requires that there are sufficient numbers of continuous air monitors in a room and that they are well positioned. Yet there are no published methodologies to quantitatively determine the optimal number and placement of continuous air monitors in a room. The goal of this study was to develop and test an approach to quantitatively determine optimal number and placement of continuous air monitors in a room. The method we have developed uses tracer aerosol releases (to simulate accidental releases) and the measurement of the temporal and spatial aspects of the dispersion of the tracer aerosol through the room. The aerosol dispersion data is then analyzed to optimize continuous air monitor utilization based on simulated worker exposure. This method was tested in a room within a Department of Energy operated plutonium facility at the Savannah River Site in South Carolina, U.S. Results from this study show that the value of quantitative airflow and aerosol dispersion studies is significant and that worker protection can be significantly improved while balancing the costs associated with CAM programs.
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.
Multivariable adaptive optimization of a continuous bioreactor with a constraint
Chang, Y.K.
1987-01-01
A single-variable on-line adaptive optimization algorithm using a bilevel forgetting factor was developed. Also a modified version of this algorithm was developed to handle a quality constraint. Both algorithms were tested in simulation studies on a continuous bakers' yeast culture for optimization speed and accuracy, reoptimization capability, and long term operational stability. The above algorithms were extended to a multivariable on-line adaptive optimization and tested in simulated optimization studies with and without a constraint on the residual ethanol concentration. The dilution rate (D) and the temperature (T) were manipulated to maximize the cellular productivity (DX). It took about 80 hours to optimize the culture and the attained steady state was very close to the optimum. When tested with a big step change in the feed substrate concentration it took 60 to 80 hours to drive and maintain the cellular productivity close to the new optimum value. Long term operational stability was also tested. The multivariable algorithm was experimentally applied to an actual bakers' yeast culture. Only unconstrained optimization was carried out. The optimization required 50 to 90 hours. The attained steady state was D = 0.301 1/hr, T = 32.8 C, and DX = 1.500 g/l/hr. A fast inferential optimization algorithm based on one of the fast responding off-gas data, the carbon dioxide evolution rate (CER), was proposed. In simulation and experimental studies this new algorithm is 2 to 3 times faster in optimization speed.
Optimal ordering policies for continuous review perishable inventory models.
Weiss, H J
1980-01-01
This paper extends the notions of perishable inventory models to the realm of continuous review inventory systems. The traditional perishable inventory costs of ordering, holding, shortage or penalty, disposal and revenue are incorporated into the continuous review framework. The type of policy that is optimal with respect to long run average expected cost is presented for both the backlogging and lost-sales models. In addition, for the lost-sales model the cost function is presented and analyzed.
Linear optimal control of continuous time chaotic systems.
Merat, Kaveh; Abbaszadeh Chekan, Jafar; Salarieh, Hassan; Alasty, Aria
2014-07-01
In this research study, chaos control of continuous time systems has been performed by using dynamic programming technique. In the first step by crossing the response orbits with a selected Poincare section and subsequently applying linear regression method, the continuous time system is converted to a discrete type. Then, by solving the Riccati equation a sub-optimal algorithm has been devised for the obtained discrete chaotic systems. In the next step, by implementing the acquired algorithm on the quantized continuous time system, the chaos has been suppressed in the Rossler and AFM systems as some case studies.
Optimality of Gaussian attacks in continuous-variable quantum cryptography.
Navascués, Miguel; Grosshans, Frédéric; Acín, Antonio
2006-11-10
We analyze the asymptotic security of the family of Gaussian modulated quantum key distribution protocols for continuous-variables systems. We prove that the Gaussian unitary attack is optimal for all the considered bounds on the key rate when the first and second momenta of the canonical variables involved are known by the honest parties.
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.
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.
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
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.
Optimal continuous-variable teleportation under energy constraint
NASA Astrophysics Data System (ADS)
Lee, Jaehak; Park, Jiyong; Nha, Hyunchul
2017-05-01
Quantum teleportation is one of the crucial protocols in quantum information processing. It is important to accomplish an efficient teleportation under practical conditions, aiming at a higher fidelity desirably using fewer resources. The continuous-variable (CV) version of quantum teleportation was first proposed using a Gaussian state as a quantum resource, while other attempts were also made to improve performance by applying non-Gaussian operations. We investigate the CV teleportation to find its ultimate fidelity under energy constraint identifying an optimal quantum state. For this purpose, we present a formalism to evaluate teleportation fidelity as an expectation value of an operator. Using this formalism, we prove that the optimal state must be a form of photon-number entangled states. We further show that Gaussian states are near optimal, while non-Gaussian states make a slight improvement and therefore are rigorously optimal, particularly in the low-energy regime.
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
Stability and optimal parameters for continuous feedback chaos control.
Kouomou, Y Chembo; Woafo, P
2002-09-01
We investigate the conditions under which an optimal continuous feedback control can be achieved. Chaotic oscillations in the single-well Duffing model, with either a positive or a negative nonlinear stiffness term, are tuned to their related Ritz approximation. The Floquet theory enables the stability analysis of the control. Critical values of the feedback control coefficient fulfilling the optimization criteria are derived. The influence of the chosen target orbit, of the feedback coefficient, and of the onset time of control on its duration is discussed. The analytic approach is confirmed by numerical simulations.
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.
Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui
2010-02-01
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.
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.
Continuous Evolution of Statistical Estimators for Optimal Decision-Making
Saunders, Ian; Vijayakumar, Sethu
2012-01-01
In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For example, when asked to combine information from multiple sources we often assign greater weight to the more reliable information. It has been proposed that statistical-optimality often observed in human perception and decision-making requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore, subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie optimal decision making. PMID:22761657
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.
Hölder Continuity and Injectivity of Optimal Maps
NASA Astrophysics Data System (ADS)
Figalli, Alessio; Kim, Young-Heon; McCann, Robert J.
2013-09-01
Consider transportation of one distribution of mass onto another, chosen to optimize the total expected cost, where cost per unit mass transported from x to y is given by a smooth function c( x, y). If the source density f +( x) is bounded away from zero and infinity in an open region {U' subset {R}^n}, and the target density f -( y) is bounded away from zero and infinity on its support {overline{V} subset {R}^n}, which is strongly c-convex with respect to U', and the transportation cost c satisfies the {{A3}_w} condition of Trudinger and Wang (Ann Sc Norm Super Pisa Cl Sci 5, 8(1):143-174, 2009), we deduce the local Hölder continuity and injectivity of the optimal map inside U' (so that the associated potential u belongs to {C^{1,α}_{loc}(U')}). Here the exponent α > 0 depends only on the dimension and the bounds on the densities, but not on c. Our result provides a crucial step in the low/interior regularity setting: in a sequel ( Figalli et al., J Eur Math Soc (JEMS), 1131-1166, 2013), we use it to establish regularity of optimal maps with respect to the Riemannian distance squared on arbitrary products of spheres. Three key tools are introduced in the present paper. Namely, we first find a transformation that under {{A3}_w} makes c-convex functions level-set convex (as was also obtained independently from us by Liu (Calc Var Partial Diff Eq 34:435-451, 2009)). We then derive new Alexandrov type estimates for the level-set convex c-convex functions, and a topological lemma showing that optimal maps do not mix the interior with the boundary. This topological lemma, which does not require {{A3}_w}, is needed by Figalli and Loeper (Calc Var Partial Diff Eq 35:537-550, 2009) to conclude the continuity of optimal maps in two dimensions. In higher dimensions, if the densities f ± are Hölder continuous, our result permits continuous differentiability of the map inside U' (in fact, {C^{2,α}_{loc}} regularity of the associated potential) to be deduced from the work
Liang, X B; Si, J
2001-01-01
This paper investigates the existence, uniqueness, and global exponential stability (GES) of the equilibrium point for a large class of neural networks with globally Lipschitz continuous activations including the widely used sigmoidal activations and the piecewise linear activations. The provided sufficient condition for GES is mild and some conditions easily examined in practice are also presented. The GES of neural networks in the case of locally Lipschitz continuous activations is also obtained under an appropriate condition. The analysis results given in the paper extend substantially the existing relevant stability results in the literature, and therefore expand significantly the application range of neural networks in solving optimization problems. As a demonstration, we apply the obtained analysis results to the design of a recurrent neural network (RNN) for solving the linear variational inequality problem (VIP) defined on any nonempty and closed box set, which includes the box constrained quadratic programming and the linear complementarity problem as the special cases. It can be inferred that the linear VIP has a unique solution for the class of Lyapunov diagonally stable matrices, and that the synthesized RNN is globally exponentially convergent to the unique solution. Some illustrative simulation examples are also given.
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.
Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.
Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting
2017-06-01
Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Tan, Yong; Tan, Mingjia
2009-11-01
This paper investigates the global asymptotic stability of equilibrium for a class of continuous-time neural networks with delays. Based on suitable Lyapunov functionals and the homeomorphism theory, some sufficient conditions for the existence and uniqueness of the equilibrium point are derived. These results extend the previously works without assuming boundedness and Lipschitz conditions of the activation functions and any symmetry of interconnections. A numerical example is also given to show the improvements of the paper.
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.
Optimal continuous variable quantum teleportation protocol for realistic settings
NASA Astrophysics Data System (ADS)
Luiz, F. S.; Rigolin, Gustavo
2015-03-01
We show the optimal setup that allows Alice to teleport coherent states | α > to Bob giving the greatest fidelity (efficiency) when one takes into account two realistic assumptions. The first one is the fact that in any actual implementation of the continuous variable teleportation protocol (CVTP) Alice and Bob necessarily share non-maximally entangled states (two-mode finitely squeezed states). The second one assumes that Alice's pool of possible coherent states to be teleported to Bob does not cover the whole complex plane (| α | < ∞). The optimal strategy is achieved by tuning three parameters in the original CVTP, namely, Alice's beam splitter transmittance and Bob's displacements in position and momentum implemented on the teleported state. These slight changes in the protocol are currently easy to be implemented and, as we show, give considerable gain in performance for a variety of possible pool of input states with Alice.
Optimal scheduling of multiple sensors in continuous time.
Wu, Xiang; Zhang, Kanjian; Sun, Changyin
2014-05-01
This paper considers an optimal sensor scheduling problem in continuous time. In order to make the model more close to the practical problems, suppose that the following conditions are satisfied: only one sensor may be active at any one time; an admissible sensor schedule is a piecewise constant function with a finite number of switches; and each sensor either doesn't operate or operates for a minimum non-negligible amount of time. However, the switching times are unknown, and the feasible region isn't connected. Thus, it's difficult to solve the problem by conventional optimization techniques. To overcome this difficulty, by combining a binary relaxation, a time-scaling transformation and an exact penalty function, an algorithm is developed for solving this problem. Numerical results show that the algorithm is effective. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
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 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.
Fast and Efficient Stochastic Optimization for Analytic Continuation
Bao, Feng; Zhang, Guannan; Webster, Clayton G; Tang, Yanfei; Scarola, Vito; Summers, Michael Stuart; Maier, Thomas A
2016-09-28
In this analytic continuation of imaginary-time quantum Monte Carlo data to extract real-frequency spectra remains a key problem in connecting theory with experiment. Here we present a fast and efficient stochastic optimization method (FESOM) as a more accessible variant of the stochastic optimization method introduced by Mishchenko et al. [Phys. Rev. B 62, 6317 (2000)], and we benchmark the resulting spectra with those obtained by the standard maximum entropy method for three representative test cases, including data taken from studies of the two-dimensional Hubbard model. Genearally, we find that our FESOM approach yields spectra similar to the maximum entropy results. In particular, while the maximum entropy method yields superior results when the quality of the data is strong, we find that FESOM is able to resolve fine structure with more detail when the quality of the data is poor. In addition, because of its stochastic nature, the method provides detailed information on the frequency-dependent uncertainty of the resulting spectra, while the maximum entropy method does so only for the spectral weight integrated over a finite frequency region. Therefore, we believe that this variant of the stochastic optimization approach provides a viable alternative to the routinely used maximum entropy method, especially for data of poor quality.
Fast and Efficient Stochastic Optimization for Analytic Continuation
Bao, Feng; Zhang, Guannan; Webster, Clayton G; Tang, Yanfei; Scarola, Vito; Summers, Michael Stuart; Maier, Thomas A
2016-09-28
In this analytic continuation of imaginary-time quantum Monte Carlo data to extract real-frequency spectra remains a key problem in connecting theory with experiment. Here we present a fast and efficient stochastic optimization method (FESOM) as a more accessible variant of the stochastic optimization method introduced by Mishchenko et al. [Phys. Rev. B 62, 6317 (2000)], and we benchmark the resulting spectra with those obtained by the standard maximum entropy method for three representative test cases, including data taken from studies of the two-dimensional Hubbard model. Genearally, we find that our FESOM approach yields spectra similar to the maximum entropy results. In particular, while the maximum entropy method yields superior results when the quality of the data is strong, we find that FESOM is able to resolve fine structure with more detail when the quality of the data is poor. In addition, because of its stochastic nature, the method provides detailed information on the frequency-dependent uncertainty of the resulting spectra, while the maximum entropy method does so only for the spectral weight integrated over a finite frequency region. Therefore, we believe that this variant of the stochastic optimization approach provides a viable alternative to the routinely used maximum entropy method, especially for data of poor quality.
Fast and efficient stochastic optimization for analytic continuation
NASA Astrophysics Data System (ADS)
Bao, F.; Tang, Y.; Summers, M.; Zhang, G.; Webster, C.; Scarola, V.; Maier, T. A.
2016-09-01
The analytic continuation of imaginary-time quantum Monte Carlo data to extract real-frequency spectra remains a key problem in connecting theory with experiment. Here we present a fast and efficient stochastic optimization method (FESOM) as a more accessible variant of the stochastic optimization method introduced by Mishchenko et al. [Phys. Rev. B 62, 6317 (2000), 10.1103/PhysRevB.62.6317], and we benchmark the resulting spectra with those obtained by the standard maximum entropy method for three representative test cases, including data taken from studies of the two-dimensional Hubbard model. We generally find that our FESOM approach yields spectra similar to the maximum entropy results. In particular, while the maximum entropy method yields superior results when the quality of the data is strong, we find that FESOM is able to resolve fine structure with more detail when the quality of the data is poor. In addition, because of its stochastic nature, the method provides detailed information on the frequency-dependent uncertainty of the resulting spectra, while the maximum entropy method does so only for the spectral weight integrated over a finite frequency region. We therefore believe that this variant of the stochastic optimization approach provides a viable alternative to the routinely used maximum entropy method, especially for data of poor quality.
Fast and Efficient Stochastic Optimization for Analytic Continuation
Bao, Feng; Zhang, Guannan; Webster, Clayton G; ...
2016-09-28
In this analytic continuation of imaginary-time quantum Monte Carlo data to extract real-frequency spectra remains a key problem in connecting theory with experiment. Here we present a fast and efficient stochastic optimization method (FESOM) as a more accessible variant of the stochastic optimization method introduced by Mishchenko et al. [Phys. Rev. B 62, 6317 (2000)], and we benchmark the resulting spectra with those obtained by the standard maximum entropy method for three representative test cases, including data taken from studies of the two-dimensional Hubbard model. Genearally, we find that our FESOM approach yields spectra similar to the maximum entropy results.more » In particular, while the maximum entropy method yields superior results when the quality of the data is strong, we find that FESOM is able to resolve fine structure with more detail when the quality of the data is poor. In addition, because of its stochastic nature, the method provides detailed information on the frequency-dependent uncertainty of the resulting spectra, while the maximum entropy method does so only for the spectral weight integrated over a finite frequency region. Therefore, we believe that this variant of the stochastic optimization approach provides a viable alternative to the routinely used maximum entropy method, especially for data of poor quality.« less
Miao, Yongfei; Su, Qinghua; Hu, Zhongbo; Xia, Xuewen
2016-01-01
For solving non-linear programming problems containing discrete and continuous variables, this article suggests two modified algorithms based on differential evolution (DE). The two proposed algorithms incorporate a novel random search strategy into DE/best/1 and DE/cur-to-best/1 respectively. Inspired by the artificial bee colony algorithm, the random search strategy overcomes the searching unbalance of DE/best/1 and DE/cur-to-best/1 by enhancing the global exploration capability of promising individuals. Two numerical experiments are given to test the two modified algorithms. Experiment 1 is conducted on the benchmark function set of CEC2005 in order to verify the effectiveness of the improved strategy. Experiment 2 is designed to optimize two mixed discrete-continuous problems to illustrate the competitiveness and the practicality of the proposed algorithms. In particular, the modified DE/cur-to-best/1 finds the new optima of two engineering optimization problems.
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.
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.
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.
Simulation and optimization of continuous extractive fermentation with recycle system
NASA Astrophysics Data System (ADS)
Widjaja, Tri; Altway, Ali; Rofiqah, Umi; Airlangga, Bramantyo
2017-05-01
Extractive fermentation is continuous fermentation method which is believed to be able to substitute conventional fermentation method (batch). The recovery system and ethanol refinery will be easier. Continuous process of fermentation will make the productivity increase although the unconverted sugar in continuous fermentation is still in high concentration. In order to make this process more efficient, the recycle process was used. Increasing recycle flow will enhance the probability of sugar to be re-fermented. However, this will make ethanol enter fermentation column. As a result, the accumulated ethanol will inhibit the growth of microorganism. This research aims to find optimum conditions of solvent to broth ratio (S:B) and recycle flow to fresh feed ratio in order to produce the best yield and productivity. This study employed optimization by Hooke Jeeves method using Matlab 7.8 software. The result indicated that optimum condition occured in S: B=2.615 and R: F=1.495 with yield = 50.2439 %.
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.
Ultrafast Quantum Process Tomography via Continuous Measurement and Convex Optimization
NASA Astrophysics Data System (ADS)
Baldwin, Charles; Riofrio, Carlos; Deutsch, Ivan
2013-03-01
Quantum process tomography (QPT) is an essential tool to diagnose the implementation of a dynamical map. However, the standard protocol is extremely resource intensive. For a Hilbert space of dimension d, it requires d2 different input preparations followed by state tomography via the estimation of the expectation values of d2 - 1 orthogonal observables. We show that when the process is nearly unitary, we can dramatically improve the efficiency and robustness of QPT through a collective continuous measurement protocol on an ensemble of identically prepared systems. Given the measurement history we obtain the process matrix via a convex program that optimizes a desired cost function. We study two estimators: least-squares and compressive sensing. Both allow rapid QPT due to the condition of complete positivity of the map; this is a powerful constraint to force the process to be physical and consistent with the data. We apply the method to a real experimental implementation, where optimal control is used to perform a unitary map on a d = 8 dimensional system of hyperfine levels in cesium atoms, and obtain the measurement record via Faraday spectroscopy of a laser probe. Supported by the NSF
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
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.
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.
Improved genetic algorithm for mixed-discrete-continuous design optimization problems
NASA Astrophysics Data System (ADS)
Lee, Kuo-Ming; Tsai, Jinn-Tsong; Liu, Tung-Kuan; Chou, Jyh-Horng
2010-10-01
An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.
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.
Analysis of optimal and near-optimal continuous-thrust transfer problems in general circular orbit
NASA Astrophysics Data System (ADS)
Kéchichian, Jean A.
2009-09-01
A pair of practical problems in optimal continuous-thrust transfer in general circular orbit is analyzed within the context of analytic averaging for rapid computations leading to near-optimal solutions. The first problem addresses the minimum-time transfer between inclined circular orbits by proposing an analytic solution based on a split-sequence strategy in which the equatorial inclination and node controls are done separately by optimally selecting the intermediate orbit size at the sequence switch point that results in the minimum-time transfer. The consideration of the equatorial inclination and node state variables besides the orbital velocity variable is needed to further account for the important J2 perturbation that precesses the orbit plane during the transfer, unlike the thrust-only case in which it is sufficient to consider the relative inclination and velocity variables thus reducing the dimensionality of the system equations. Further extensions of the split-sequence strategy with analytic J2 effect are thus possible for equal computational ease. The second problem addresses the maximization of the equatorial inclination in fixed time by adopting a particular thrust-averaging scheme that controls only the inclination and velocity variables, leaving the node at the mercy of the J2 precession, providing robust fast-converging codes that lead to efficient near-optimal solutions. Example transfers for both sets of problems are solved showing near-optimal features as far as transfer time is concerned, by directly comparing the solutions to "exact" purely numerical counterparts that rely on precision integration of the raw unaveraged system dynamics with continuously varying thrust vector orientation in three-dimensional space.
Continuous adjoint sensitivity analysis for aerodynamic and acoustic optimization
NASA Astrophysics Data System (ADS)
Ghayour, Kaveh
1999-11-01
A gradient-based shape optimization methodology based on continuous adjoint sensitivities has been developed for two-dimensional steady Euler equations on unstructured meshes and the unsteady transonic small disturbance equation. The continuous adjoint sensitivities of the Helmholtz equation for acoustic applications have also been derived and discussed. The highlights of the developments for the steady two-dimensional Euler equations are the generalization of the airfoil surface boundary condition of the adjoint system to allow a proper closure of the Lagrangian functional associated with a general cost functional and the results for an inverse problem with density as the prescribed target. Furthermore, it has been demonstrated that a transformation to the natural coordinate system, in conjunction with the reduction of the governing state equations to the control surface, results in sensitivity integrals that are only a function of the tangential derivatives of the state variables. This approach alleviates the need for directional derivative computations with components along the normal to the control surface, which can render erroneous results. With regard to the unsteady transonic small disturbance equation (UTSD), the continuous adjoint methodology has been successfully extended to unsteady flows. It has been demonstrated that for periodic airfoil oscillations leading to limit-cycle behavior, the Lagrangian functional can be only closed if the time interval of interest spans one or more periods of the flow oscillations after the limit-cycle has been attained. The steady state and limit-cycle sensitivities are then validated by comparing with the brute-force derivatives. The importance of accounting for the flow circulation sensitivity, appearing in the form of a Dirac delta in the wall boundary condition at the trailing edge, has been stressed and demonstrated. Remarkably, the cost of an unsteady adjoint solution is about 0.2 times that of a UTSD solution
Optimal Continuous Variable Quantum Teleportation with Limited Resources
NASA Astrophysics Data System (ADS)
Liuzzo-Scorpo, Pietro; Mari, Andrea; Giovannetti, Vittorio; Adesso, Gerardo
2017-09-01
Given a certain amount of entanglement available as a resource, what is the most efficient way to accomplish a quantum task? We address this question in the relevant case of continuous variable quantum teleportation protocols implemented using two-mode Gaussian states with a limited degree of entanglement and energy. We first characterize the class of single-mode phase-insensitive Gaussian channels that can be simulated via a Braunstein-Kimble protocol with nonunit gain and minimum shared entanglement, showing that infinite energy is not necessary apart from the special case of the quantum limited attenuator. We also find that apart from the identity, all phase-insensitive Gaussian channels can be simulated through a two-mode squeezed state with finite energy, albeit with a larger entanglement. We then consider the problem of teleporting single-mode coherent states with Gaussian-distributed displacement in phase space. Performing a geometrical optimization over phase-insensitive Gaussian channels, we determine the maximum average teleportation fidelity achievable with any finite entanglement and for any realistically finite variance of the input distribution.
Landsat Data Continuity Mission (LDCM) - Optimizing X-Band Usage
NASA Technical Reports Server (NTRS)
Garon, H. M.; Gal-Edd, J. S.; Dearth, K. W.; Sank, V. I.
2010-01-01
The NASA version of the low-density parity check (LDPC) 7/8-rate code, shortened to the dimensions of (8160, 7136), has been implemented as the forward error correction (FEC) schema for the Landsat Data Continuity Mission (LDCM). This is the first flight application of this code. In order to place a 440 Msps link within the 375 MHz wide X band we found it necessary to heavily bandpass filter the satellite transmitter output . Despite the significant amplitude and phase distortions that accompanied the spectral truncation, the mission required BER is maintained at < 10(exp -12) with less than 2 dB of implementation loss. We utilized a band-pass filter designed ostensibly to replicate the link distortions to demonstrate link design viability. The same filter was then used to optimize the adaptive equalizer in the receiver employed at the terminus of the downlink. The excellent results we obtained could be directly attributed to the implementation of the LDPC code and the amplitude and phase compensation provided in the receiver. Similar results were obtained with receivers from several vendors.
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 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.
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
Emergence of Global Shape Processing Continues through Adolescence
ERIC Educational Resources Information Center
Scherf, K. Suzanne; Behrmann, Marlene; Kimchi, Ruth; Luna, Beatriz
2009-01-01
The developmental trajectory of perceptual organization in humans is unclear. This study investigated perceptual grouping abilities across a wide age range (8-30 years) using a classic compound letter global/local (GL) task and a more fine-grained microgenetic prime paradigm (MPP) with both few- and many-element hierarchical displays. In the GL…
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.
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.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Froelicher, T. L.; Winton, M.; Sarmiento, J. L.
2014-12-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. These studies suggest that the cooling effect of reduction in radiative forcing due to the decrease in atmospheric CO2 is roughly balanced by the warming effect of reduction in ocean heat uptake. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. For example, global mean surface temperature may increase by 0.6°C after carbon emissions are stopped at 2°C above preindustrial. Surprisingly, the temperature increase occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake—a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales in these models. We show that ocean heat uptake, which occurs preferentially at subpolar latitudes, has a larger temperature impact per watt per square meter than the CO2 radiative forcing. In other words, the cooling effect of a high-latitude heat sink is larger than that of an equivalent tropical heat sink. The implications of our results for estimates of allowable carbon emissions required to remain below a specific global warming target will be discussed.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Frölicher, Thomas; Winton, Michael; Sarmiento, Jorge
2014-05-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. For example, global mean surface temperature may increase by 0.6°C after a carbon emissions stoppage at 2-degree. This increase occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake—a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales. Our study also reveals that equilibrium climate sensitivity estimates based on a widely used method of regressing the Earth's energy imbalance against surface temperature change are biased. Uncertainty in the magnitude of the feedback effects associated with the magnitude and geographic distribution of ocean heat uptake therefore contributes substantially to the uncertainty in allowable carbon emissions for a given multi-century warming target.
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.
Continuing Health Professional Education: Principles for Global Application.
ERIC Educational Resources Information Center
Woolf, Colin R.
1993-01-01
Offers a list of continuing health professional education principles developed by a network of 26 individuals in 14 countries that provide a broad perspective and, as a result of this consultation with individuals of varying cultural circumstances, show differences in emphasis. Proposes personal, educational, and administrative principles.…
Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury.
Aries, Marcel J H; Czosnyka, Marek; Budohoski, Karol P; Steiner, Luzius A; Lavinio, Andrea; Kolias, Angelos G; Hutchinson, Peter J; Brady, Ken M; Menon, David K; Pickard, John D; Smielewski, Peter
2012-08-01
We have sought to develop an automated methodology for the continuous updating of optimal cerebral perfusion pressure (CPPopt) for patients after severe traumatic head injury, using continuous monitoring of cerebrovascular pressure reactivity. We then validated the CPPopt algorithm by determining the association between outcome and the deviation of actual CPP from CPPopt. Retrospective analysis of prospectively collected data. Neurosciences critical care unit of a university hospital. A total of 327 traumatic head-injury patients admitted between 2003 and 2009 with continuous monitoring of arterial blood pressure and intracranial pressure. Arterial blood pressure, intracranial pressure, and CPP were continuously recorded, and pressure reactivity index was calculated online. Outcome was assessed at 6 months. An automated curve fitting method was applied to determine CPP at the minimum value for pressure reactivity index (CPPopt). A time trend of CPPopt was created using a moving 4-hr window, updated every minute. Identification of CPPopt was, on average, feasible during 55% of the whole recording period. Patient outcome correlated with the continuously updated difference between median CPP and CPPopt (chi-square=45, p<.001; outcome dichotomized into fatal and nonfatal). Mortality was associated with relative "hypoperfusion" (CPP
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.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Frölicher, Thomas Lukas; Winton, Michael; Sarmiento, Jorge Louis
2014-01-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. This occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake--a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales. Our study also reveals that equilibrium climate sensitivity estimates based on a widely used method of regressing the Earth's energy imbalance against surface temperature change are biased. Uncertainty in the magnitude of the feedback effects associated with the magnitude and geographic distribution of ocean heat uptake therefore contributes substantially to the uncertainty in allowable carbon emissions for a given multi-century warming target.
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.
Continuation methods in multiobjective optimization for combined structure control design
NASA Technical Reports Server (NTRS)
Milman, M.; Salama, M.; Scheid, R.; Bruno, R.; Gibson, J. S.
1990-01-01
A homotopy approach involving multiobjective functions is developed to outline the methods that have evolved for the combined control-structure optimization of physical systems encountered in the technology of large space structures. A method to effect a timely consideration of the control performance prior to the finalization of the structural design involves integrating the control and structural design processes into a unified design methodology that combines the two optimization problems into a single formulation. This study uses the combined optimization problem as a family of weighted structural and control costs. Connections with vector optimizations are described; an analysis of the zero-set of required conditions is made, and a numerical example is given.
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.
hp-Pseudospectral method for solving continuous-time nonlinear optimal control problems
NASA Astrophysics Data System (ADS)
Darby, Christopher L.
2011-12-01
In this dissertation, a direct hp-pseudospectral method for approximating the solution to nonlinear optimal control problems is proposed. The hp-pseudospectral method utilizes a variable number of approximating intervals and variable-degree polynomial approximations of the state within each interval. Using the hp-discretization, the continuous-time optimal control problem is transcribed to a finite-dimensional nonlinear programming problem (NLP). The differential-algebraic constraints of the optimal control problem are enforced at a finite set of collocation points, where the collocation points are either the Legendre-Gauss or Legendre-Gauss-Radau quadrature points. These sets of points are chosen because they correspond to high-accuracy Gaussian quadrature rules for approximating the integral of a function. Moreover, Runge phenomenon for high-degree Lagrange polynomial approximations to the state is avoided by using these points. The key features of the hp-method include computational sparsity associated with low-order polynomial approximations and rapid convergence rates associated with higher-degree polynomials approximations. Consequently, the hp-method is both highly accurate and computationally efficient. Two hp-adaptive algorithms are developed that demonstrate the utility of the hp-approach. The algorithms are shown to accurately approximate the solution to general continuous-time optimal control problems in a computationally efficient manner without a priori knowledge of the solution structure. The hp-algorithms are compared empirically against local (h) and global (p) collocation methods over a wide range of problems and are found to be more efficient and more accurate. The hp-pseudospectral approach developed in this research not only provides a high-accuracy approximation to the state and control of an optimal control problem, but also provides high-accuracy approximations to the costate of the optimal control problem. The costate is approximated by
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.
Global distribution and continuing spread of Aedes albopictus.
Knudsen, A B
1995-12-01
Aedes albopictus ranks second only to Ae. aegypti in importance to man as a vector of dengue and dengue haemorrhagic fever (DHF) which viruses place at risk a potential population of 2 billion people living in tropical and sub-tropical regions. Due to its predilection for breeding in a plethora of habitat within urban and suburban environs as well as peri-rural areas it is spreading rapidly where suitable breeding is available. It exhibits strain differences ranging from the cold-hardy to tropic loving, yet despite limited flight range, it has spread beyond the Orient to China, the Pacific, the Indian Ocean islands, the Americas, parts of continental Africa and into southern Europe. This has been done principally by means of transport of eggs in used tyres via rapid air and sea transport. Egg positive used tyres, when shipped, and later rehydrated by rainfall, produce adult mosquitoes within a few days rapidly infesting new areas. Although dengue and other vector-borne arboviral diseases have not been in Europe in epidemic form for many decades, travelers do not infrequently return from dengue endemic areas with dengue and other similar infections. Aedes albopictus is a potential vector of a number of arboviruses and can transmit them in a vertical or transvenereal manner in nature, thereby providing a means for their maintenance and transmission. Where Ae. albopictus newly occurs, the affected populace immediately are aware of a new daytime, nuisance biting mosquito and complaints addressed to local mosquito control authorities increase significantly. The biological characteristics of the mosquito make its spread within Europe highly probable. The paper offers several avenues to be pursued to reduce the global spread of Ae. albopictus, when examined within the context of Europe and the wider world community.
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
Globalization as Continuing Colonialism: Critical Global Citizenship Education in an Unequal World
ERIC Educational Resources Information Center
Mikander, Pia
2016-01-01
In an unequal world, education about global inequality can be seen as a controversial but necessary topic for social science to deal with. Even though the world no longer consists of colonies and colonial powers, many aspects of the global economy follow the same patterns as during colonial times, with widening gaps between the world's richest and…
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.
Optimal digital redesign of continuous-time controllers
NASA Technical Reports Server (NTRS)
Shieh, Leang S.; Zhang, Jian L.; Coleman, Norman P.
1991-01-01
This paper proposes a new optimal digital redesign technique for finding a dynamic digital control law from the available analog counterpart and simultaneously minimizing a quadratic performance index. The proposed technique can be applied to a system with a more general class of reference inputs, and the developed digital regulator can be implemented using low cost microcomputers.
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.
Optimized GPU simulation of continuous-spin glass models
NASA Astrophysics Data System (ADS)
Yavors'kii, T.; Weigel, M.
2012-08-01
We develop a highly optimized code for simulating the Edwards-Anderson Heisenberg model on graphics processing units (GPUs). Using a number of computational tricks such as tiling, data compression and appropriate memory layouts, the simulation code combining over-relaxation, heat bath and parallel tempering moves achieves a peak performance of 0.29 ns per spin update on realistic system sizes, corresponding to a more than 150 fold speed-up over a serial CPU reference implementation. The optimized implementation is used to study the spin-glass transition in a random external magnetic field to probe the existence of a de Almeida-Thouless line in the model, for which we give benchmark results.
Optimal Continuous Thrust Orbital Evasive Maneuvers from Geosynchronous Orbit
1986-12-01
control thrusters, if its warning time and orbital parameters were appropriate. A model is developed using optimal control theory and is solved numericaly...Maneuvers of a Spacecraft Relative to a Point in Circular Orbit ,’ Journal of Guidance, Control . and Dynamics. 9(l): 27-31 (January -February 1966). 10... Elliptical Orbit ," Joursal of Guidance. Control . and Drjsmakjs1 (4: 271-275 (July- August 1979). 22. Meirovitch, Leonard. Methods of Anakytical Dynamics
Continuous Control Artificial Potential Function Methods and Optimal Control
2014-03-27
and Optimal Control R. Andrew Fields, BSAE Civ, USAF Approved: //signed// 12 March 2014 Lt Col J. Agte , PhD, (Chairman) date //signed// 12 March 2014...like to thank my research advisor, Lt. Col. Jeremy Agte . Without the countless conversations and whiteboard drawings in Lt. Col. Agte’s office, this...LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 84 19a. NAME OF RESPONSIBLE PERSON Lt. Col Jeremy Agte (ENY) REPORT U
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.
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.
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.
Optimal multistage designs for randomised clinical trials with continuous outcomes
Wason, James MS; Mander, Adrian P; Thompson, Simon G
2012-01-01
Multistage designs allow considerable reductions in the expected sample size of a trial. When stopping for futility or efficacy is allowed at each stage, the expected sample size under different possible true treatment effects (δ) is of interest. The δ-minimax design is the one for which the maximum expected sample size is minimised amongst all designs that meet the types I and II error constraints. Previous work has compared a two-stage δ-minimax design with other optimal two-stage designs. Applying the δ-minimax design to designs with more than two stages was not previously considered because of computational issues. In this paper, we identify the δ-minimax designs with more than two stages through use of a novel application of simulated annealing. We compare them with other optimal multistage designs and the triangular design. We show that, as for two-stage designs, the δ-minimax design has good expected sample size properties across a broad range of treatment effects but generally has a higher maximum sample size. To overcome this drawback, we use the concept of admissible designs to find trials which balance the maximum expected sample size and maximum sample size. We show that such designs have good expected sample size properties and a reasonable maximum sample size and, thus, are very appealing for use in clinical trials. Copyright © 2011 John Wiley & Sons, Ltd. PMID:22139822
Optimal multistage designs for randomised clinical trials with continuous outcomes.
Wason, James M S; Mander, Adrian P; Thompson, Simon G
2012-02-20
Multistage designs allow considerable reductions in the expected sample size of a trial. When stopping for futility or efficacy is allowed at each stage, the expected sample size under different possible true treatment effects (δ) is of interest. The δ-minimax design is the one for which the maximum expected sample size is minimised amongst all designs that meet the types I and II error constraints. Previous work has compared a two-stage δ-minimax design with other optimal two-stage designs. Applying the δ-minimax design to designs with more than two stages was not previously considered because of computational issues. In this paper, we identify the δ-minimax designs with more than two stages through use of a novel application of simulated annealing. We compare them with other optimal multistage designs and the triangular design. We show that, as for two-stage designs, the δ-minimax design has good expected sample size properties across a broad range of treatment effects but generally has a higher maximum sample size. To overcome this drawback, we use the concept of admissible designs to find trials which balance the maximum expected sample size and maximum sample size. We show that such designs have good expected sample size properties and a reasonable maximum sample size and, thus, are very appealing for use in clinical trials.
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.
NASA Astrophysics Data System (ADS)
Bhardwaj, Piyush; Dasgupta, Bhaskar; Deb, Kalyanmoy
2014-07-01
In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.
Design optimization of continuous partially prestressed concrete beams
NASA Astrophysics Data System (ADS)
Al-Gahtani, A. S.; Al-Saadoun, S. S.; Abul-Feilat, E. A.
1995-04-01
An effective formulation for optimum design of two-span continuous partially prestressed concrete beams is described in this paper. Variable prestressing forces along the tendon profile, which may be jacked from one end or both ends with flexibility in the overlapping range and location, and the induced secondary effects are considered. The imposed constraints are on flexural stresses, ultimate flexural strength, cracking moment, ultimate shear strength, reinforcement limits cross-section dimensions, and cable profile geometries. These constraints are formulated in accordance with ACI (American Concrete Institute) code provisions. The capabilities of the program to solve several engineering problems are presented.
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.
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
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
Robust model predictive control for optimal continuous drug administration.
Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos
2014-10-01
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Collision-free nonuniform dynamics within continuous optimal velocity models
NASA Astrophysics Data System (ADS)
Tordeux, Antoine; Seyfried, Armin
2014-10-01
Optimal velocity (OV) car-following models give with few parameters stable stop-and -go waves propagating like in empirical data. Unfortunately, classical OV models locally oscillate with vehicles colliding and moving backward. In order to solve this problem, the models have to be completed with additional parameters. This leads to an increase of the complexity. In this paper, a new OV model with no additional parameters is defined. For any value of the inputs, the model is intrinsically asymmetric and collision-free. This is achieved by using a first-order ordinary model with two predecessors in interaction, instead of the usual inertial delayed first-order or second-order models coupled with the predecessor. The model has stable uniform solutions as well as various stable stop-and -go patterns with bimodal distribution of the speed. As observable in real data, the modal speed values in congested states are not restricted to the free flow speed and zero. They depend on the form of the OV function. Properties of linear, concave, convex, or sigmoid speed functions are explored with no limitation due to collisions.
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
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.
Environmental optimization of continuous flow ozonation for urban wastewater reclamation.
Rodríguez, Antonio; Muñoz, Iván; Perdigón-Melón, José A; Carbajo, José B; Martínez, María J; Fernández-Alba, Amadeo R; García-Calvo, Eloy; Rosal, Roberto
2012-10-15
Wastewater samples from the secondary clarifier of two treatment plants were spiked in the microgram-to-tens-of-microgram per liter range with diuron (herbicide), ibuprofen and diclofenac (anti-inflammatory drugs), sulfamethoxazole and erythromycin (antibiotics), bezafibrate and gemfibrozil (lipid regulators), atenolol (β-blocker), carbamazepine (anti-epileptic), hydrochlorothiazide (diuretic), caffeine (stimulant) and N-acetyl-4-amino-antipiryne, a metabolite of the antipyretic drug dypirone. They were subsequently ozonated in continuous flow using 1.2L lab-scale bubble columns. The concentration of all spiking compounds was monitored in the outlet stream. The effects of varying ozone input, expressed as energy per unit volume, and water flow rate, and of using single or double column were studied in relation to the efficiency of ozone usage and the ratio of pollutant depletion. The ozone dosage required to treat both wastewaters with pollutant depletion of >90% was in the 5.5-8.5 mg/L range with ozone efficiencies greater than 80% depending on the type of wastewater and the operating conditions. This represented 100-200 mol of ozone transferred per mole of pollutant removed. Direct and indirect environmental impacts of ozonation were assessed according to Life Cycle Assessment, a technique that helped identify the most effective treatments in terms of potential toxicity reduction, as well as of toxicity reduction per unit mass of greenhouse-gas emissions, which were used as an indicator of environmental efficiency. A trade-off between environmental effectiveness (toxicity reduction) and greenhouse-gas emissions was observed since maximizing toxicity removal led to higher greenhouse-gas emissions, due to the latter's relatively high ozone requirements. Also, there is an environmental trade-off between effectiveness and efficiency. Our results indicate that an efficient use of ozone was not compatible with a full pollutant removal.
Josiński, Henryk; Kostrzewa, Daniel; Michalczuk, Agnieszka; Switoński, Adam
2014-01-01
This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature.
Josiński, Henryk; Michalczuk, Agnieszka; Świtoński, Adam
2014-01-01
This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature. PMID:24955420
Badilini, Fabio; Vaglio, Martino; Sarapa, Nenad
2009-01-01
Continuous 12-lead ECG monitoring (Holter) in early-phase pharmaceutical studies is today widely used as an ideal platform to extract discrete ECGs for analysis. The extraction process is typically performed manually by trained readers using commercial Holter processing systems. Antares, a novel method for automatic 12-lead extraction from continuous Holter recordings applying minimal noise criteria and heart-rate stability conditions is presented. A set of 12-lead Holter recordings from healthy subjects administered with sotalol is used to compare ECG extractions at fixed time points with ECG extractions generated by Antares optimizing noise and heart rate inside 5 minute windows centered around each expected time point of interest. Global, low- and high-frequency noise content of extracted ECGs was significantly reduced via optimized approach by Antares. Heart rate was also slightly reduced (from 69 +/- 13 to 64 +/- 13 bpm, P < 0.05). Similarly, the corrected QT interval from optimized extractions was significantly reduced (QTcB from 414 +/- 32 to 402 +/- 30 ms, P < 0.05). Using only baseline data, and after adjusting for intersubject variability, the standard deviation (SD) of QT intervals was highly reduced with optimized extraction (SD of QTcF from 11 +/- 8 to 7 +/- 2 ms, P < 0.05). Extraction of discrete 12-lead ECG strips from continuous Holter generates less noisy and more stable ECGs leading to more robust QTc data, thereby potentially facilitating the assessment of ECG effects on clinical trials.
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.
Equivalence between entanglement and the optimal fidelity of continuous variable teleportation.
Adesso, Gerardo; Illuminati, Fabrizio
2005-10-07
We devise the optimal form of Gaussian resource states enabling continuous-variable teleportation with maximal fidelity. We show that a nonclassical optimal fidelity of N-user teleportation networks is necessary and sufficient for N-party entangled Gaussian resources, yielding an estimator of multipartite entanglement. The entanglement of teleportation is equivalent to the entanglement of formation in a two-user protocol, and to the localizable entanglement in a multiuser one. Finally, we show that the continuous-variable tangle, quantifying entanglement sharing in three-mode Gaussian states, is defined operationally in terms of the optimal fidelity of a tripartite teleportation network.
Optimal control of nonlinear continuous-time systems in strict-feedback form.
Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani
2015-10-01
This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.
Liu, Liqiang; Dai, Yuntao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
The global dynamics of a discrete juvenile-adult model with continuous and seasonal reproduction.
Ackleh, Azmy S; Chiquet, Ross A
2009-03-01
A general discrete juvenile-adult population model with time-dependent birth rate and nonlinear survivorship rates is studied. When breeding is continuous, it is shown that the model has a unique globally asymptotically stable positive equilibrium provided the net reproductive number is larger than one. If it is smaller than one, then the extinction equilibrium is globally asymptotically stable. When breeding is seasonal, it is shown that there exists a unique globally asymptotically stable periodic solution provided the net reproductive number is larger than one. When this value is less than one, the population goes to extinction. Conditions on the birth rate where the population with seasonal breeding survives while the population with continuous breeding becomes extinct are provided.
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
Mokeddem, Diab; Khellaf, Abdelhafid
2014-06-01
The key feature of this paper is the optimization of an industrial process for continuous production of lactic acid. For this, a two-stage fermentor process integrated with cell recycling has been mathematically modeled and optimized for overall productivity, conversion, and yield simultaneously. Non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the constrained multi-objective optimization problem as it is capable of finding multiple Pareto-optimal solutions in a single run, thereby avoiding the need to use a single-objective optimization several times. Compared with traditional methods, NSGA-II could find most of the solutions in the true Pareto-front and its simulation is also very direct and convenient. The effects of operating variables on the optimal solutions are discussed in detail. It was observed that we can make higher profit with an acceptable compromise in a two-stage system with greater efficiency.
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
Continuation of the NVAP Global Water Vapor Data Sets for Pathfinder Science Analysis
NASA Technical Reports Server (NTRS)
VonderHaar, Thomas H.; Engelen, Richard J.; Forsythe, John M.; Randel, David L.; Ruston, Benjamin C.; Woo, Shannon; Dodge, James (Technical Monitor)
2001-01-01
This annual report covers August 2000 - August 2001 under NASA contract NASW-0032, entitled "Continuation of the NVAP (NASA's Water Vapor Project) Global Water Vapor Data Sets for Pathfinder Science Analysis". NASA has created a list of Earth Science Research Questions which are outlined by Asrar, et al. Particularly relevant to NVAP are the following questions: (a) How are global precipitation, evaporation, and the cycling of water changing? (b) What trends in atmospheric constituents and solar radiation are driving global climate? (c) How well can long-term climatic trends be assessed or predicted? Water vapor is a key greenhouse gas, and an understanding of its behavior is essential in global climate studies. Therefore, NVAP plays a key role in addressing the above climate questions by creating a long-term global water vapor dataset and by updating the dataset with recent advances in satellite instrumentation. The NVAP dataset produced from 1988-1998 has found wide use in the scientific community. Studies of interannual variability are particularly important. A recent paper by Simpson, et al. that examined the NVAP dataset in detail has shown that its relative accuracy is sufficient for the variability studies that contribute toward meeting NASA's goals. In the past year, we have made steady progress towards continuing production of this high-quality dataset as well as performing our own investigations of the data. This report summarizes the past year's work on production of the NVAP dataset and presents results of analyses we have performed in the past year.
NASA Astrophysics Data System (ADS)
Ye, Hong-Ling; Wang, Wei-Wei; Chen, Ning; Sui, Yun-Kang
2017-03-01
The purpose of the present work is to study the buckling problem with plate/shell topology optimization of orthotropic material. A model of buckling topology optimization is established based on the independent, continuous, and mapping method, which considers structural mass as objective and buckling critical loads as constraints. Firstly, composite exponential function (CEF) and power function (PF) as filter functions are introduced to recognize the element mass, the element stiffness matrix, and the element geometric stiffness matrix. The filter functions of the orthotropic material stiffness are deduced. Then these filter functions are put into buckling topology optimization of a differential equation to analyze the design sensitivity. Furthermore, the buckling constraints are approximately expressed as explicit functions with respect to the design variables based on the first-order Taylor expansion. The objective function is standardized based on the second-order Taylor expansion. Therefore, the optimization model is translated into a quadratic program. Finally, the dual sequence quadratic programming (DSQP) algorithm and the global convergence method of moving asymptotes algorithm with two different filter functions (CEF and PF) are applied to solve the optimal model. Three numerical results show that DSQP&CEF has the best performance in the view of structural mass and discretion.
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.
Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System
NASA Astrophysics Data System (ADS)
Bendjeghaba, Omar
2014-01-01
This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
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.
CFD optimization of continuous stirred-tank (CSTR) reactor for biohydrogen production.
Ding, Jie; Wang, Xu; Zhou, Xue-Fei; Ren, Nan-Qi; Guo, Wan-Qian
2010-09-01
There has been little work on the optimal configuration of biohydrogen production reactors. This paper describes three-dimensional computational fluid dynamics (CFD) simulations of gas-liquid flow in a laboratory-scale continuous stirred-tank reactor used for biohydrogen production. To evaluate the role of hydrodynamics in reactor design and optimize the reactor configuration, an optimized impeller design has been constructed and validated with CFD simulations of the normal and optimized impeller over a range of speeds and the numerical results were also validated by examination of residence time distribution. By integrating the CFD simulation with an ethanol-type fermentation process experiment, it was shown that impellers with different type and speed generated different flow patterns, and hence offered different efficiencies for biohydrogen production. The hydrodynamic behavior of the optimized impeller at speeds between 50 and 70 rev/min is most suited for economical biohydrogen production.
Geoscience Australia Continuous Global Positioning System (CGPS) Station Field Campaign Report
Ruddick, R.; Twilley, B.
2016-03-01
This station formed part of the Australian Regional GPS Network (ARGN) and South Pacific Regional GPS Network (SPRGN), which is a network of continuous GPS stations operating within Australia and its Territories (including Antarctica) and the Pacific. These networks support a number of different science applications including maintenance of the Geospatial Reference Frame, both national and international, continental and tectonic plate motions, sea level rise, and global warming.
Time dependence of breakdown in a global fiber-bundle model with continuous damage
NASA Astrophysics Data System (ADS)
Moral, L.; Moreno, Y.; Gómez, J. B.; Pacheco, A. F.
2001-06-01
A time-dependent global fiber-bundle model of fracture with continuous damage is formulated in terms of a set of coupled nonlinear differential equations. A first integral of this set is analytically obtained. The time evolution of the system is studied by applying a discrete probabilistic method. Several results are discussed emphasizing their differences with the standard time-dependent model. The results obtained show that with this simple model a variety of experimental observations can be qualitatively reproduced.
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
Albano Farias, L.; Stephany, J.
2010-12-15
We analyze the statistics of observables in continuous-variable (CV) quantum teleportation in the formalism of the characteristic function. We derive expressions for average values of output-state observables, in particular, cumulants which are additive in terms of the input state and the resource of teleportation. Working with a general class of teleportation resources, the squeezed-bell-like states, which may be optimized in a free parameter for better teleportation performance, we discuss the relation between resources optimal for fidelity and those optimal for different observable averages. We obtain the values of the free parameter of the squeezed-bell-like states which optimize the central momenta and cumulants up to fourth order. For the cumulants the distortion between in and out states due to teleportation depends only on the resource. We obtain optimal parameters {Delta}{sub (2)}{sup opt} and {Delta}{sub (4)}{sup opt} for the second- and fourth-order cumulants, which do not depend on the squeezing of the resource. The second-order central momenta, which are equal to the second-order cumulants, and the photon number average are also optimized by the resource with {Delta}{sub (2)}{sup opt}. We show that the optimal fidelity resource, which has been found previously to depend on the characteristics of input, approaches for high squeezing to the resource that optimizes the second-order momenta. A similar behavior is obtained for the resource that optimizes the photon statistics, which is treated here using the sum of the squared differences in photon probabilities of input versus output states as the distortion measure. This is interpreted naturally to mean that the distortions associated with second-order momenta dominate the behavior of the output state for large squeezing of the resource. Optimal fidelity resources and optimal photon statistics resources are compared, and it is shown that for mixtures of Fock states both resources are equivalent.
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.
Development of a new adaptive ordinal approach to continuous-variable probabilistic optimization.
Romero, Vicente JosÔe; Chen, Chun-Hung (George Mason University, Fairfax, VA)
2006-11-01
A very general and robust approach to solving continuous-variable optimization problems involving uncertainty in the objective function is through the use of ordinal optimization. At each step in the optimization problem, improvement is based only on a relative ranking of the uncertainty effects on local design alternatives, rather than on precise quantification of the effects. One simply asks ''Is that alternative better or worse than this one?'' -not ''HOW MUCH better or worse is that alternative to this one?'' The answer to the latter question requires precise characterization of the uncertainty--with the corresponding sampling/integration expense for precise resolution. However, in this report we demonstrate correct decision-making in a continuous-variable probabilistic optimization problem despite extreme vagueness in the statistical characterization of the design options. We present a new adaptive ordinal method for probabilistic optimization in which the trade-off between computational expense and vagueness in the uncertainty characterization can be conveniently managed in various phases of the optimization problem to make cost-effective stepping decisions in the design space. Spatial correlation of uncertainty in the continuous-variable design space is exploited to dramatically increase method efficiency. Under many circumstances the method appears to have favorable robustness and cost-scaling properties relative to other probabilistic optimization methods, and uniquely has mechanisms for quantifying and controlling error likelihood in design-space stepping decisions. The method is asymptotically convergent to the true probabilistic optimum, so could be useful as a reference standard against which the efficiency and robustness of other methods can be compared--analogous to the role that Monte Carlo simulation plays in uncertainty propagation.
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.
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.
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.
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)
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.
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.
Dynamic characteristics of a continuous optimization method based on fictitious play theory
NASA Astrophysics Data System (ADS)
Lee, Chang-Yong
2017-05-01
In this paper, we propose a continuous optimization algorithm based on fictitious play theory and investigate the dynamic characteristics of the proposed algorithm. Fictitious play is a model for a learning rule in evolutionary game theory, and it can be used as an optimization method when all players have an identical utility function. In order to apply fictitious play to a continuous optimization algorithm, we consider two methods, equal width and equal frequency, of discretizing continuous values into a finite set of a player's strategies. The equal-frequency method turns out to outperform the equal-width method in terms of minimizing inseparable functions. To understand the mechanism of the equal-frequency method, we investigate two important quantities, the mixed strategy and the best response, in the algorithm from the statistical physics viewpoint. We find that the dynamics of the mixed strategies can be described as a 1/ f noise. In addition, we adopt the set of best responses as the probability measure and find that the probability distribution of the set can be best characterized by a multifractal; moreover, the support of the measure has a fractal dimension. The dynamics of the proposed algorithm with equal-frequency discretization contains a complex and rich structure that can be related to the optimization mechanism.
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.
Simulation of an Ant Colony Optimization Technique in Continuous Space-Time
NASA Astrophysics Data System (ADS)
Vlachos, D. S.
2008-11-01
The ant colony optimization system is an algorithm inspired by the ants' foraging behavior. The good results obtained by this system on academic problems has made it appealing for applications in industrial settings, one of the current hot topics of the method is the application in continuous problems. In this work, a modified model is presented which is based on autonomous agents, the ants, which behave like the ants in the ant colony system. These agents communicate by the biological inspired pheromone mechanism in order to find sources of food which located near their nest (optimal solutions).
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.
Global prediction of continuous hydrocarbon accumulations in self-sourced reservoirs
Eoff, Jennifer D.
2012-01-01
This report was first presented as an abstract in poster format at the American Association of Petroleum Geologists (AAPG) 2012 Annual Convention and Exhibition, April 22-25, Long Beach, Calif., as Search and Discovery Article no. 90142. Shale resource plays occur in predictable tectonic settings within similar orders of magnitude of eustatic events. A conceptual model for predicting the presence of resource-quality shales is essential for evaluating components of continuous petroleum systems. Basin geometry often distinguishes self-sourced resource plays from conventional plays. Intracratonic or intrashelf foreland basins at active margins are the predominant depositional settings among those explored for the development of self-sourced continuous accumulations, whereas source rocks associated with conventional accumulations typically were deposited in rifted passive margin settings (or other cratonic environments). Generally, the former are associated with the assembly of supercontinents, and the latter often resulted during or subsequent to the breakup of landmasses. Spreading rates, climate, and eustasy are influenced by these global tectonic events, such that deposition of self-sourced reservoirs occurred during periods characterized by rapid plate reconfiguration, predominantly greenhouse climate conditions, and in areas adjacent to extensive carbonate sedimentation. Combined tectonic histories, eustatic curves, and paleogeographic reconstructions may be useful in global predictions of organic-rich shale accumulations suitable for continuous resource development. Accumulation of marine organic material is attributed to upwellings that enhance productivity and oxygen-minimum bottom waters that prevent destruction of organic matter. The accumulation of potential self-sourced resources can be attributed to slow sedimentation rates in rapidly subsiding (incipient, flexural) foreland basins, while flooding of adjacent carbonate platforms and other cratonic highs
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
NASA Astrophysics Data System (ADS)
Mulder, T. E.; Baatsen, M. L. J.; Wubs, F. W.; Dijkstra, H. A.
2017-07-01
In the field of paleoceanographic modeling, the different positioning of Earth's continental configurations is often a major challenge for obtaining equilibrium ocean flow solutions. In this paper, we introduce numerical parameter continuation techniques to compute equilibrium solutions of ocean flows in the geological past, where we change the continental geometry and allow the flow to deform using a homotopy parameter. The methods are illustrated by computing equilibrium three-dimensional global ocean circulation patterns over the last 65 Ma under a highly idealized atmospheric forcing. These results already show interesting major transitions in ocean circulation patterns due to changes in ocean gateways, that may have been relevant for Cenozoic climate transitions. In addition, the techniques are shown to be computationally efficient compared to the established continuation spin-up approach.
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.
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 Technical Reports Server (NTRS)
Rakowska, Joanna
1992-01-01
The paper describes the conditions for continuation of the efficient curve for bi-objective control-structure optimization of a ten-bar truss with two collocated sensors and actuators. The curve has been obtained with an active set algorithm using a homotopy method. The curve is discontinuous. A general stability theory has been implemented to determine sufficient conditions for the persistence of minima, and bifurcation theory has been used to characterize the possible points of discontinuity of the path.
NASA Astrophysics Data System (ADS)
Yang, Xiong; Liu, Derong; Wang, Ding
2014-03-01
In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.
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
Optimization of continuous and intermittent microwave extraction of pectin from banana peels.
Swamy, Gabriela John; Muthukumarappan, Kasiviswanathan
2017-04-01
Continuous and intermittent microwave-assisted extractions were used to extract pectin from banana peels. Extraction parameters which were employed in the continuous process were microwave power (300-900W), time (100-300s), pH (1-3) and in the intermittent process were microwave power (300-900W), pulse ratio (0.5-1), pH (1-3). The independent factors were optimized with the Box-Behnken response surface design (BBD) (three factor three level) with the desirability function methodology. Results indicate that the independent factors have substantial effect on the pectin yield. Optimized solutions for highest pectin yield (2.18%) from banana peels were obtained with microwave power of 900W, time 100s and pH 3.00 in the continuous method while the intermittent process yielded the highest pectin content (2.58%) at microwave power of 900W, pulse ratio of 0.5 and pH of 3.00. The optimized conditions were validated and close agreement was observed with the validation experiment and predicted value.
NASA Astrophysics Data System (ADS)
Ghorbani, Mehrdad; Assadian, Nima
2013-12-01
In this study the gravitational perturbations of the Sun and other planets are modeled on the dynamics near the Earth-Moon Lagrange points and optimal continuous and discrete station-keeping maneuvers are found to maintain spacecraft about these points. The most critical perturbation effect near the L1 and L2 Lagrange points of the Earth-Moon is the ellipticity of the Moon's orbit and the Sun's gravity, respectively. These perturbations deviate the spacecraft from its nominal orbit and have been modeled through a restricted five-body problem (R5BP) formulation compatible with circular restricted three-body problem (CR3BP). The continuous control or impulsive maneuvers can compensate the deviation and keep the spacecraft on the closed orbit about the Lagrange point. The continuous control has been computed using linear quadratic regulator (LQR) and is compared with nonlinear programming (NP). The multiple shooting (MS) has been used for the computation of impulsive maneuvers to keep the trajectory closed and subsequently an optimized MS (OMS) method and multiple impulses optimization (MIO) method have been introduced, which minimize the summation of multiple impulses. In these two methods the spacecraft is allowed to deviate from the nominal orbit; however, the spacecraft trajectory should close itself. In this manner, some closed or nearly closed trajectories around the Earth-Moon Lagrange points are found that need almost zero station-keeping maneuver.
A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity.
Quan, Quan; Cai, Kai-Yuan
2016-02-01
In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.
Wagner, Caroline S.; Park, Han Woo; Leydesdorff, Loet
2015-01-01
Global collaboration continues to grow as a share of all scientific cooperation, measured as coauthorships of peer-reviewed, published papers. The percent of all scientific papers that are internationally coauthored has more than doubled in 20 years, and they account for all the growth in output among the scientifically advanced countries. Emerging countries, particularly China, have increased their participation in global science, in part by doubling their spending on R&D; they are increasingly likely to appear as partners on internationally coauthored scientific papers. Given the growth of connections at the international level, it is helpful to examine the phenomenon as a communications network and to consider the network as a new organization on the world stage that adds to and complements national systems. When examined as interconnections across the globe over two decades, a global network has grown denser but not more clustered, meaning there are many more connections but they are not grouping into exclusive ‘cliques’. This suggests that power relationships are not reproducing those of the political system. The network has features an open system, attracting productive scientists to participate in international projects. National governments could gain efficiencies and influence by developing policies and strategies designed to maximize network benefits—a model different from those designed for national systems. PMID:26196296
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.
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)
Petronevich, V. V.
2016-10-01
The paper observes the issues related to the increase of efficiency and information content of experimental research in transonic wind tunnels (WT). In particular, questions of optimizing the WT Data Acquisition and Control Systems (DACS) to provide the continuous mode test method are discussed. The problem of Mach number (M number) stabilization in the test section of the large transonic compressor-type wind tunnels at subsonic flow conditions with continuous change of the aircraft model angle of attack is observed on the example of T-128 wind tunnel. To minimize the signals distortion in T-128 DACS measurement channels the optimal MGCplus filter settings of the data acquisition system used in T-128 wind tunnel to measure loads were experimentally determined. As a result of the tests performed a good agreement of the results of balance measurements for pitch/pause and continuous test modes was obtained. Carrying out balance tests for pitch/pause and continuous test methods was provided by the regular data acquisition and control system of T-128 wind tunnel with unified software package POTOK. The architecture and functional abilities of POTOK software package are observed.
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.
NASA Astrophysics Data System (ADS)
Vaziri Yazdi Pin, Mohammad
Electric power distribution systems are the last high voltage link in the chain of production, transport, and delivery of the electric energy, the fundamental goals of which are to supply the users' demand safely, reliably, and economically. The number circuit miles traversed by distribution feeders in the form of visible overhead or imbedded underground lines, far exceed those of all other bulk transport circuitry in the transmission system. Development and expansion of the distribution systems, similar to other systems, is directly proportional to the growth in demand and requires careful planning. While growth of electric demand has recently slowed through efforts in the area of energy management, the need for a continued expansion seems inevitable for the near future. Distribution system and expansions are also independent of current issues facing both the suppliers and the consumers of electrical energy. For example, deregulation, as an attempt to promote competition by giving more choices to the consumers, while it will impact the suppliers' planning strategies, it cannot limit the demand growth or the system expansion in the global sense. Curiously, despite presence of technological advancements and a 40-year history of contributions in the area, many of the major utilities still relay on experience and resort to rudimentary techniques when planning expansions. A comprehensive literature review of the contributions and careful analyses of the proposed algorithms for distribution expansion, confirmed that the problem is a complex, multistage and multiobjective problem for which a practical solution remains to be developed. In this research, based on the 15-year experience of a utility engineer, the practical expansion problem has been clearly defined and the existing deficiencies in the previous work identified and analyzed. The expansion problem has been formulated as a multistage planning problem in line with a natural course of development and industry
Optimal Compensation with Hidden Action and Lump-Sum Payment in a Continuous-Time Model
Cvitanic, Jaksa Wan, Xuhu Zhang Jianfeng
2009-02-15
We consider a problem of finding optimal contracts in continuous time, when the agent's actions are unobservable by the principal, who pays the agent with a one-time payoff at the end of the contract. We fully solve the case of quadratic cost and separable utility, for general utility functions. The optimal contract is, in general, a nonlinear function of the final outcome only, while in the previously solved cases, for exponential and linear utility functions, the optimal contract is linear in the final output value. In a specific example we compute, the first-best principal's utility is infinite, while it becomes finite with hidden action, which is increasing in value of the output. In the second part of the paper we formulate a general mathematical theory for the problem. We apply the stochastic maximum principle to give necessary conditions for optimal contracts. Sufficient conditions are hard to establish, but we suggest a way to check sufficiency using non-convex optimization.
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.
Cao, Daliang; Earl, Matthew A; Luan, Shuang; Shepard, David M
2006-04-01
A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases were selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle3 treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.
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.
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
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.
Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani
2017-03-01
This paper presents an approximate optimal control of nonlinear continuous-time systems in affine form by using the adaptive dynamic programming (ADP) with event-sampled state and input vectors. The knowledge of the system dynamics is relaxed by using a neural network (NN) identifier with event-sampled inputs. The value function, which becomes an approximate solution to the Hamilton-Jacobi-Bellman equation, is generated by using event-sampled NN approximator. Subsequently, the NN identifier and the approximated value function are utilized to obtain the optimal control policy. Both the identifier and value function approximator weights are tuned only at the event-sampled instants leading to an aperiodic update scheme. A novel adaptive event sampling condition is designed to determine the sampling instants, such that the approximation accuracy and the stability are maintained. A positive lower bound on the minimum inter-sample time is guaranteed to avoid accumulation point, and the dependence of inter-sample time upon the NN weight estimates is analyzed. A local ultimate boundedness of the resulting nonlinear impulsive dynamical closed-loop system is shown. Finally, a numerical example is utilized to evaluate the performance of the near-optimal design. The net result is the design of an event-sampled ADP-based controller for nonlinear continuous-time systems.
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.
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.
NASA Technical Reports Server (NTRS)
Childs, A. G.
1971-01-01
A discrete steepest ascent method which allows controls which are not piecewise constant (for example, it allows all continuous piecewise linear controls) was derived for the solution of optimal programming problems. This method is based on the continuous steepest ascent method of Bryson and Denham and new concepts introduced by Kelley and Denham in their development of compatible adjoints for taking into account the effects of numerical integration. The method is a generalization of the algorithm suggested by Canon, Cullum, and Polak with the details of the gradient computation given. The discrete method was compared with the continuous method for an aerodynamics problem for which an analytic solution is given by Pontryagin's maximum principle, and numerical results are presented. The discrete method converges more rapidly than the continuous method at first, but then for some undetermined reason, loses its exponential convergence rate. A comparsion was also made for the algorithm of Canon, Cullum, and Polak using piecewise constant controls. This algorithm is very competitive with the continuous algorithm.
Optimizing the imaging of the monkey auditory cortex: sparse vs. continuous fMRI.
Petkov, Christopher I; Kayser, Christoph; Augath, Mark; Logothetis, Nikos K
2009-10-01
The noninvasive imaging of the monkey auditory system with functional magnetic resonance imaging (fMRI) can bridge the gap between electrophysiological studies in monkeys and imaging studies in humans. Some of the recent imaging of monkey auditory cortical and subcortical structures relies on a technique of "sparse imaging," which was developed in human studies to sidestep the negative influence of scanner noise by adding periods of silence in between volume acquisition. Among the various aspects that have gone into the ongoing optimization of fMRI of the monkey auditory cortex, replacing the more common continuous-imaging paradigm with sparse imaging seemed to us to make the most obvious difference in the amount of activity that we could reliably obtain from awake or anesthetized animals. Here, we directly compare the sparse- and continuous-imaging paradigms in anesthetized animals. We document a strikingly greater auditory response with sparse imaging, both quantitatively and qualitatively, which includes a more expansive and robust tonotopic organization. There were instances where continuous imaging could better reveal organizational properties that sparse imaging missed, such as aspects of the hierarchical organization of auditory cortex. We consider the choice of imaging paradigm as a key component in optimizing the fMRI of the monkey auditory cortex.
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.
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.
Lin, Na; Chen, Hanning; Jing, Shikai; Liu, Fang; Liang, Xiaodan
2017-03-01
In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS(2)Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS(2)O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS(2)O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm's performance. Then PS(2)O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.
NASA Astrophysics Data System (ADS)
Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu
2016-01-01
An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.
A policy iteration approach to online optimal control of continuous-time constrained-input systems.
Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L
2013-09-01
This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach.
Binder, H; Sauerbrei, W
2010-03-30
When global techniques, based on fractional polynomials (FPs), are employed for modeling potentially nonlinear effects of several continuous covariates on a response, accessible model equations are obtained. However, local features might be missed. Therefore, a procedure is introduced, which systematically checks model fits, obtained by the multivariable fractional polynomial (MFP) approach, for overlooked local features. Statistically significant local polynomials are then parsimoniously added. This approach, called MFP + L, is seen to result in an effective control of the Type I error with respect to the addition of local components in a small simulation study with univariate and multivariable settings. Prediction performance is compared with that of a penalized regression spline technique. In a setting unfavorable for FPs, the latter outperforms the MFP approach, if there is much information in the data. However, the addition of local features reduces this performance difference. There is only a small detrimental effect in settings where the MFP approach performs better. In an application example with children's respiratory health data, fits from the spline-based approach indicate many local features, but MFP + L adds only few significant features, which seem to have good support in the data. The proposed approach may be expected to be superior in settings with local features, but retains the good properties of the MFP approach in a large number of settings where global functions are sufficient.
Confidence intervals for the symmetry point: an optimal cutpoint in continuous diagnostic tests.
López-Ratón, Mónica; Cadarso-Suárez, Carmen; Molanes-López, Elisa M; Letón, Emilio
2016-01-01
Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For this reason, it is useful to select an appropriate discrimination threshold. There are several optimality criteria: the North-West corner, the Youden index, the concordance probability and the symmetry point, among others. In this paper, we focus on the symmetry point that maximizes simultaneously the two types of correct classifications. We construct confidence intervals for this optimal cutpoint and its associated specificity and sensitivity indexes using two approaches: one based on the generalized pivotal quantity and the other on empirical likelihood. We perform a simulation study to check the practical behaviour of both methods and illustrate their use by means of three real biomedical datasets on melanoma, prostate cancer and coronary artery disease. Copyright © 2016 John Wiley & Sons, Ltd.
Liu, Haorui; Yi, Fengyan; Yang, Heli
2016-01-01
The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. PMID:26819584
Hurtado, F J; Kaiser, A S; Zamora, B
2015-03-15
Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR.
Ren, N Q; Chua, H; Chan, S Y; Tsang, Y F; Wang, Y J; Sin, N
2007-07-01
In this study, the optimal fermentation type and the operating conditions of anaerobic process in continuous-flow acidogenic reactors was investigated for the maximization of bio-hydrogen production using mixed cultures. Butyric acid type fermentation occurred at pH>6, propionic acid type fermentation occurred at pH about 5.5 with E(h) (redox potential) >-278mV, and ethanol-type fermentation occurred at pH<4.5. The representative strains of these fermentations were Clostridium sp., Propionibacterium sp. and Bacteriodes sp., respectively. Ethanol fermentation was optimal type by comparing the operating stabilities and hydrogen production capacities between the fermentation types, which remained stable when the organic loading rate (OLR) reached the highest OLR at 86.1kgCOD/m(3)d. The maximum hydrogen production reached up to 14.99L/d.
Liu, Haorui; Yi, Fengyan; Yang, Heli
2016-01-01
The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the "elite strategy" to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.
Haque, Shafiul; Khan, Saif; Wahid, Mohd; Dar, Sajad A; Soni, Nipunjot; Mandal, Raju K; Singh, Vineeta; Tiwari, Dileep; Lohani, Mohtashim; Areeshi, Mohammed Y; Govender, Thavendran; Kruger, Hendrik G; Jawed, Arshad
2016-01-01
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties.
Haque, Shafiul; Khan, Saif; Wahid, Mohd; Dar, Sajad A.; Soni, Nipunjot; Mandal, Raju K.; Singh, Vineeta; Tiwari, Dileep; Lohani, Mohtashim; Areeshi, Mohammed Y.; Govender, Thavendran; Kruger, Hendrik G.; Jawed, Arshad
2016-01-01
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600 nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600 nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties. PMID:27920762
Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir
2007-01-01
In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.
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.
NASA Astrophysics Data System (ADS)
Wang, Dianhui; Dillon, T. S.
2001-03-01
Extracting and optimizing rules from continuous or mixed- mode data directly for pattern classification problems is a challenging problem. Self-organizing neural-nets are employed to initialize the rules. A regularization model which trades off misclassification rate, recognition rate and generalization ability is first presented for refining the initial rules. To generate rules for patterns with lower probability density but considerable conceptual importance, an approach to iteratively resolving the clustering part for a filtered set of data is used. The methodology is evaluated using Iris data and demonstrates the effectiveness of technique.
NASA Astrophysics Data System (ADS)
Zhu, Zhengfan; Gan, Qingbo; Yang, Xin; Gao, Yang
2017-08-01
We have developed a novel continuation technique to solve optimal bang-bang control for low-thrust orbital transfers considering the first-order necessary optimality conditions derived from Lawden's primer vector theory. Continuation on the thrust amplitude is mainly described in this paper. Firstly, a finite-thrust transfer with an ;On-Off-On; thrusting sequence is modeled using a two-impulse transfer as initial solution, and then the thrust amplitude is decreased gradually to find an optimal solution with minimum thrust. Secondly, the thrust amplitude is continued from its minimum value to positive infinity to find the optimal bang-bang control, and a thrust switching principle is employed to determine the control structure by monitoring the variation of the switching function. In the continuation process, a bifurcation of bang-bang control is revealed and the concept of critical thrust is proposed to illustrate this phenomenon. The same thrust switching principle is also applicable to the continuation on other parameters, such as transfer time, orbital phase angle, etc. By this continuation technique, fuel-optimal orbital transfers with variable mission parameters can be found via an automated algorithm, and there is no need to provide an initial guess for the costate variables. Moreover, continuation is implemented in the solution space of bang-bang control that is either optimal or non-optimal, which shows that a desired solution of bang-bang control is obtained via continuation on a single parameter starting from an existing solution of bang-bang control. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed continuation technique. Specifically, this continuation technique provides an approach to find multiple solutions satisfying the first-order necessary optimality conditions to the same orbital transfer problem, and a continuation strategy is presented as a preliminary approach for solving the bang-bang control of many
An intelligent factory-wide optimal operation system for continuous production process
NASA Astrophysics Data System (ADS)
Ding, Jinliang; Chai, Tianyou; Wang, Hongfeng; Wang, Junwei; Zheng, Xiuping
2016-03-01
In this study, a novel intelligent factory-wide operation system for a continuous production process is designed to optimise the entire production process, which consists of multiple units; furthermore, this system is developed using process operational data to avoid the complexity of mathematical modelling of the continuous production process. The data-driven approach aims to specify the structure of the optimal operation system; in particular, the operational data of the process are used to formulate each part of the system. In this context, the domain knowledge of process engineers is utilised, and a closed-loop dynamic optimisation strategy, which combines feedback, performance prediction, feed-forward, and dynamic tuning schemes into a framework, is employed. The effectiveness of the proposed system has been verified using industrial experimental results.
Arifeen, Najmul; Wang, Ruohang; Kookos, Ioannis; Webb, Colin; Koutinas, Apostolis A
2007-01-01
A wheat-based continuous process for the production of a nutrient-complete feedstock for bioethanol production by yeast fermentation has been cost-optimized. This process could substitute for the current wheat dry milling process employed in industry for bioethanol production. Each major wheat component (bran, gluten, starch) is extracted and processed for different end-uses. The separate stages, liquefaction and saccharification, used currently in industry for starch hydrolysis have been integrated into a simplified continuous process by exploiting the complex enzymatic consortium produced by on-site fungal bioconversions. A process producing 120 m3 h-1 nutrient-complete feedstock for bioethanol production containing 250 g L-1 glucose and 0.85 g L-1 free amino nitrogen would result in a production cost of $0.126/kg glucose.
Strong-continuation, contrast-invariant inpainting with a third-order optimal PDE.
Bertalmío, Marcelo
2006-07-01
PDE-based image inpainting has become a very active area of research after the pioneering works of Masnou and Morel, Bertalmío et al., and Ballester et al. In this paper, we take a different approach, inspired by the excellent work of Caselles et al. We view the inpainting problem as a particular case of image interpolation in which we intend to propagate level lines. Expressing this in terms of local neighborhoods and using a Taylor expansion we derive a third-order PDE that performs inpainting. This PDE is optimal in the sense that it is the most accurate third-order PDE which can ensure continuation of level lines. The continuation is strong, allowing the restoration of thin structures occluded by a wide gap. The result is also contrast invariant. This is a novel PDE, which, in both its accuracy and contrast invariance, outperforms the approaches cited above.
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.
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
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.
Peng, Xian; Yuan, Han; Chen, Wufan; Wang, Tao; Ding, Lei
2017-01-01
Continuous loop averaging deconvolution (CLAD) is one of the proven methods for recovering transient auditory evoked potentials (AEPs) in rapid stimulation paradigms, which requires an elaborated stimulus sequence design to attenuate impacts from noise in data. The present study aimed to develop a new metric in gauging a CLAD sequence in terms of noise gain factor (NGF), which has been proposed previously but with less effectiveness in the presence of pink (1/f) noise. We derived the new metric by explicitly introducing the 1/f model into the proposed time-continuous sequence. We selected several representative CLAD sequences to test their noise property on typical EEG recordings, as well as on five real CLAD electroencephalogram (EEG) recordings to retrieve the middle latency responses. We also demonstrated the merit of the new metric in generating and quantifying optimized sequences using a classic genetic algorithm. The new metric shows evident improvements in measuring actual noise gains at different frequencies, and better performance than the original NGF in various aspects. The new metric is a generalized NGF measurement that can better quantify the performance of a CLAD sequence, and provide a more efficient mean of generating CLAD sequences via the incorporation with optimization algorithms. The present study can facilitate the specific application of CLAD paradigm with desired sequences in the clinic.
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.
NASA Astrophysics Data System (ADS)
Li, Yuan; Gosálvez, Miguel A.; Pal, Prem; Sato, Kazuo; Xing, Yan
2015-05-01
We combine the particle swarm optimization (PSO) method and the continuous cellular automaton (CCA) in order to simulate deep reactive ion etching (DRIE), also known as the Bosch process. By considering a generic growth/etch process, the proposed PSO-CCA method provides a general, integrated procedure to optimize the parameter values of any given theoretical model conceived to describe the corresponding experiments, which are simulated by the CCA method. To stress the flexibility of the PSO-CCA method, two different theoretical models of the DRIE process are used, namely, the ballistic transport and reaction (BTR) model, and the reactant concentration (RC) model. DRIE experiments are designed and conducted to compare the simulation results with the experiments on different machines and process conditions. Previously reported experimental data are also considered to further test the flexibility of the proposed method. The agreement between the simulations and experiments strongly indicates that the PSO-CCA method can be used to adjust the theoretical parameters by using a limited amount of experimental data. The proposed method has the potential to be applied on the modeling and optimization of other growth/etch processes.
Optimization strategies for single-stage, multi-stage and continuous ADRs
NASA Astrophysics Data System (ADS)
Shirron, Peter
2014-07-01
Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.
Optimization Strategies for Single-Stage, Multi-Stage and Continuous ADRs
NASA Technical Reports Server (NTRS)
Shirron, Peter J.
2014-01-01
Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.
Mostafaei, M; Ghobadian, B; Barzegar, M; Banakar, A
2015-11-01
This paper evaluates and optimizes the continuous production of biodiesel from waste cooking oil. In this research work, methanol and potassium hydroxide were used as catalyst engaging response surface methodology. For this purpose, the central composite experimental design (CCED), the effects of various factors such as irradiation distance, probe diameter, ultrasonic amplitude, vibration pulse and material flow into the reactor on reaction yield were studied to optimize the process. The results showed that all of the considered parameters affect the reaction efficiency significantly. The optimum combination of the findings include: irradiation distance which was 75 mm, probe diameter of 28 mm, ultrasonic amplitude of 56%, vibration pulse of 62% and flow rate of 50 ml/min that caused the reaction yield of 91.6% and energy consumption of 102.8 W. To verify this optimized combination, three tests were carried out. The results showed an average efficiency of 91.12% and 102.4 W power consumption which is well matched with the model's predictions. Copyright © 2015 Elsevier B.V. All rights reserved.
Optimization of partial nitritation in a continuous flow internal loop airlift reactor.
Jin, Ren-Cun; Xing, Bao-Shan; Ni, Wei-Min
2013-11-01
In the present study, the performance of the partial nitritation (PN) process in a continuous flow internal loop airlift reactor was optimized by applying the response surface method (RSM). The purpose of this work was to find the optimal combination of influent ammonium (NH4(+)-Ninf), dissolved oxygen (DO) and the alkalinity/ammonium ratio (Alk/NH4(+)-N) with respect to the effluent nitrite to ammonium molar ratio and nitrite accumulation ratio. Based on the RSM results, the reduced cubic model and the quadratic model developed for the responses indicated that the optimal conditions were a DO content of 1.1-2.1 mg L(-1), an Alk/NH4(+)-N ratio of 3.30-5.69 and an NH4(+)-Ninf content of 608-1039 mg L(-1). The results of confirmation trials were close to the predictions of the developed models. Furthermore, three types of alkali were comparatively explored for use in the PN process, and bicarbonate was found to be the best alkalinity source.
Aerodynamic Optimization Design of Multi-stage Turbine Using the Continuous Adjoint Method
NASA Astrophysics Data System (ADS)
Chen, Lei; Chen, Jiang
2015-05-01
This paper develops a continuous adjoint formulation for the aerodynamic shape design of a turbine in a multi-stage environment based on S2 surface governed by the Euler equations with source terms. First, given the general expression of the objective function, the adjoint equations and their boundary conditions are derived by introducing the adjoint variable vectors. Then, the final expression of the objective function gradient only includes the terms pertinent to the physical shape variations. The adjoint system is solved numerically by a finite-difference method with the Jameson spatial scheme employing first and third order dissipative flux and the time-marching is conducted by Runge-Kutta time method. Integrating the blade stagger angles, stacking lines and passage perturbation parameterization with the Quasi-Newton method of BFGS, a gradient-based aerodynamic optimization design system is constructed. Finally, the application of the adjoint method is validated through the blade and passage optimization of a 2-stage turbine with an objective function of entropy generation. The efficiency increased by 0.37% with the deviations of the mass flow rate and the pressure ratio within 1% via the optimization, which demonstrates the capability of the gradient-based system for turbine aerodynamic design.
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
PollyNET: a global network of automated Raman-polarization lidars for continuous aerosol profiling
NASA Astrophysics Data System (ADS)
Baars, H.; Kanitz, T.; Engelmann, R.; Althausen, D.; Heese, B.; Komppula, M.; Preißler, J.; Tesche, M.; Ansmann, A.; Wandinger, U.; Lim, J.-H.; Ahn, J. Y.; Stachlewska, I. S.; Amiridis, V.; Marinou, E.; Seifert, P.; Hofer, J.; Skupin, A.; Schneider, F.; Bohlmann, S.; Foth, A.; Bley, S.; Pfüller, A.; Giannakaki, E.; Lihavainen, H.; Viisanen, Y.; Hooda, R. K.; Pereira, S.; Bortoli, D.; Wagner, F.; Mattis, I.; Janicka, L.; Markowicz, K. M.; Achtert, P.; Artaxo, P.; Pauliquevis, T.; Souza, R. A. F.; Sharma, V. P.; van Zyl, P. G.; Beukes, J. P.; Sun, J. Y.; Rohwer, E. G.; Deng, R.; Mamouri, R. E.; Zamorano, F.
2015-10-01
A global vertically resolved aerosol data set covering more than 10 years of observations at more than 20 measurement sites distributed from 63° N to 52° S and 72° W to 124° E has been achieved within the Raman and polarization lidar network PollyNET. This network consists of portable, remote-controlled multiwavelength-polarization-Raman lidars (Polly) for automated and continuous 24/7 observations of clouds and aerosols. PollyNET is an independent, voluntary, and scientific network. All Polly lidars feature a standardized instrument design and apply unified calibration, quality control, and data analysis. The observations are processed in near-real time without manual intervention, and are presented online at http://polly.tropos.de. The paper gives an overview of the observations on four continents and two research vessels obtained with eight Polly systems. The specific aerosol types at these locations (mineral dust, smoke, dust-smoke and other dusty mixtures, urban haze, and volcanic ash) are identified by their Ångström exponent, lidar ratio, and depolarization ratio. The vertical aerosol distribution at the PollyNET locations is discussed on the basis of more than 55 000 automatically retrieved 30 min particle backscatter coefficient profiles at 532 nm. A seasonal analysis of measurements at selected sites revealed typical and extraordinary aerosol conditions as well as seasonal differences. These studies show the potential of PollyNET to support the establishment of a global aerosol climatology that covers the entire troposphere.
NASA Astrophysics Data System (ADS)
Jacobson, Gloria; Rella, Chris; Farinas, Alejandro
2014-05-01
Technological advancement of instrumentation in atmospheric and other geoscience disciplines over the past decade has lead to a shift from discrete sample analysis to continuous, in-situ monitoring. Standard error analysis used for discrete measurements is not sufficient to assess and compare the error contribution of noise and drift from continuous-measurement instruments, and a different statistical analysis approach should be applied. The Allan standard deviation analysis technique developed for atomic clock stability assessment by David W. Allan [1] can be effectively and gainfully applied to continuous measurement instruments. As an example, P. Werle et al has applied these techniques to look at signal averaging for atmospheric monitoring by Tunable Diode-Laser Absorption Spectroscopy (TDLAS) [2]. This presentation will build on, and translate prior foundational publications to provide contextual definitions and guidelines for the practical application of this analysis technique to continuous scientific measurements. The specific example of a Picarro G2401 Cavity Ringdown Spectroscopy (CRDS) analyzer used for continuous, atmospheric monitoring of CO2, CH4 and CO will be used to define the basics features the Allan deviation, assess factors affecting the analysis, and explore the time-series to Allan deviation plot translation for different types of instrument noise (white noise, linear drift, and interpolated data). In addition, the useful application of using an Allan deviation to optimize and predict the performance of different calibration schemes will be presented. Even though this presentation will use the specific example of the Picarro G2401 CRDS Analyzer for atmospheric monitoring, the objective is to present the information such that it can be successfully applied to other instrument sets and disciplines. [1] D.W. Allan, "Statistics of Atomic Frequency Standards," Proc, IEEE, vol. 54, pp 221-230, Feb 1966 [2] P. Werle, R. Miicke, F. Slemr, "The Limits
Relative performance of two-stage continual reassessment method in contrast to an optimal benchmark
Wages, Nolan A.; Conaway, Mark R.; O’Quigley, John
2015-01-01
Background The two-stage, likelihood-based continual reassessment method (CRM-L; O’Quigley and Shen [1]) entails the specification of a set of design parameters prior to the beginning of its use in a study. The impression of clinicians is that the success of model-based designs, such as CRM-L, depends upon some of the choices made in regard to these specifications, such as the choice of parametric dose-toxicity model and the initial guess of toxicity probabilities. Purpose In studying the efficiency and comparative performance of competing dose-finding designs for finite (typically small) samples, the non-parametric optimal benchmark (O’Quigley, Paoletti and Maccario [2]) is a useful tool. When comparing a dose-finding design to the optimal design, we are able to assess how much room there is for potential improvement. Methods The optimal method, based only on an assumption of monotonicity of the dose-toxicity function, is a valuable theoretical construct serving as a benchmark in theoretical studies, similar to that of a Cramer-Rao bound. We consider the performance of CRM-L under various design specifications and how it compares to the optimal design across a range of practical situations. Results Using simple recommendations for design specifications, the CRM-L will produce performances, in terms of identifying doses at and around the MTD, that are close to the optimal method on average over a broad group of dose-toxicity scenarios. Limitations Although the simulation settings vary the number of doses considered, the target toxicity rate and the sample size, the results here are presented for a small, though widely-used, set of two-stage CRM designs. Conclusions Based on simulations here, and many others not shown, CRM-L is almost as accurate, in many scenarios, as the (unknown and unavailable) optimal design. On average, there appears to be very little margin for improvement. Even if a finely tuned skeleton [3] offers some improvement over a simple skeleton
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.
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.
Spatially continuous mapping of daily global ozone distribution (2004-2014) with the Aura OMI sensor
NASA Astrophysics Data System (ADS)
Peng, Xiaolin; Shen, Huanfeng; Zhang, Liangpei; Zeng, Chao; Yang, Gang; He, Zongyi
2016-11-01
Total ozone data from the Aura Ozone Monitoring Instrument (OMI) play an important role in the monitoring of the spatial distribution and temporal change of total ozone. However, since September 2005, and especially after mid-2006, due to row anomalies in the OMI instrument, one third to one half of the OMI total ozone data has been missing. In this study, we generate a spatially continuous and daily global total ozone product (2004-2014) by quantitatively reconstructing the level 3 (gridded) total ozone data via a new two-step method, which takes full advantage of the temporal and spatial correlation characteristics. First, a preliminary prediction is made based on an adaptive weighted temporal fitting method. Residual correction based on an anisotropic kriging method is then proposed to further improve the prediction accuracy. To assess the efficacy of the proposed method, a comparison of different gap filling algorithms through a series of simulated experiments was performed. On this basis, we further evaluated the proposed product with Brewer spectrophotometers' total ozone columns. The evaluation results suggest that the proposed method outperforms the other algorithms, and its product is better able to capture total ozone variation than the MERRA-2 assimilated ozone product. The total ozone product produced in this study can be freely downloaded from http://sendimage.whu.edu.cn/send-resource-download/.
Namiki, Ryo; Koashi, Masato; Imoto, Nobuyuki
2006-03-15
We investigate the security of continuous-variable quantum key distribution using coherent states and reverse reconciliation against Gaussian individual attacks based on an optimal Gaussian 1{yields}2 cloning machine. We provide an implementation of the optimal Gaussian individual attack. We also find a Bell-measurement attack which works without delayed choice of measurements and has better performance than the cloning attack.
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.
A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw
2001-01-01
An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).
Rakshit, Sourav; Ananthasuresh, G K
2010-02-07
We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa-Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation.
Optimization of high-resolution continuous flow analysis for transient climate signals in ice cores.
Bigler, Matthias; Svensson, Anders; Kettner, Ernesto; Vallelonga, Paul; Nielsen, Maibritt E; Steffensen, Jørgen Peder
2011-05-15
Over the past two decades, continuous flow analysis (CFA) systems have been refined and widely used to measure aerosol constituents in polar and alpine ice cores in very high-depth resolution. Here we present a newly designed system consisting of sodium, ammonium, dust particles, and electrolytic meltwater conductivity detection modules. The system is optimized for high-resolution determination of transient signals in thin layers of deep polar ice cores. Based on standard measurements and by comparing sections of early Holocene and glacial ice from Greenland, we find that the new system features a depth resolution in the ice of a few millimeters which is considerably better than other CFA systems. Thus, the new system can resolve ice strata down to 10 mm thickness and has the potential of identifying annual layers in both Greenland and Antarctic ice cores throughout the last glacial cycle.
Vercruysse, J; Peeters, E; Fonteyne, M; Cappuyns, P; Delaet, U; Van Assche, I; De Beer, T; Remon, J P; Vervaet, C
2015-01-01
Since small scale is key for successful introduction of continuous techniques in the pharmaceutical industry to allow its use during formulation development and process optimization, it is essential to determine whether the product quality is similar when small quantities of materials are processed compared to the continuous processing of larger quantities. Therefore, the aim of this study was to investigate whether material processed in a single cell of the six-segmented fluid bed dryer of the ConsiGma™-25 system (a continuous twin screw granulation and drying system introduced by GEA Pharma Systems, Collette™, Wommelgem, Belgium) is predictive of granule and tablet quality during full-scale manufacturing when all drying cells are filled. Furthermore, the performance of the ConsiGma™-1 system (a mobile laboratory unit) was evaluated and compared to the ConsiGma™-25 system. A premix of two active ingredients, powdered cellulose, maize starch, pregelatinized starch and sodium starch glycolate was granulated with distilled water. After drying and milling (1000 μm, 800 rpm), granules were blended with magnesium stearate and compressed using a Modul™ P tablet press (tablet weight: 430 mg, main compression force: 12 kN). Single cell experiments using the ConsiGma™-25 system and ConsiGma™-1 system were performed in triplicate. Additionally, a 1h continuous run using the ConsiGma™-25 system was executed. Process outcomes (torque, barrel wall temperature, product temperature during drying) and granule (residual moisture content, particle size distribution, bulk and tapped density, hausner ratio, friability) as well as tablet (hardness, friability, disintegration time and dissolution) quality attributes were evaluated. By performing a 1h continuous run, it was detected that a stabilization period was needed for torque and barrel wall temperature due to initial layering of the screws and the screw chamber walls with material. Consequently, slightly deviating
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.
Najafpoor, Ali Asghar; Davoudi, Mojtaba; Salmani, Elham Rahmanpour
2017-03-01
Copper, as an inseparable part of many industrial discharges, threatens both public and environmental health. In this work, an electrochemical cell utilizing a cellulosic separator was used to evaluate Cu removal using graphite anodes and stainless steel cathodes in a continuous-flow mode reactor. In the experimental matrix, Cu concentration (1-5 mg L(-1)), electrolysis time (20-90 min), and current intensity (0.1-0.4 A) were employed. Results showed that the maximum removal efficiency of copper was obtained as 99%. The removal efficiency was independent of initial copper concentration and directly related to electrolysis time and current intensity. Energy consumption was more dependent on current intensity than electrolysis time. Under optimal conditions (75.8 min electrolysis time, 0.18 A current intensity, and 3 mg L(-1) copper concentration), the removal efficiency was obtained as 91% while 7.05 kWh m(-3) electrical energy was consumed. The differences between the actual and predicted data under optimal conditions were 0.42% for copper removal and 0.23% for energy consumption, which signify the performance and reliability of the developed models. The results exhibited the suitability of the electrochemical reduction for copper removal from aqueous solutions, which was facilitated under alkaline conditions prevailing in the cathodic compartment due to applying a cell divided by a cellulosic separator.
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.
Clinical review: Optimal dose of continuous renal replacement therapy in acute kidney injury
2011-01-01
Continuous renal replacement therapy (CRRT) is the preferred treatment for acute kidney injury in intensive care units (ICUs) throughout much of the world. Despite the widespread use of CRRT, controversy and center-specific practice variation in the clinical application of CRRT continue. In particular, whereas two single-center studies have suggested survival benefit from delivery of higher-intensity CRRT to patients with acute kidney injury in the ICU, other studies have been inconsistent in their results. Now, however, two large multi-center randomized controlled trials - the Veterans Affairs/National Institutes of Health Acute Renal Failure Trial Network (ATN) study and the Randomized Evaluation of Normal versus Augmented Level (RENAL) Replacement Therapy Study - have provided level 1 evidence that effluent flow rates above 25 mL/kg per hour do not improve outcomes in patients in the ICU. In this review, we discuss the concept of dose of CRRT, its relationship with clinical outcomes, and what target optimal dose of CRRT should be pursued in light of the high-quality evidence now available. PMID:21489322
Balasubramanian, Sundar; Allen, James D; Kanitkar, Akanksha; Boldor, Dorin
2011-02-01
A 1.2 kW, 2450 MHz resonant continuous microwave processing system was designed and optimized for oil extraction from green algae (Scenedesmus obliquus). Algae-water suspension (1:1 w/w) was heated to 80 and 95°C, and subjected to extraction for up to 30 min. Maximum oil yield was achieved at 95°C and 30 min. The microwave system extracted 76-77% of total recoverable oil at 20-30 min and 95°C, compared to only 43-47% for water bath control. Extraction time and temperature had significant influence (p<0.0001) on extraction yield. Oil analysis indicated that microwaves extracted oil containing higher percentages of unsaturated and essential fatty acids (indicating higher quality). This study validates for the first time the efficiency of a continuous microwave system for extraction of lipids from algae. Higher oil yields, faster extraction rates and superior oil quality demonstrate this system's feasibility for oil extraction from a variety of feedstock.
An online spaced-education game for global continuing medical education: a randomized trial.
Kerfoot, B Price; Baker, Harley
2012-07-01
To assess the efficacy of a "spaced-education" game as a method of continuing medical education (CME) among physicians across the globe. The efficacy of educational games for the CME has yet to be established. We created a novel online educational game by incorporating game mechanics into "spaced education" (SE), an evidence-based method of online CME. This 34-week randomized trial enrolled practicing urologists across the globe. The SE game consisted of 40 validated multiple-choice questions and explanations on urology clinical guidelines. Enrollees were randomized to 2 cohorts: cohort A physicians were sent 2 questions via an automated e-mail system every 2 days, and cohort B physicians were sent 4 questions every 4 days. Adaptive game mechanics re-sent the questions in 12 or 24 days if answered incorrectly and correctly, respectively. Questions expired if not answered on time (appointment dynamic). Physicians retired questions by answering each correctly twice-in-a-row (progression dynamic). Competition was fostered by posting relative performance among physicians. Main outcome measures were baseline scores (percentage of questions answered correctly upon initial presentation) and completion scores (percentage of questions retired). A total of 1470 physicians from 63 countries enrolled. Median baseline score was 48% (interquartile range [IQR] 17) and, in multivariate analyses, was found to vary significantly by region (Cohen dmax = 0.31, P = 0.001) and age (dmax = 0.41, P < 0.001). Median completion score was 98% (IQR 25) and varied significantly by age (dmax = 0.21, P < 0.001) and American Board of Urology certification (d = 0.10, P = 0.033) but not by region (multivariate analyses). Question clustering reduced physicians' performance (d = 0.43, P < 0.001). Seventy-six percent of enrollees (1111/1470) requested to participate in future SE games. An online SE game can substantially improve guidelines knowledge and is a well-accepted method of global CME delivery.
Orozco, Raquel; Godfrey, Scott; Coffman, Jon; Amarikwa, Linus; Parker, Stephanie; Hernandez, Lindsay; Wachuku, Chinenye; Mai, Ben; Song, Brian; Hoskatti, Shashidhar; Asong, Jinkeng; Shamlou, Parviz; Bardliving, Cameron; Fiadeiro, Marcus
2017-02-11
We designed, built or 3D printed, and screened tubular reactors that minimize axial dispersion to serve as incubation chambers for continuous virus inactivation of biological products. Empirical residence time distribution data were used to derive each tubular design's volume equivalent to a theoretical plate (VETP) values at a various process flow rates. One design, the Jig in a Box (JIB), yielded the lowest VETP, indicating optimal radial mixing and minimal axial dispersion. A minimum residence time (MRT) approach was employed, where the MRT is the minimum time the product spends in the tubular reactor. This incubation time is typically 60 minutes in a batch process. We provide recommendations for combinations of flow rates and device dimensions for operation of the JIB connected in series that will meet a 60-min MRT. The results show that under a wide range of flow rates and corresponding volumes, it takes 75 ± 3 min for 99% of the product to exit the reactor while meeting the 60-min MRT criterion and fulfilling the constraint of keeping a differential pressure drop under 5 psi. Under these conditions, the VETP increases slightly from 3 to 5 mL though the number of theoretical plates stays constant at about 1326 ± 88. We also demonstrated that the final design volume was only 6% ± 1% larger than the ideal plug flow volume. Using such a device would enable continuous viral inactivation in a truly continuous process or in the effluent of a batch chromatography column. Viral inactivation studies would be required to validate such a design. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 2017.
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
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
USDA-ARS?s Scientific Manuscript database
Over twenty-two million hectares of U.S. croplands are irrigated, but the impacts of continuous crop residue removal and tillage on soil organic carbon (SOC) stocks, soil greenhouse gas (GHG) emissions, and global warming potential (GWP) in irrigated cropping systems are relatively unknown. Residue...
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.
Algorithms for optimized maximum entropy and diagnostic tools for analytic continuation
NASA Astrophysics Data System (ADS)
Bergeron, Dominic; Tremblay, A.-M. S.
2016-08-01
Analytic continuation of numerical data obtained in imaginary time or frequency has become an essential part of many branches of quantum computational physics. It is, however, an ill-conditioned procedure and thus a hard numerical problem. The maximum-entropy approach, based on Bayesian inference, is the most widely used method to tackle that problem. Although the approach is well established and among the most reliable and efficient ones, useful developments of the method and of its implementation are still possible. In addition, while a few free software implementations are available, a well-documented, optimized, general purpose, and user-friendly software dedicated to that specific task is still lacking. Here we analyze all aspects of the implementation that are critical for accuracy and speed and present a highly optimized approach to maximum entropy. Original algorithmic and conceptual contributions include (1) numerical approximations that yield a computational complexity that is almost independent of temperature and spectrum shape (including sharp Drude peaks in broad background, for example) while ensuring quantitative accuracy of the result whenever precision of the data is sufficient, (2) a robust method of choosing the entropy weight α that follows from a simple consistency condition of the approach and the observation that information- and noise-fitting regimes can be identified clearly from the behavior of χ2 with respect to α , and (3) several diagnostics to assess the reliability of the result. Benchmarks with test spectral functions of different complexity and an example with an actual physical simulation are presented. Our implementation, which covers most typical cases for fermions, bosons, and response functions, is available as an open source, user-friendly software.
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 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
Quantifying and visualizing variations in sets of images using continuous linear optimal transport
NASA Astrophysics Data System (ADS)
Kolouri, Soheil; Rohde, Gustavo K.
2014-03-01
Modern advancements in imaging devices have enabled us to explore the subcellular structure of living organisms and extract vast amounts of information. However, interpreting the biological information mined in the captured images is not a trivial task. Utilizing predetermined numerical features is usually the only hope for quantifying this information. Nonetheless, direct visual or biological interpretation of results obtained from these selected features is non-intuitive and difficult. In this paper, we describe an automatic method for modeling visual variations in a set of images, which allows for direct visual interpretation of the most significant differences, without the need for predefined features. The method is based on a linearized version of the continuous optimal transport (OT) metric, which provides a natural linear embedding for the image data set, in which linear combination of images leads to a visually meaningful image. This enables us to apply linear geometric data analysis techniques such as principal component analysis and linear discriminant analysis in the linearly embedded space and visualize the most prominent modes, as well as the most discriminant modes of variations, in the dataset. Using the continuous OT framework, we are able to analyze variations in shape and texture in a set of images utilizing each image at full resolution, that otherwise cannot be done by existing methods. The proposed method is applied to a set of nuclei images segmented from Feulgen stained liver tissues in order to investigate the major visual differences in chromatin distribution of Fetal-Type Hepatoblastoma (FHB) cells compared to the normal cells.
Continuation of long-term global SO2 pollution monitoring from OMI to OMPS
NASA Astrophysics Data System (ADS)
Zhang, Y.; Li, C.; Krotkov, N. A.; Joiner, J.
2016-12-01
In the past 12+ years, Ozone Monitoring Instrument (OMI) on board NASA EOS Aura satellite has pioneered the first high-resolution global SO2 pollution monitoring, which enabled new studies of atmospheric chemistry and applications for air quality management. Such long-term SO2 record will be continued with other satellite instruments, i.e., the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper on board NASA-NOAA Suomi National Polar-orbiting Partnership (S-NPP) satellite and the follow up JPSS series satellites. In this presentation, we demonstrate the first comparison between OMI and OMPS SO2 retrievals from the OMI operational SO2 algorithm, which is our state-of-the-art principal component analysis (PCA) approach. The PCA technique does not use any sort of "soft calibration" corrections required in concurrent satellite SO2 algorithms and enables seamless merging of different satellite datasets. We demonstrate a very good consistency of the retrievals from OMI and OMPS. Four full years of OMI and OMPS SO2 retrievals during 2012-2015 have been analyzed over some of the world's most polluted regions: eastern China, Mexico, and South Africa. In general, the comparisons show high correlations (r =0.79-0.96) of SO2 mass between the two instruments on a daily basis and less than unity regression slopes (0.76-0.97) indicating slightly lower OMPS SO2 mass as compared with OMI. The annual averaged SO2 loading difference between OMI and OMPS is negligible (< 0.03 Dobson Unit (DU)) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r=0.92-0.97) between the spatial distributions of the annual mean SO2 over the three regions. The two instruments also show generally good agreement in terms of the daily spatial distribution in SO2. For example, over the Mexico region for 82% of the days, the two instruments have a spatial correlation coefficient of 0.6 or better. Such consistent retrievals were achieved without any explicit
Roberts, Matthew John; Perera, Marlon; Lawrentschuk, Nathan; Romanic, Diana; Papa, Nathan; Bolton, Damien
2015-04-23
Journal clubs are an essential tool in promoting clinical evidence-based medical education to all medical and allied health professionals. Twitter represents a public, microblogging forum that can facilitate traditional journal club requirements, while also reaching a global audience, and participation for discussion with study authors and colleagues. The aim of the current study was to evaluate the current state of social media-facilitated journal clubs, specifically Twitter, as an example of continuing professional development. A systematic review of literature databases (Medline, Embase, CINAHL, Web of Science, ERIC via ProQuest) was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of Twitter, the followers of identified journal clubs, and Symplur was also performed. Demographic and monthly tweet data were extracted from Twitter and Symplur. All manuscripts related to Twitter-based journal clubs were included. Statistical analyses were performed in MS Excel and STATA. From a total of 469 citations, 11 manuscripts were included and referred to five Twitter-based journal clubs (#ALiEMJC, #BlueJC, #ebnjc, #urojc, #meded). A Twitter-based journal club search yielded 34 potential hashtags/accounts, of which 24 were included in the final analysis. The median duration of activity was 11.75 (interquartile range [IQR] 19.9, SD 10.9) months, with 7 now inactive. The median number of followers and participants was 374 (IQR 574) and 157 (IQR 272), respectively. An overall increasing establishment of active Twitter-based journal clubs was observed, resulting in an exponential increase in total cumulative tweets (R(2)=.98), and tweets per month (R(2)=.72). Cumulative tweets for specific journal clubs increased linearly, with @ADC_JC, @EBNursingBMJ, @igsjc, @iurojc, and @NephJC, and showing greatest rate of change, as well as total impressions per month since establishment. An average of two
Perera, Marlon; Lawrentschuk, Nathan; Romanic, Diana; Papa, Nathan; Bolton, Damien
2015-01-01
Background Journal clubs are an essential tool in promoting clinical evidence-based medical education to all medical and allied health professionals. Twitter represents a public, microblogging forum that can facilitate traditional journal club requirements, while also reaching a global audience, and participation for discussion with study authors and colleagues. Objective The aim of the current study was to evaluate the current state of social media–facilitated journal clubs, specifically Twitter, as an example of continuing professional development. Methods A systematic review of literature databases (Medline, Embase, CINAHL, Web of Science, ERIC via ProQuest) was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of Twitter, the followers of identified journal clubs, and Symplur was also performed. Demographic and monthly tweet data were extracted from Twitter and Symplur. All manuscripts related to Twitter-based journal clubs were included. Statistical analyses were performed in MS Excel and STATA. Results From a total of 469 citations, 11 manuscripts were included and referred to five Twitter-based journal clubs (#ALiEMJC, #BlueJC, #ebnjc, #urojc, #meded). A Twitter-based journal club search yielded 34 potential hashtags/accounts, of which 24 were included in the final analysis. The median duration of activity was 11.75 (interquartile range [IQR] 19.9, SD 10.9) months, with 7 now inactive. The median number of followers and participants was 374 (IQR 574) and 157 (IQR 272), respectively. An overall increasing establishment of active Twitter-based journal clubs was observed, resulting in an exponential increase in total cumulative tweets (R 2=.98), and tweets per month (R 2=.72). Cumulative tweets for specific journal clubs increased linearly, with @ADC_JC, @EBNursingBMJ, @igsjc, @iurojc, and @NephJC, and showing greatest rate of change, as well as total impressions per month since
NASA Technical Reports Server (NTRS)
Stackhouse, P.; Perez, R.; Sengupta, M.; Knapp, K.; Cox, Stephen; Mikovitz, J. Colleen; Zhang, T.; Hemker, K.; Schlemmer, J.; Kivalov, S.
2014-01-01
Background: Considering the likelihood of global climatic weather pattern changes and the global competition for energy resources, there is an increasing need to provide improved and continuously updated global Earth surface solar resource information. Toward this end, a project was funded under the NASA Applied Science program involving the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), National Renewable Energy Laboratory (NREL), the State University of New York/Albany (SUNY) and the NOAA National Climatic Data Center (NCDC) to provide NREL with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and variability and to provide a mechanism for continual updates of solar resource information. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983. The old version of the ISCCP data provided this information for all the world TMs available geosynchronous satellite systems and NOAA TMs AVHRR data sets at a 30 km effective resolution. This new version aims to provide a new and improved satellite calibration at an effective 10 km resolution. Thus, working with SUNY, NASA will develop and test an improved production system that will enable NREL to continually update the Earth TM solar resource. Objective and Methods: In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. An assessment of the solar resource values relative to calibration uncertainty and assumptions are presented. Errors resulting assumptions in snow cover and background aerosol
Continuation of long-term global SO2 pollution monitoring from OMI to OMPS
NASA Astrophysics Data System (ADS)
Zhang, Yan; Li, Can; Krotkov, Nickolay A.; Joiner, Joanna; Fioletov, Vitali; McLinden, Chris
2017-04-01
Over the past 20 years, advances in satellite remote sensing of pollution-relevant species have made space-borne observations an increasingly important part of atmospheric chemistry research and air quality management. This progress has been facilitated by advanced UV-vis spectrometers, such as the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing System (EOS) Aura satellite, and continues with new instruments, such as the Ozone Mapping and Profiler Suite (OMPS) on board the NASA-NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate that it is possible, using our state-of-the-art principal component analysis (PCA) retrieval technique, to continue the long-term global SO2 pollution monitoring started by OMI with the current and future OMPS instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1, 2, 3, and 4 satellites in addition to SNPP, with a very good consistency of retrievals from these instruments. Since OMI SO2 data have been primarily used for (1) providing regional context on air pollution and long-range transport on a daily basis and (2) providing information on point emission sources on an annual basis after data averaging, we focused on these two aspects in our OMI-OMPS comparisons. Four years of retrievals (2012-2015) have been compared for three regions: eastern China, Mexico, and South Africa. In general, the comparisons show relatively high correlations (r = 0. 79-0.96) of daily regional averaged SO2 mass between the two instruments and near-unity regression slopes (0.76-0.97). The annual averaged SO2 loading differences between OMI and OMPS are small (< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r = 0. 92-0.97) in the spatial distribution of annual averaged SO2 between OMI and OMPS over the three regions during 2012-2015. The emissions from ˜ 400 SO2 sources calculated with the two instruments also
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.
Assuring the continued recycling of light metals in end-of-life vehicles: A global perspective
NASA Astrophysics Data System (ADS)
Gesing, Adam
2004-08-01
This article reviews issues and technologies in recycling, both current and future, with a focus on end-of-life vehicles (ELVs) and their increasing light material content. Discussion includes the issues involved in designing for recycling, the existing global scrap recycling system, and interactions between different types of recyclables and different sections of the global market. A review follows of current scrap recycling technologies and compares the vehicle recycling regulations in the United States, European Union, and Japan. Finally, opinions are presented on useful, and some not so useful, global and local recycling regulations and initiatives.
Process design and optimization of novel wheat-based continuous bioethanol production system.
Arifeen, Najmul; Wang, Ruohang; Kookos, Ioannis K; Webb, Colin; Koutinas, Apostolis A
2007-01-01
A novel design of a wheat-based biorefinery for bioethanol production, including wheat milling, gluten extraction as byproduct, fungal submerged fermentation for enzyme production, starch hydrolysis, fungal biomass autolysis for nutrient regeneration, yeast fermentation with recycling integrated with a pervaporation membrane for ethanol concentration, and fuel-grade ethanol purification by pressure swing distillation (PSD), was optimized in continuous mode using the equation-based software General Algebraic Modelling System (GAMS). The novel wheat biorefining strategy could result in a production cost within the range of dollars 0.96-0.50 gal(-1) ethanol (dollars 0.25-0.13 L(-1) ethanol) when the production capacity of the plant is within the range of 10-33.5 million gal y(-1) (37.85-126.8 million L y(-1)). The production of value-added byproducts (e.g., bran-rich pearlings, gluten, pure yeast cells) was identified as a crucial factor for improving the economics of fuel ethanol production from wheat. Integration of yeast fermentation with pervaporation membrane could result in the concentration of ethanol in the fermentation outlet stream (up to 40 mol %). The application of a PSD system that consisted of a low-pressure and a high-pressure column and employing heat integration between the high- and low-pressure columns resulted in reduced operating cost (up to 44%) for fuel-grade ethanol production.
Yang, Yi-Hung; Klinthong, Worasaung; Tan, Chung-Sung
2015-12-01
CO2-expanded methanol (CXM) was used to extract lipids from the microalgae Chlorella vulgaris (a total lipid content of 20.7% was determined by Soxhlet extraction with methanol at 373 K for 96 h) in a continuous mode. The CXM was found to be a superior solvent to methanol, ethanol, pressurized methanol and ethanol, and CO2-expanded ethanol for lipid extraction. The effects of operation variables including temperature, pressure and CO2 flow rate on extraction performance were examined using the response surface and contour plot methodologies. The optimal operating conditions were at a pressure of 5.5 MPa, a temperature of 358 K, a methanol flow rate of 1 mL/min and a CO2 flow rate of 3.0 mL/min, providing an extracted lipid yield of 84.8 wt% over an extraction period of 30 min. Compared with propane methanol mixture, CXM was safer and more energy efficient for lipid extraction from C. vulgaris.
Song, Qiankun; Yan, Huan; Zhao, Zhenjiang; Liu, Yurong
2016-09-01
This paper investigates the stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential stability of equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential stability of equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.
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.
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
Optimal Multi-scale Demand-side Management for Continuous Power-Intensive Processes
NASA Astrophysics Data System (ADS)
Mitra, Sumit
With the advent of deregulation in electricity markets and an increasing share of intermittent power generation sources, the profitability of industrial consumers that operate power-intensive processes has become directly linked to the variability in energy prices. Thus, for industrial consumers that are able to adjust to the fluctuations, time-sensitive electricity prices (as part of so-called Demand-Side Management (DSM) in the smart grid) offer potential economical incentives. In this thesis, we introduce optimization models and decomposition strategies for the multi-scale Demand-Side Management of continuous power-intensive processes. On an operational level, we derive a mode formulation for scheduling under time-sensitive electricity prices. The formulation is applied to air separation plants and cement plants to minimize the operating cost. We also describe how a mode formulation can be used for industrial combined heat and power plants that are co-located at integrated chemical sites to increase operating profit by adjusting their steam and electricity production according to their inherent flexibility. Furthermore, a robust optimization formulation is developed to address the uncertainty in electricity prices by accounting for correlations and multiple ranges in the realization of the random variables. On a strategic level, we introduce a multi-scale model that provides an understanding of the value of flexibility of the current plant configuration and the value of additional flexibility in terms of retrofits for Demand-Side Management under product demand uncertainty. The integration of multiple time scales leads to large-scale two-stage stochastic programming problems, for which we need to apply decomposition strategies in order to obtain a good solution within a reasonable amount of time. Hence, we describe two decomposition schemes that can be applied to solve two-stage stochastic programming problems: First, a hybrid bi-level decomposition scheme with
ERIC Educational Resources Information Center
National Univ. Continuing Education Association, Washington, DC.
This fourth report in the National University Continuing Education Association (NUCEA) Challenges for Continuing Higher Education Leadership series draws attention to the wider and more complex set of issues surrounding the "internationalization" of U.S. society and its educational componets. The report consists of papers prsented at a…
ERIC Educational Resources Information Center
National Univ. Continuing Education Association, Washington, DC.
This fourth report in the National University Continuing Education Association (NUCEA) Challenges for Continuing Higher Education Leadership series draws attention to the wider and more complex set of issues surrounding the "internationalization" of U.S. society and its educational componets. The report consists of papers prsented at a…
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)
Zhou, Bin; Hou, Ming-Zhe; Duan, Guang-Ren
2013-04-01
This article is concerned with L ∞ and L 2 semi-global stabilisation of continuous-time periodic linear systems with bounded controls. Two problems, namely L ∞ semi-global stabilisation with controls having bounded magnitude and L 2 semi-global stabilisation with controls having bounded energy, are solved based on solutions to a class of periodic Lyapunov differential equations (PLDEs) resulting from the problem of minimal energy control with guaranteed convergence rate. Under the assumption that the open-loop system is (asymptotically) null controllable with constrained controls, periodic feedback are established to solve the concerned problems. The proposed PLDE-based approaches possess the advantage that the resulting controllers are easy to implement since the designers need only to solve a linear differential equation. A numerical example is worked out to illustrate the effectiveness of the proposed approach.
Lemmel, S A; Heimsch, R C; Edwards, L L
1979-02-01
The yeasts Candida utilis and Saccharomycopsis fibuliger were propagated as a source of single-cell protein in a continuous, mixed, aerobic, single-stage cultivation on blancher water generated during potato processing. A series of steady-state experiments based on a two-level factorial design, half-replicate modified with an intermediate experiment, was performed to determine the effect of pH, 3.8 to 4.8; dissolved oxygen, 42 to 80% saturation; dilution rate, 0.17 to 0.31 h(-1); and temperature, 27 to 32 degrees C on the amount of carbon consumed, the rate of carbon consumption (R(c)), the amount of reducing sugar consumed, the rate of sugar consumption (R(g)), the amount of protein produced, the rate of protein production (R(p)), the yield from carbon, and the yield from reducing sugar. The results were analyzed by stepwise multiple regression and Fisher's least significant difference test. Analyses showed that high dilution rates resulted in increased R(c), R(g), and R(p) and indicated that a rate of 0.31 h(-1) was below the critical dilution rate. A temperature of 32 degrees C increased the amount of carbon consumed by 34%. A pH of 4.3 to 4.8 increased the amount of protein produced. The yield from carbon was constant, and the relatively high yield from reducing sugar indicated that other substrates were consumed. Dissolved oxygen was in excess at 42% saturation and above. Since C. utilis predominated the mixed cultures and amylase production appeared to be limited, a single-stage fermentation lacked efficiency. The experimental design allowed preliminary optimization of major environmental variables with relatively few experiments and provided a basis for future kinetic studies.
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)
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.
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.
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.
NASA Technical Reports Server (NTRS)
Sabaka, T. J.; Rowlands, D. D.; Luthcke, S. B.; Boy, J.-P.
2010-01-01
We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.
NASA Astrophysics Data System (ADS)
Sabaka, T. J.; Rowlands, D. D.; Luthcke, S. B.; Boy, J.-P.
2010-11-01
We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mascons on a global 2° × 2° equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and find that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.
NASA Technical Reports Server (NTRS)
Sabaka, T. J.; Rowlands, D. D.; Luthcke, S. B.; Boy, J.-P.
2010-01-01
We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.
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
Response of snow-dependent hydrologic extremes to continued global warming.
Diffenbaugh, Noah S; Scherer, Martin; Ashfaq, Moetasim
2013-04-01
Snow accumulation is critical for water availability in the northern hemisphere (1,2), raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions (1,3). Although regional hydrologic changes have been observed (e.g., (1,3-5)), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate change impacts (3,6,7). We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the northern hemisphere, with areas of western North America, northeastern Europe, and the Greater Himalaya showing the strongest emergence during the near-term decades and at 2°C global warming. The occurrence of extremely low snow years becomes widespread by the late-21(st) century, as do the occurrence of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the northern hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2°C above the pre-industrial baseline.
Response of snow-dependent hydrologic extremes to continued global warming
Diffenbaugh, Noah S.; Scherer, Martin; Ashfaq, Moetasim
2013-01-01
Snow accumulation is critical for water availability in the northern hemisphere 1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions 1,3. Although regional hydrologic changes have been observed (e.g., 1,3–5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate change impacts 3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the northern hemisphere, with areas of western North America, northeastern Europe, and the Greater Himalaya showing the strongest emergence during the near-term decades and at 2°C global warming. The occurrence of extremely low snow years becomes widespread by the late-21st century, as do the occurrence of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the northern hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2°C above the pre-industrial baseline. PMID:24015153
Saltman, Deborah C; Kidd, Michael R; Jackson, Debra; Cleary, Michelle
2012-07-09
In workforces that are traditionally mobile and have long lead times for new supply, such as health, effective global indicators of tertiary education are increasingly essential. Difficulties with transportability of qualifications and cross-accreditation are now recognised as key barriers to meeting the rapidly shifting international demands for health care providers. The plethora of mixed education and service arrangements poses challenges for employers and regulators, let alone patients; in determining equivalence of training and competency between individuals, institutions and geographical locations. This paper outlines the shortfall of the current indicators in assisting the process of global certification and competency recognition in the health care workforce. Using Organisation for Economic Cooperation and Development (OECD) data we highlight how International standardisation in the tertiary education sector is problematic for the global health workforce. Through a series of case studies, we then describe a model which enables institutions to compare themselves internally and with others internationally using bespoke or prioritised parameters rather than standards. The mobility of the global health workforce means that transportability of qualifications is an increasing area of concern. Valid qualifications based on workplace learning and assessment requires at least some variables to be benchmarked in order to judge performance.
Continued development of a global precipitation dataset from satellite and ground-based gauges
NASA Astrophysics Data System (ADS)
Dietzsch, Felix; Andersson, Axel; Schröder, Marc; Ziese, Markus; Becker, Andreas
2017-04-01
The project framework MiKlip ("Mittelfristige Klimaprognosen") is focused on the development of an operational forecast system for decadal climate predictions. The objective of the "Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized" (DAPAGLOCO) project, is the development and operationalization of a global precipitation dataset for forecast validation of the MPI-ESM experiments used in MiKlip. The dataset is a combination of rain gauge measurement data over land and satellite-based precipitation retrievals over ocean. Over land, gauge data from the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst (DWD) are used. Over ocean, retrievals from the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) dataset are used as data source. The currently available dataset consists of 21 years of data (1988-2008) and is provided in different spatial resolutions of 1° and 2.5° on the global scale, and 0.5° for Europe. Rain rates over ocean are currently derived from satellite microwave imagers by using a neuronal network. For the future it is intended to switch this retrieval method to a 1D-Var method. The current state of the dataset is presented, an introduction to the future retrieval and its features is given and first results from evaluation and application are shown.
NASA Astrophysics Data System (ADS)
Min, Huang; Na, Cai
2017-06-01
These years, ant colony algorithm has been widely used in solving the domain of discrete space optimization, while the research on solving the continuous space optimization was relatively little. Based on the original optimization for continuous space, the article proposes the improved ant colony algorithm which is used to Solve the optimization for continuous space, so as to overcome the ant colony algorithm’s disadvantages of searching for a long time in continuous space. The article improves the solving way for the total amount of information of each interval and the due number of ants. The article also introduces a function of changes with the increase of the number of iterations in order to enhance the convergence rate of the improved ant colony algorithm. The simulation results show that compared with the result in literature[5], the suggested improved ant colony algorithm that based on the information distribution function has a better convergence performance. Thus, the article provides a new feasible and effective method for ant colony algorithm to solve this kind of problem.
Gutowski, William J.; Prusa, Joseph M.; Smolarkiewicz, Piotr K.
2012-05-08
This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the "physics" of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited. 3a. EULAG Advances EULAG is a non-hydrostatic, parallel computational model for all-scale geophysical flows. EULAG's name derives from its two computational options: EULerian (flux form) or semi-LAGrangian (advective form). The model combines nonoscillatory forward-in-time (NFT) numerical algorithms with a robust elliptic Krylov solver. A signature feature of EULAG is that it is formulated in generalized time-dependent curvilinear coordinates. In particular, this enables grid adaptivity. In total, these features give EULAG novel advantages over many existing dynamical cores. For EULAG itself, numerical advances included refining boundary conditions and filters for optimizing model performance in polar regions. We also added flexibility to the model's underlying formulation, allowing it to work with the pseudo-compressible equation set of Durran in addition to EULAG's standard anelastic formulation. Work in collaboration with others also extended the demonstrated range of
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
Xu, Gongxian; Liu, Ying; Gao, Qunwang
2016-02-10
This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems.
Continuity of Care to Optimize Chronic Disease Management in the Community Setting
2013-01-01
Background This evidence-based analysis reviews relational and management continuity of care. Relational continuity refers to the duration and quality of the relationship between the care provider and the patient. Management continuity ensures that patients receive coherent, complementary, and timely care. There are 4 components of continuity of care: duration, density, dispersion, and sequence. Objective The objective of this evidence-based analysis was to determine if continuity of care is associated with decreased health resource utilization, improved patient outcomes, and patient satisfaction. Data Sources MEDLINE, EMBASE, CINAHL, the Cochrane Library, and the Centre for Reviews and Dissemination database were searched for studies on continuity of care and chronic disease published from January 2002 until December 2011. Review Methods Systematic reviews, randomized controlled trials, and observational studies were eligible if they assessed continuity of care in adults and reported health resource utilization, patient outcomes, or patient satisfaction. Results Eight systematic reviews and 13 observational studies were identified. The reviews concluded that there is an association between continuity of care and outcomes; however, the literature base is weak. The observational studies found that higher continuity of care was frequently associated with fewer hospitalizations and emergency department visits. Three systematic reviews reported that higher continuity of care is associated with improved patient satisfaction, especially among patients with chronic conditions. Limitations Most of the studies were retrospective cross-sectional studies of large administrative databases. The databases do not capture information on trust and confidence in the provider, which is a critical component of relational continuity of care. The definitions for the selection of patients from the databases varied across studies. Conclusions There is low quality evidence that: Higher
NASA Astrophysics Data System (ADS)
Schmidt, Lennart; Krischer, Katharina
2015-06-01
We study an oscillatory medium with a nonlinear global coupling that gives rise to a harmonic mean-field oscillation with constant amplitude and frequency. Two types of cluster states are found, each undergoing a symmetry-breaking transition towards a related chimera state. We demonstrate that the diffusional coupling is non-essential for these complex dynamics. Furthermore, we investigate localized turbulence and discuss whether it can be categorized as a chimera state.
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.
Solving Continuous-Time Optimal-Control Problems with a Spreadsheet.
ERIC Educational Resources Information Center
Naevdal, Eric
2003-01-01
Explains how optimal control problems can be solved with a spreadsheet, such as Microsoft Excel. Suggests the method can be used by students, teachers, and researchers as a tool to find numerical solutions for optimal control problems. Provides several examples that range from simple to advanced. (JEH)
Solving Continuous-Time Optimal-Control Problems with a Spreadsheet.
ERIC Educational Resources Information Center
Naevdal, Eric
2003-01-01
Explains how optimal control problems can be solved with a spreadsheet, such as Microsoft Excel. Suggests the method can be used by students, teachers, and researchers as a tool to find numerical solutions for optimal control problems. Provides several examples that range from simple to advanced. (JEH)
Response of snow-dependent hydrologic extremes to continued global warming
Diffenbaugh, Noah; Scherer, Martin; Ashfaq, Moetasim
2012-01-01
Snow accumulation is critical for water availability in the Northern Hemisphere1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions1,3. Although regional hydrologic changes have been observed (for example, refs 1,3 5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate- change impacts3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the Northern Hemisphere, with areas of western North America, northeastern Europe and the Greater Himalaya showing the strongest emergence during the near- termdecadesandat2 Cglobalwarming.Theoccurrenceof extremely low snow years becomes widespread by the late twenty-first century, as do the occurrences of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the Northern Hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2 C above the pre-industrial baseline.
NASA Astrophysics Data System (ADS)
Qin, Chunbin; Zhang, Huaguang; Luo, Yanhong
2014-05-01
In this paper, a novel theoretic formulation based on adaptive dynamic programming (ADP) is developed to solve online the optimal tracking problem of the continuous-time linear system with unknown dynamics. First, the original system dynamics and the reference trajectory dynamics are transformed into an augmented system. Then, under the same performance index with the original system dynamics, an augmented algebraic Riccati equation is derived. Furthermore, the solutions for the optimal control problem of the augmented system are proven to be equal to the standard solutions for the optimal tracking problem of the original system dynamics. Moreover, a new online algorithm based on the ADP technique is presented to solve the optimal tracking problem of the linear system with unknown system dynamics. Finally, simulation results are given to verify the effectiveness of the theoretic results.
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.
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.
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.
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
The Global Challenge in Basic Education: Why Continued Investment in Basic Education Is Important
ERIC Educational Resources Information Center
Mertaugh, Michael T.; Jimenez, Emmanuel Y.; Patrinos, Harry A.
2009-01-01
This paper documents the importance of continued investment in basic education and argues that investments need to be carefully targeted to address the constraints that limit the coverage and quality of education if they are to provide expected benefits. Part I begins with a discussion of the returns to investment in education. Part II then…
The Global Challenge in Basic Education: Why Continued Investment in Basic Education Is Important
ERIC Educational Resources Information Center
Mertaugh, Michael T.; Jimenez, Emmanuel Y.; Patrinos, Harry A.
2009-01-01
This paper documents the importance of continued investment in basic education and argues that investments need to be carefully targeted to address the constraints that limit the coverage and quality of education if they are to provide expected benefits. Part I begins with a discussion of the returns to investment in education. Part II then…
ERIC Educational Resources Information Center
Ekanem, Ekpenyong E.; Ekpiken, William E.
2013-01-01
Continuous assessment is an important management tool for transforming university education. Although this policy employed measurable criteria to retain students' interest and objectivity, most academic staff of Nigerian universities lack basic knowledge and skills in test construction and interpretation and are thus, ineffective in continuous…
ERIC Educational Resources Information Center
Ekanem, Ekpenyong E.; Ekpiken, William E.
2013-01-01
Continuous assessment is an important management tool for transforming university education. Although this policy employed measurable criteria to retain students' interest and objectivity, most academic staff of Nigerian universities lack basic knowledge and skills in test construction and interpretation and are thus, ineffective in continuous…
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.
NASA Astrophysics Data System (ADS)
Shaltev, M.
2016-02-01
The search for continuous gravitational waves in a wide parameter space at a fixed computing cost is most efficiently done with semicoherent methods, e.g., StackSlide, due to the prohibitive computing cost of the fully coherent search strategies. Prix and Shaltev [Phys. Rev. D 85, 084010 (2012)] have developed a semianalytic method for finding optimal StackSlide parameters at a fixed computing cost under ideal data conditions, i.e., gapless data and a constant noise floor. In this work, we consider more realistic conditions by allowing for gaps in the data and changes in the noise level. We show how the sensitivity optimization can be decoupled from the data selection problem. To find optimal semicoherent search parameters, we apply a numerical optimization using as an example the semicoherent StackSlide search. We also describe three different data selection algorithms. Thus, the outcome of the numerical optimization consists of the optimal search parameters and the selected data set. We first test the numerical optimization procedure under ideal conditions and show that we can reproduce the results of the analytical method. Then we gradually relax the conditions on the data and find that a compact data selection algorithm yields higher sensitivity compared to a greedy data selection procedure.
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.
Evolutionary Optimization of Non-Continuous and Non-Sinusoidal Gaits of a Self-Propelled Swimmer
NASA Astrophysics Data System (ADS)
Ayancik, Fatma; Akoz, Emre; Moored, Keith
2016-11-01
Animals propel themselves through the oceans with a wide variety of swimming gaits. However, it is typically assumed that biological propulsion is achieved by using continuous, sinusoidal motions. Yet, animals have been observed using non-continuous or intermittent swimming gaits and at many times non-sinusoidal motions. Through the use of an evolutionary algorithm, optimal swimming gaits that can be both nonsinusoidal and intermittent are determined. Both the non-dimensional cost of transport and swimming speed are optimized for a virtual body combined with a two-dimensional self-propelled pitching and heaving foil within a boundary element method numerical framework. Nonsinusoidal motions are varied from a triangle-wave to a square-wave motion and the intermittency of the gait is varied by changing the duty cycle of the active phase to the coasting phase during swimming. Both pure pitching, and combined heaving and pitching motions are examined. The Pareto front of optimal solutions is investigated for trends in the optimally efficient swimming gait as the swimming speed is increased. The variation in the wake structures produced by optimally efficient swimmers is probed. Supported by the Office of Naval Research under Program Director Dr. Bob Brizzolara, MURI Grant Number N00014-14-1-0533.
A continuous linear optimal transport approach for pattern analysis in image datasets
Kolouri, Soheil; Tosun, Akif B.; Ozolek, John A.; Rohde, Gustavo K.
2015-01-01
We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embedding for the images. Hence, it enables shape and appearance modeling using linear geometric analysis techniques in the embedded space. In contrast to previous work, we use Monge's formulation of the optimal transport problem, which allows for reasonably fast computation of the linearized optimal transport embedding for large images. We demonstrate the application of the method to recover and visualize meaningful variations in a supervised-learning setting on several image datasets, including chromatin distribution in the nuclei of cells, galaxy morphologies, facial expressions, and bird species identification. We show that the new approach allows for high-resolution construction of modes of variations and discrimination and can enhance classification accuracy in a variety of image discrimination problems. PMID:26858466
NASA Technical Reports Server (NTRS)
Kurits, Inna; Lewis, M. J.; Hamner, M. P.; Norris, Joseph D.
2007-01-01
Heat transfer rates are an extremely important consideration in the design of hypersonic vehicles such as atmospheric reentry vehicles. This paper describes the development of a data reduction methodology to evaluate global heat transfer rates using surface temperature-time histories measured with the temperature sensitive paint (TSP) system at AEDC Hypervelocity Wind Tunnel 9. As a part of this development effort, a scale model of the NASA Crew Exploration Vehicle (CEV) was painted with TSP and multiple sequences of high resolution images were acquired during a five run test program. Heat transfer calculation from TSP data in Tunnel 9 is challenging due to relatively long run times, high Reynolds number environment and the desire to utilize typical stainless steel wind tunnel models used for force and moment testing. An approach to reduce TSP data into convective heat flux was developed, taking into consideration the conditions listed above. Surface temperatures from high quality quantitative global temperature maps acquired with the TSP system were then used as an input into the algorithm. Preliminary comparison of the heat flux calculated using the TSP surface temperature data with the value calculated using the standard thermocouple data is reported.
Occupational exposure limits in Europe and Asia--continued divergence or global harmonization?
Ding, Qian; Schenk, Linda; Malkiewicz, Katarzyna; Hansson, Sven Ove
2011-12-01
Occupational exposure limits (OELs) are used as a risk management tool aiming at protecting against negative health effects of occupational exposure to harmful substances. The systems of OEL development have not been standardized and divergent outcomes have been reported. However some harmonization processes have been initiated, primarily in Europe. This study investigates the state of harmonization in a global context. The OEL systems of eight Asian and seventeen European organizations are analyzed with respect to similarities and differences in: (1) the system for determining OELs, (2) the selection of substances, and (3) the levels of the OELs. The majority of the investigated organizations declare themselves to have been influenced by the American Conference of Governmental Industrial Hygienists (ACGIH), and in many cases this can be empirically confirmed. The EU harmonization process is reflected in trends towards convergence within the EU. However, comparisons of Asian and European organizations provide no obvious evidence that OELs are becoming globally harmonized. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
Continuing professional development systems for medical physicists: a global survey and analysis.
Round, W Howell
2013-05-01
Continuing professional development (CPD) and continuing professional education (CPE) are seen as being necessary for medical physicists to ensure that they are up-to-date with current clinical practice. CPD is more than just continuing professional education, but can include research publication, working group contribution, thesis examination and many other activities. A systematic way of assessing and recording such activities that a medical physicist undertakes is used in a number of countries. This can be used for certification and licensing renewal purposes. Such systems are used in 27 countries, but they should be implemented in all countries where clinical medical physicists are employed. A survey of the CPD systems that are currently operated around the world is presented. In general they are quite similar although there are a few countries that have CPD systems that differ significantly from the others in many respects. Generally they ensure that medical physicists are kept up-to-date, although there are some that clearly will fail to achieve that. An analysis of what is required to construct a useful medical physics CPD system is made. Finally, the need for medical physicist professional organizations to cooperate and share in the production and distribution of CPD and CPE materials is emphasized. Copyright © 2012 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Choice of Technique in a Continuous Time Infinite Horizon Optimal Growth Model.
1981-06-01
C., "Continuous Programming, Part One: Linear Objective," Journal of Mathematical Analysis and Applications , Vol. 28, No. 1 (1969). [3] Hanson, M., "A...Class of Continuous Convex Programming Problems," Journal of Mathematical Analysis and Applications , Vol. 22, pp. 427-437 (1968). [41 Lehman, R. S
Synthesis of Optimal Digital Controller for Continuous-Data Model-Following.
1983-01-01
Synthesis. 20. ABSTRACT (Continue on reverse side if necesaer mind Identify by block number) In converting a continuous-data (analog) controller into a...OBSOLETE UNCLASSIFIED 80 o 0 UCURITY CLASSIFICATION Of THIS PAGE (When Doafta e83 06 10 UNCLASSIFIED SUCURiTY CLASIFICATION oir Tis PAGIZ(Wen Dat nered
NASA Astrophysics Data System (ADS)
Defries, Ruth S.; Field, Christopher B.; Fung, Inez; Justice, Christopher O.; Los, Sietse; Matson, Pamela A.; Matthews, Elaine; Mooney, Harold A.; Potter, Christopher S.; Prentice, Katharine; Sellers, Piers J.; Townshend, John R. G.; Tucker, Compton J.; Ustin, Susan L.; Vitousek, Peter M.
1995-10-01
Global land surface characteristics are important boundary conditions for global models that describe exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere. Existing data sets of global land cover are based on classification schemes that characterize each grid cell as a discrete vegetation type. Consequently, parameter fields derived from these data sets are dependent on the particular scheme and the number of vegetation types it includes. The functional controls on exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere are now well enough understood that it is increasingly feasible to model these exchanges using a small number of vegetation characteristics that either are related to or closely related to the functional controls. Ideally, these characteristics would be mapped as continuous distributions to capture mixtures and gradients in vegetation within the cell size of the model. While such an approach makes it more difficult to build models from detailed observations at a small number of sites, it increases the potential for capturing functionally important variation within, as well as between, vegetation types. Globally, the vegetation characteristics that appear to be most important in controlling fluxes of water, energy, and carbon dioxide include (1) growth form (tree, shrub, herb), (2) seasonality of woody vegetation (deciduous, evergreen), (3) leaf type (broadleaf, coniferous), (4) photosynthetic pathway of nonwoody vegetation (C3, C4), (5) longevity (annual, perennial), and (6) type and intensity of disturbance (e.g., cultivation, fire history). Many of these characteristics can be obtained through remote sensing, though some require ground-based information. The minimum number and the identity of the required land surface characteristics almost certainly vary with the intended objective, but the philosophy of driving models with continuous distributions of a small number of land surface
Optimal experimental designs for dose-response studies with continuous endpoints.
Holland-Letz, Tim; Kopp-Schneider, Annette
2015-11-01
In most areas of clinical and preclinical research, the required sample size determines the costs and effort for any project, and thus, optimizing sample size is of primary importance. An experimental design of dose-response studies is determined by the number and choice of dose levels as well as the allocation of sample size to each level. The experimental design of toxicological studies tends to be motivated by convention. Statistical optimal design theory, however, allows the setting of experimental conditions (dose levels, measurement times, etc.) in a way which minimizes the number of required measurements and subjects to obtain the desired precision of the results. While the general theory is well established, the mathematical complexity of the problem so far prevents widespread use of these techniques in practical studies. The paper explains the concepts of statistical optimal design theory with a minimum of mathematical terminology and uses these concepts to generate concrete usable D-optimal experimental designs for dose-response studies on the basis of three common dose-response functions in toxicology: log-logistic, log-normal and Weibull functions with four parameters each. The resulting designs usually require control plus only three dose levels and are quite intuitively plausible. The optimal designs are compared to traditional designs such as the typical setup of cytotoxicity studies for 96-well plates. As the optimal design depends on prior estimates of the dose-response function parameters, it is shown what loss of efficiency occurs if the parameters for design determination are misspecified, and how Bayes optimal designs can improve the situation.
Prusa, Joseph
2012-05-08
This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the physics of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer- reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited.
NASA Astrophysics Data System (ADS)
Hafner, K.; Davis, J. P.; Wilson, D.; Woodward, R.
2015-12-01
The Global Seismographic Network (GSN) is a 151 station, globally distributed permanent network of state-of-the-art seismological and geophysical sensors that is a result of an ongoing successful partnership between IRIS, the USGS, the University of California at San Diego, NSF and numerous host institutions worldwide. In recent years, the GSN has standardized their dataloggers to the Quanterra Q330HR data acquisition system at all but three stations. Current equipment modernization efforts are focused on the development of a new very broadband borehole sensor to replace failing KS-54000 instruments and replacing the aging Streckeisen STS-1 surface instruments at many GSN stations. Aging of GSN equipment and discoveries of quality problems with GSN data (e.g., the long period response of the STS-1 sensors) have resulted in the GSN placing major emphasis on quantifying, validating and maintaining data quality. This has resulted in the implementation of MUSTANG and DQA systems for analyzing GSN data quality and enables both network operators and data end users to quickly characterize the performance of stations and networks. We will present summary data quality metrics for the GSN as obtained via these quality assurance tools. Data from the GSN are used not only for research, but on a daily basis are part of the operational missions of the USGS NEIC, NOAA tsunami warning centers, the Comprehensive Nuclear-Test-Ban-Treaty Organization as well as other organizations. The primary challenges for the GSN include maintaining these operational capabilities while simultaneously developing and replacing the primary borehole sensors, replacing as needed the primary vault sensors, maintaining high quality data and repairing station infrastructure, all during a period of very tight federal budgets. We will provide an overview of the operational status of the GSN, with a particular emphasis on the status of the primary borehole and vault sensors.
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 Astrophysics Data System (ADS)
Walsh, Jonathan A.; Romano, Paul K.; Forget, Benoit; Smith, Kord S.
2015-11-01
In this work we propose, implement, and test various optimizations of the typical energy grid-cross section pair lookup algorithm in Monte Carlo particle transport codes. The key feature common to all of the optimizations is a reduction in the length of the vector of energies that must be searched when locating the index of a particle's current energy. Other factors held constant, a reduction in energy vector length yields a reduction in CPU time. The computational methods we present here are physics-informed. That is, they are designed to utilize the physical information embedded in a simulation in order to reduce the length of the vector to be searched. More specifically, the optimizations take advantage of information about scattering kinematics, neutron cross section structure and data representation, and also the expected characteristics of a system's spatial flux distribution and energy spectrum. The methods that we present are implemented in the OpenMC Monte Carlo neutron transport code as part of this work. The gains in computational efficiency, as measured by overall code speedup, associated with each of the optimizations are demonstrated in both serial and multithreaded simulations of realistic systems. Depending on the system, simulation parameters, and optimization method employed, overall code speedup factors of 1.2-1.5, relative to the typical single-nuclide binary search algorithm, are routinely observed.
Comparison of batch and continuous multi-column protein A capture processes by optimal design.
Baur, Daniel; Angarita, Monica; Müller-Späth, Thomas; Steinebach, Fabian; Morbidelli, Massimo
2016-07-01
Multi-column capture processes show several advantages compared to batch capture. It is however not evident how many columns one should use exactly. To investigate this issue, twin-column CaptureSMB, 3- and 4-column periodic counter-current chromatography (PCC) and single column batch capture are numerically optimized and compared in terms of process performance for capturing a monoclonal antibody using protein A chromatography. Optimization is carried out with respect to productivity and capacity utilization (amount of product loaded per cycle compared to the maximum amount possible), while keeping yield and purity constant. For a wide range of process parameters, all three multi-column processes show similar maximum capacity utilization and performed significantly better than batch. When maximizing productivity, the CaptureSMB process shows optimal performance, except at high feed titers, where batch chromatography can reach higher productivity values than the multi-column processes due to the complete decoupling of the loading and elution steps, albeit at a large cost in terms of capacity utilization. In terms of trade-off, i.e. how much the capacity utilization decreases with increasing productivity, CaptureSMB is optimal for low and high feed titers, whereas the 3-column process is optimal in an intermediate region. Using these findings, the most suitable process can be chosen for different production scenarios.
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
Spotlight on Global Malnutrition: A Continuing Challenge in the 21st Century.
Steiber, Alison; Hegazi, Refaat; Herrera, Marianella; Zamor, Marie Landy; Chimanya, Kudakwashe; Pekcan, Ayla Gülden; Redondo-Samin, Divina Cristy D; Correia, Maria Isabel T D; Ojwang, Alice A
2015-08-01
Malnutrition as undernutrition, overnutrition, or an imbalance of specific nutrients, can be found in all countries and in both community and hospital settings around the world. The prevalence of malnutrition is unacceptably high in all settings and affects children, adolescents, pregnant women, and sick and older adults. Malnutrition has multiple underlying issues (food insecurity, chronic and acute illnesses, sanitation and safety, and aging in the community), which need to be addressed. At the same time, direct nutrition interventions (food supplements and micronutrient supplementation) help support immediate resolution of malnutrition. Awareness of malnutrition issues in the community and in clinical setting must be stimulated in order to provide better care. Different countries have implemented a wide range of interventions to prevent and treat malnutrition. These include nutrition education, engagement of the community, resolution of sanitation problems affecting food and water, routine screening and assessment and diagnosis of malnutrition (when feasible), and food supplements and micronutrients. Such programs are achieving improved outcomes; however, further engagement and training is needed for more community and clinical health workers. Many countries lack qualified nutrition and dietetics practitioners or have low dietitian-to-patient ratios with suboptimal salaries. Thus, an increase in number of and empowerment of nutrition and dietetics practitioners is desperately needed to help prevent and treat malnutrition globally.
Dill, K.A.; Phillips, A.T.; Rosen, J.B.
1997-12-01
Proteins require specific three-dimensional conformations to function properly. These {open_quotes}native{close_quotes} conformations result primarily from intramolecular interactions between the atoms in the macromolecule, and also intermolecular interactions between the macromolecule and the surrounding solvent. Although the folding process can be quite complex, the instructions guiding this process are specified by the one-dimensional primary sequence of the protein or nucleic acid: external factors, such as helper (chaperone) proteins, present at the time of folding have no effect on the final state of the protein. Many denatured proteins spontaneously refold into functional conformations once denaturing conditions are removed. Indeed, the existence of a unique native conformation, in which residues distant in sequence but close in proximity exhibit a densely packed hydrophobic core, suggests that this three-dimensional structure is largely encoded within the sequential arrangement of these specific amino acids. In any case, the native structure is often the conformation at the global minimum energy. In addition to the unique native (minimum energy) structure, other less stable structures exist as well, each with a corresponding potential energy. These structures, in conjunction with the native structure, make up an energy landscape that can be used to characterize various aspects of the protein structure. 22 refs., 10 figs., 2 tabs.
NASA Astrophysics Data System (ADS)
Schlutz, Juergen; Hufenbach, Bernhard; Laurini, Kathy; Spiero, Francois
2016-07-01
Future space exploration goals call for sending humans and robots beyond low Earth orbit and establishing sustained access to destinations such as the Moon, asteroids and Mars. Space agencies participating in the International Space Exploration Coordination Group (ISECG) are discussing an international approach for achieving these goals, documented in ISECG's Global Exploration Roadmap (GER). The GER reference scenario reflects a step-wise evolution of critical capabilities from ISS to missions in the lunar vicinity in preparation for the journey of humans to Mars. As ISECG agencies advance their individual planning, they also advance the mission themes and reference architecture of the GER to consolidate common goals, near-term mission scenarios and initial opportunities for collaboration. In this context, particular focus has been given to the Better understanding and further refinement of cislunar infrastructure and potential lunar transportation architecture Interaction with international science communities to identify and articulate the scientific opportunities of the near-term exploration mission themes Coordination and consolidation of interest in lunar polar volatiles prospecting and potential for in-situ resource utilisation Identification and articulation of the benefits from exploration and the technology transfer activities The paper discusses the ongoing roadmapping activity of the ISECG agencies. It provides an insight into the status of the above activities and an outlook towards the evolution of the GER that is currently foreseen in the 2017 timeframe.
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.
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.
NASA Astrophysics Data System (ADS)
Holzgräfe, Christian; Wallin, Stefan
2015-04-01
Recent design experiments have demonstrated that some proteins can switch their folds in response to a small number of point mutations either directly, in a single mutational step, or via intermediate bistable sequences, which populate two different folds simultaneously. Here we explore the hypothesis that bistable intermediates are more common in switches between structurally similar folds while direct switches are more common between dissimilar folds. To this end, we use a reduced model with seven atoms per amino acid and three amino acid types as a biophysical basis for protein folding and stability. We compare a set of mutational pathways, selected for optimal stability properties, that lead to switches between β-hairpin and α-helix folds with 16 amino acids and between α +2β and 2α folds with 35 amino acids, respectively. Fold switching in each case is sharp, taking only a few mutations to be completed. While the sharpness of mutationally driven protein fold switching can be traced to a shift in the energy balance of the two native states, conformational entropy contributes to determining the point at which fold switching occurs along a pathway.
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 rea