A GPS-Based Pitot-Static Calibration Method Using Global Output-Error Optimization
Foster, John V.; Cunningham, Kevin
2010-01-01
Pressure-based airspeed and altitude measurements for aircraft typically require calibration of the installed system to account for pressure sensing errors such as those due to local flow field effects. In some cases, calibration is used to meet requirements such as those specified in Federal Aviation Regulation Part 25. Several methods are used for in-flight pitot-static calibration including tower fly-by, pacer aircraft, and trailing cone methods. In the 1990 s, the introduction of satellite-based positioning systems to the civilian market enabled new inflight calibration methods based on accurate ground speed measurements provided by Global Positioning Systems (GPS). Use of GPS for airspeed calibration has many advantages such as accuracy, ease of portability (e.g. hand-held) and the flexibility of operating in airspace without the limitations of test range boundaries or ground telemetry support. The current research was motivated by the need for a rapid and statistically accurate method for in-flight calibration of pitot-static systems for remotely piloted, dynamically-scaled research aircraft. Current calibration methods were deemed not practical for this application because of confined test range size and limited flight time available for each sortie. A method was developed that uses high data rate measurements of static and total pressure, and GPSbased ground speed measurements to compute the pressure errors over a range of airspeed. The novel application of this approach is the use of system identification methods that rapidly compute optimal pressure error models with defined confidence intervals in nearreal time. This method has been demonstrated in flight tests and has shown 2- bounds of approximately 0.2 kts with an order of magnitude reduction in test time over other methods. As part of this experiment, a unique database of wind measurements was acquired concurrently with the flight experiments, for the purpose of experimental validation of the
Wang, Liping; Wang, Boquan; Zhang, Pu; Liu, Minghao; Li, Chuangang
2017-06-01
The study of reservoir deterministic optimal operation can improve the utilization rate of water resource and help the hydropower stations develop more reasonable power generation schedules. However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules. In this paper, output error generated by the uncertainty of the forecasting inflow was regarded as a variable to develop a short-term reservoir optimal operation model for reducing operation risk. To accomplish this, the concept of Value at Risk (VaR) was first applied to present the maximum possible loss of power generation schedules, and then an extreme value theory-genetic algorithm (EVT-GA) was proposed to solve the model. The cascade reservoirs of Yalong River Basin in China were selected as a case study to verify the model, according to the results, different assurance rates of schedules can be derived by the model which can present more flexible options for decision makers, and the highest assurance rate can reach 99%, which is much higher than that without considering output error, 48%. In addition, the model can greatly improve the power generation compared with the original reservoir operation scheme under the same confidence level and risk attitude. Therefore, the model proposed in this paper can significantly improve the effectiveness of power generation schedules and provide a more scientific reference for decision makers.
Ronaldo Vieira Cruz
2010-01-01
Full Text Available This article focuses on the problem of parameter estimation of the uncoupled, linear, short-period aerodynamic derivatives of a “Twin Squirrel” helicopter in level flight and constant speed. A flight test campaign is described with respect to maneuver specification, flight test instrumentation, and experimental data collection used to estimate the aerodynamic derivatives. The identification problem is solved in the time domain using the output-error approach, with a combination of Genetic Algorithm (GA and Levenberg-Marquardt optimization algorithms. The advantages of this hybrid GA and gradient-search methodology in helicopter system identification are discussed.
Output Error Method for Tiltrotor Unstable in Hover
Lichota Piotr
2017-03-01
Full Text Available This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.
Deterministic Global Optimization
Scholz, Daniel
2012-01-01
This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c. programming, and interval analysis.It also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the theoretical as well as from the empirical point of view. Furthermore, extensions of the prototype algorithm for multicriteria global optimization problems as well as mixed combinatorial optimization
Real-Time Parameter Estimation Using Output Error
Grauer, Jared A.
2014-01-01
Output-error parameter estimation, normally a post- ight batch technique, was applied to real-time dynamic modeling problems. Variations on the traditional algorithm were investigated with the goal of making the method suitable for operation in real time. Im- plementation recommendations are given that are dependent on the modeling problem of interest. Application to ight test data showed that accurate parameter estimates and un- certainties for the short-period dynamics model were available every 2 s using time domain data, or every 3 s using frequency domain data. The data compatibility problem was also solved in real time, providing corrected sensor measurements every 4 s. If uncertainty corrections for colored residuals are omitted, this rate can be increased to every 0.5 s.
MONOTONIZATION IN GLOBAL OPTIMIZATION
WU ZHIYOU; BAI FUSHENG; ZHANG LIANSHENG
2005-01-01
A general monotonization method is proposed for converting a constrained programming problem with non-monotone objective function and monotone constraint functions into a monotone programming problem. An equivalent monotone programming problem with only inequality constraints is obtained via this monotonization method. Then the existingconvexification and concavefication methods can be used to convert the monotone programming problem into an equivalent better-structured optimization problem.
Convex analysis and global optimization
Tuy, Hoang
2016-01-01
This book presents state-of-the-art results and methodologies in modern global optimization, and has been a staple reference for researchers, engineers, advanced students (also in applied mathematics), and practitioners in various fields of engineering. The second edition has been brought up to date and continues to develop a coherent and rigorous theory of deterministic global optimization, highlighting the essential role of convex analysis. The text has been revised and expanded to meet the needs of research, education, and applications for many years to come. Updates for this new edition include: · Discussion of modern approaches to minimax, fixed point, and equilibrium theorems, and to nonconvex optimization; · Increased focus on dealing more efficiently with ill-posed problems of global optimization, particularly those with hard constraints;
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.
Method of constrained global optimization
Altschuler, E.L.; Williams, T.J.; Ratner, E.R.; Dowla, F.; Wooten, F. (Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, California 94551 (United States) Department of Applied Physics, Stanford University, Stanford, California 94305 (United States) Department of Applied Science, University of California, Davis/Livermore, P.O. Box 808, Livermore, California 94551 (United States))
1994-04-25
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.
A Global Optimization Approach to Quantum Mechanics
Huang, Xiaofei
2006-01-01
This paper presents a global optimization approach to quantum mechanics, which describes the most fundamental dynamics of the universe. It suggests that the wave-like behavior of (sub)atomic particles could be the critical characteristic of a global optimization method deployed by nature so that (sub)atomic systems can find their ground states corresponding to the global minimum of some energy function associated with the system. The classic time-independent Schrodinger equation is shown to b...
Essays and surveys in global optimization
Audet, Charles; Savard, Giles
2005-01-01
Global optimization aims at solving the most general problems of deterministic mathematical programming. In addition, once the solutions are found, this methodology is also expected to prove their optimality. With these difficulties in mind, global optimization is becoming an increasingly powerful and important methodology. This book is the most recent examination of its mathematical capability, power, and wide ranging solutions to many fields in the applied sciences.
Introduction to Nonlinear and Global Optimization
Hendrix, E.M.T.; Tóth, B.
2010-01-01
This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization
Advances in stochastic and deterministic global optimization
Zhigljavsky, Anatoly; Žilinskas, Julius
2016-01-01
Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book. This volume is dedicated to the 70th birthday of Antanas Žilinskas who is a leading world expert in global optimization. Professor Žilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and mu...
Global optimality of extremals: An example
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 new closed-loop output error method for parameter identification of robot dynamics
Gautier, Maxime; Vandanjon, Pierre-Olivier
2010-01-01
Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method requires only the joint force/torque measurement. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference t...
Global Optimization by Energy Landscape Paving
Wille, L T; Wille, Luc T.
2002-01-01
We introduce a novel heuristic global optimization method, energy landscape paving (ELP), which combines core ideas from energy surface deformation and tabu search. In appropriate limits, ELP reduces to existing techniques. The approach is very general and flexible and is illustrated here on two protein folding problems. For these examples, the technique gives faster convergence to the global minimum than previous approaches.
A Controlled Particle Filter for Global Optimization
Zhang, Chi; Taghvaei, Amirhossein; Mehta, Prashant G.
2017-01-01
A particle filter is introduced to numerically approximate a solution of the global optimization problem. The theoretical significance of this work comes from its variational aspects: (i) the proposed particle filter is a controlled interacting particle system where the control input represents the solution of a mean-field type optimal control problem; and (ii) the associated density transport is shown to be a gradient flow (steepest descent) for the optimal value function, with respect to th...
FPSO Global Strength and Hull Optimization
Junyuan Ma; Jianhua Xiao; Rui Ma; Kai Cao
2014-01-01
Global strength is a significant item for floating production storage and offloading (FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.
Modified evolutionary algorithm for global optimization
郭崇慧; 陆玉昌; 唐焕文
2004-01-01
A modification of evolutionary programming or evolution strategies for n-dimensional global optimization is proposed. Based on the ergodicity and inherent-randomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase Ⅰ. Adjustment strategy of step-length and intensive searches in Phase Ⅱ are employed.The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm
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.
Evolutionary global optimization, manifolds and applications
Aguiar e Oliveira Junior, Hime
2016-01-01
This book presents powerful techniques for solving global optimization problems on manifolds by means of evolutionary algorithms, and shows in practice how these techniques can be applied to solve real-world problems. It describes recent findings and well-known key facts in general and differential topology, revisiting them all in the context of application to current optimization problems. Special emphasis is put on game theory problems. Here, these problems are reformulated as constrained global optimization tasks and solved with the help of Fuzzy ASA. In addition, more abstract examples, including minimizations of well-known functions, are also included. Although the Fuzzy ASA approach has been chosen as the main optimizing paradigm, the book suggests that other metaheuristic methods could be used as well. Some of them are introduced, together with their advantages and disadvantages. Readers should possess some knowledge of linear algebra, and of basic concepts of numerical analysis and probability theory....
Global 4-D trajectory optimization for spacecraft
无
2010-01-01
Global 4-D trajectory(x,y,z,t)is optimized for a spacecraft,which is launched from the Earth to fly around the Sun,just as star-drift of 1437 asteroids in the solar system.The spacecraft trajectory is controlled by low thrust.The performance index of optimal trajectory is to maximize the rendezvous times with the intermediate asteroids,and also maximize the final mass.This paper provides a combined algorithm of global 4-D trajectory optimization.The algorithm is composed of dynamic programming and two-point-boundary algorithm based on optimal control theory.The best 4-D trajectory is obtained:the spacecraft flies passing 55 asteroids,and rendezvous with(following or passing again)asteroids for 454 days,and finally rendezvous with the asteroid 2005SN25 on the day 60521(MJD),the final mass of the spacecraft is 836.53 kg.
Extended Global Convergence Framework for Unconstrained Optimization
(A)rpád B(U)RMEN; Franc BRATKOVI(C); Janez PUHAN; Iztok FAJFAR; Tadej TUMA
2004-01-01
An extension of the global convergence framework for unconstrained derivative-free optimization methods is presented. The extension makes it possible for the framework to include optimization methods with varying cardinality of the ordered direction set. Grid-based search methods are shown to be a special case of the more general extended global convergence framework. Furthermore,the required properties of the sequence of ordered direction sets listed in the definition of grid-based methods are re]axed and simplified by removing the requirement of structural equivalence.
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.
Conference on Convex Analysis and Global Optimization
Pardalos, Panos
2001-01-01
There has been much recent progress in global optimization algo rithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. Convex analysis plays a fun damental role in the analysis and development of global optimization algorithms. This is due essentially to the fact that virtually all noncon vex optimization problems can be described using differences of convex functions and differences of convex sets. A conference on Convex Analysis and Global Optimization was held during June 5 -9, 2000 at Pythagorion, Samos, Greece. The conference was honoring the memory of C. Caratheodory (1873-1950) and was en dorsed by the Mathematical Programming Society (MPS) and by the Society for Industrial and Applied Mathematics (SIAM) Activity Group in Optimization. The conference was sponsored by the European Union (through the EPEAEK program), the Department of Mathematics of the Aegean University and the Center for Applied Optimization of the University of Florida, by th...
Global Optimality of the Successive Maxbet Algorithm.
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)
Simulating Protein Conformations through Global Optimization
Mucherino, A; Pardalos, P M
2008-01-01
Many researches have been working on the protein folding problem from more than half century. Protein folding is indeed one of the major unsolved problems in science. In this work, we discuss a model for the simulation of protein conformations. This simple model is based on the idea of imposing few geometric requirements on chains of atoms representing the backbone of a protein conformation. The model leads to the formulation of a global optimization problem, whose solutions correspond to conformations satisfying the desired requirements. The global optimization problem is solved by the recently proposed Monkey Search algorithm. The simplicity of the optimization problem and the effectiveness of the used meta-heuristic search allowed the simulation of a large set of high-quality conformations. We show that, even though only few geometric requirements are imposed, some of the simulated conformation results to be similar (in terms of RMSD) to conformations real proteins actually have in nature.
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
Competing intelligent search agents in global optimization
Streltsov, S.; Vakili, P. [Boston Univ., MA (United States); Muchnik, I. [Rutgers Univ., Piscataway, NJ (United States)
1996-12-31
In this paper we present a new search methodology that we view as a development of intelligent agent approach to the analysis of complex system. The main idea is to consider search process as a competition mechanism between concurrent adaptive intelligent agents. Agents cooperate in achieving a common search goal and at the same time compete with each other for computational resources. We propose a statistical selection approach to resource allocation between agents that leads to simple and efficient on average index allocation policies. We use global optimization as the most general setting that encompasses many types of search problems, and show how proposed selection policies can be used to improve and combine various global optimization methods.
Solving global optimization problems on GPU cluster
Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya
2016-06-01
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.
Smoothing techniques for macromolecular global optimization
More, J.J.; Wu, Zhijun
1995-09-01
We study global optimization problems that arise in macromolecular modeling, and the solution of these problems via continuation and smoothing. Our results unify and extend the theory associated with the use of the Gaussian transform for smoothing. We show that the, Gaussian transform can be viewed as a special case of a generalized transform and that these generalized transforms share many of the properties of the Gaussian transform. We also show that the smoothing behavior of the generalized transform can be studied in terms of the Fourier transform and that these results indicate that the Gaussian transform has superior smoothing properties.
On Global Optimal Sailplane Flight Strategy
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.
Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems
Zahra Beheshti
2014-01-01
Full Text Available In recent years, particle swarm optimization (PSO has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20 unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.
A Survey on Meta-Heuristic Global Optimization Algorithms
Mohammad Khajehzadeh
2011-06-01
Full Text Available Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, metaheuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. In the present study, an attempt is made to review the most popular and well known metaheuristic global optimization algorithms introduced during the past decades.
Modified Grey Wolf Optimizer for Global Engineering Optimization
Nitin Mittal
2016-01-01
Full Text Available Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.
Global Optimization using Interval Analysis: Interval Optimization for Aerospace Applications
Van Kampen, E.
2010-01-01
Optimization is an important element in aerospace related research. It is encountered for example in trajectory optimization problems, such as: satellite formation flying, spacecraft re-entry optimization and airport approach and departure optimization; in control optimization, for example in adapti
Compact video synopsis via global spatiotemporal optimization.
Nie, Yongwei; Xiao, Chunxia; Sun, Hanqiu; Li, Ping
2013-10-01
Video synopsis aims at providing condensed representations of video data sets that can be easily captured from digital cameras nowadays, especially for daily surveillance videos. Previous work in video synopsis usually moves active objects along the time axis, which inevitably causes collisions among the moving objects if compressed much. In this paper, we propose a novel approach for compact video synopsis using a unified spatiotemporal optimization. Our approach globally shifts moving objects in both spatial and temporal domains, which shifting objects temporally to reduce the length of the video and shifting colliding objects spatially to avoid visible collision artifacts. Furthermore, using a multilevel patch relocation (MPR) method, the moving space of the original video is expanded into a compact background based on environmental content to fit with the shifted objects. The shifted objects are finally composited with the expanded moving space to obtain the high-quality video synopsis, which is more condensed while remaining free of collision artifacts. Our experimental results have shown that the compact video synopsis we produced can be browsed quickly, preserves relative spatiotemporal relationships, and avoids motion collisions.
4th International Conference on Frontiers in Global Optimization
Pardalos, Panos
2004-01-01
Global Optimization has emerged as one of the most exciting new areas of mathematical programming. Global optimization has received a wide attraction from many fields in the past few years, due to the success of new algorithms for addressing previously intractable problems from diverse areas such as computational chemistry and biology, biomedicine, structural optimization, computer sciences, operations research, economics, and engineering design and control. This book contains refereed invited papers submitted at the 4th international confer ence on Frontiers in Global Optimization held at Santorini, Greece during June 8-12, 2003. Santorini is one of the few sites of Greece, with wild beauty created by the explosion of a volcano which is in the middle of the gulf of the island. The mystic landscape with its numerous mult-extrema, was an inspiring location particularly for researchers working on global optimization. The three previous conferences on "Recent Advances in Global Opti mization", "State-of-the-...
3rd World Congress on Global Optimization in Engineering & Science
Ruan, Ning; Xing, Wenxun; WCGO-III; Advances in Global Optimization
2015-01-01
This proceedings volume addresses advances in global optimization—a multidisciplinary research field that deals with the analysis, characterization, and computation of global minima and/or maxima of nonlinear, non-convex, and nonsmooth functions in continuous or discrete forms. The volume contains selected papers from the third biannual World Congress on Global Optimization in Engineering & Science (WCGO), held in the Yellow Mountains, Anhui, China on July 8-12, 2013. The papers fall into eight topical sections: mathematical programming; combinatorial optimization; duality theory; topology optimization; variational inequalities and complementarity problems; numerical optimization; stochastic models and simulation; and complex simulation and supply chain analysis.
An approximation based global optimization strategy for structural synthesis
Sepulveda, A. E.; Schmit, L. A.
1991-01-01
A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.
Global Optimization Problems in Optimal Design of Experiments in Regression Models
Boer, E.P.J.; Hendrix, E.M.T.
2000-01-01
In this paper we show that optimal design of experiments, a specific topic in statistics, constitutes a challenging application field for global optimization. This paper shows how various structures in optimal design of experiments problems determine the structure of corresponding challenging global
Microwave tomography global optimization, parallelization and performance evaluation
Noghanian, Sima; Desell, Travis; Ashtari, Ali
2014-01-01
This book provides a detailed overview on the use of global optimization and parallel computing in microwave tomography techniques. The book focuses on techniques that are based on global optimization and electromagnetic numerical methods. The authors provide parallelization techniques on homogeneous and heterogeneous computing architectures on high performance and general purpose futuristic computers. The book also discusses the multi-level optimization technique, hybrid genetic algorithm and its application in breast cancer imaging.
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.
A LEVEL-VALUE ESTIMATION METHOD FOR SOLVING GLOBAL OPTIMIZATION
WU Dong-hua; YU Wu-yang; TIAN Wei-wen; ZHANG Lian-sheng
2006-01-01
A level-value estimation method was illustrated for solving the constrained global optimization problem. The equivalence between the root of a modified variance equation and the optimal value of the original optimization problem is shown. An alternate algorithm based on the Newton's method is presented and the convergence of its implementable approach is proved. Preliminary numerical results indicate that the method is effective.
Udink ten Cate, A.J.
1985-01-01
Discrete-time least-squares algorithms for recursive parameter estimation have continuous-time counterparts, which minimize a quadratic functional. The continuous-time algorithms can also include (in)equality constraints. Asymptotic convergence is demonstrated by means of Lyapunov methods. The constrained algorithms are applied in a stabilized output error configuration for parameter estimation in stochastic linear systems.
Strategies for Global Optimization of Temporal Preferences
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.
Unification of Filled Function and Tunnelling Function in Global Optimization
Wei Wang; Yong-jian Yang; Lian-sheng Zhang
2007-01-01
In this paper, two auxiliary functions for global optimization are proposed. These two auxiliary functions possess all characters of tunnelling functions and filled functions under certain general assumptions.Thus, they can be considered as the unification of filled function and tunnelling function. Moreover, the process of tunneling or filling for global optimization can be unified as the minimization of such auxiliary functions.Result of numerical experiments shows that such two auxiliary functions are effective.
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Gradient-Based Cuckoo Search for Global Optimization
Seif-Eddeen K. Fateen
2014-01-01
Full Text Available One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.
A New Theoretical Framework for Analyzing Stochastic Global Optimization Algorithms
无
1999-01-01
In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.
A global optimality result using Geraghty type contraction
Binayak S. Choudhury
2014-07-01
Full Text Available In this paper we prove two proximity point results for finding the distance between two sets. Unlike the best approximation theorems they provide with globally optimal values. Here our approach is to reduce the problem to that of finding optimal approximate solutions of some fixed point equations. We use Geraghty type contractive inequalities in our theorem. Two illustrative examples are given.
Global Stiffness Optimization of Parallel Robots Using Kinetostatic Performance Indices
Zhang, Dan
2010-01-01
This chapter focused on the stiffness optimization of a spatial 5-DOF parallel manipulator. It is shown that the mean value and the standard deviation of the trace of the generalized compliance matrix can not only be used to characterize the kinetostatic behaviour of PKMs globally, but can be used for design optimization. This methodology paves the way for providing not only the effective guidance, but also a new approach of dimensional synthesis for the optimal design of general parallel mec...
Global optimization framework for solar building design
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.
AN OPTIMIZED GLOBAL SYNCHRONIZATION ON SDDCN
M.SHARANYA
2010-12-01
Full Text Available The complex networks have been gaining increasing research attention because of their potential applications in many real-worldsystems from a variety of fields such as biology, social systems, linguistic networks, and technological systems. In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete time stochastic complex networks with randomly occurred nonlinearities (RONs and time delays. The discrete-time complex networks under consideration are subject to: 1 stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; 2 stochastic disturbances that enter the coupling term, the delayed coupling term as well as the overall network; and 3 time delays that include both the discrete and distributed ones. Note that the newly introduced RONsand the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment. By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem. By employing a combination of the linear matrix inequality (LMI techniques, thefree-weighting matrix method and stochastic analysis theories, several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays. The criteria derived are characterized in terms of LMIs whose solution can be solved by utilizing the standard numerical software. While these solvers are significantly faster than classical convex optimization algorithms, it should be kept in mind that the complexity of LMI computations remains higher than that of solving, say, a Riccati equation. For instance, problems with a thousand design variables typically take over an hour on today
Global Optimization methods for Gravitational Lens Systems with Regularized Sources
Rogers, Adam
2012-01-01
Several approaches exist to model gravitational lens systems. In this study, we apply global optimization methods to find the optimal set of lens parameters using a genetic algorithm. We treat the full optimization procedure as a two-step process: an analytical description of the source plane intensity distribution is used to find an initial approximation to the optimal lens parameters. The second stage of the optimization uses a pixelated source plane with the semilinear method to determine an optimal source. Regularization is handled by means of an iterative method and the generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly used in standard image deconvolution problems. This approach simultaneously estimates the optimal regularization parameter and the number of degrees of freedom in the source. Using the GCV and UPRE functions we are able to justify an estimation of the number of source degrees of freedom found in previous work. We test our approach ...
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.
Wolf Pack Algorithm for Unconstrained Global Optimization
Hu-Sheng Wu
2014-01-01
Full Text Available The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA. Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Global metabolic optimality in the structure of the coronary arteries
Keelan, Jonathan; Hague, James P
2014-01-01
The structure of the large coronary arteries is both heritable and reasonably consistent between individuals, but the extent to which this results from evolutionary pressure towards an energy-efficient, globally-optimal, structure is unknown. We present an algorithm for the determination of an energetically globally optimal arterial tree in arbitrary tissue geometries. We demonstrate through application of the algorithm that it is possible to generate in-silico vasculatures that closely match porcine anatomical data on all length scales. We therefore conclude that evolutionary pressure has resulted in a near globally optimal structure of the larger coronary arteries. We also examine the effect of changing length scales, predicting that the structures of the coronary arteries can change from a meandering form for small animals to very straight vessels for large animals. The method presented here is not limited to hearts, and represents a major advance in modeling the arterial vasculature, that could have impor...
Deterministic global optimization an introduction to the diagonal approach
Sergeyev, Yaroslav D
2017-01-01
This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field. Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz derivatives defined over hyperintervals are examined. A class of algorithms using several Lipschitz constants is introduced which has its origins in the DIRECT (DIviding RECTangles) method. This new class is based on an efficient strategy that is applied for the search domain partitioning. In addition a survey on derivative free methods and methods using the first derivatives is given for both one-dimensional and multi-dimensional cases. Non-smooth and smooth minorants and acceleration techniques that can speed up several classes of global optimization methods with examples of applications and problems arising in numerical testing of global optimization algorithms are discussed...
Meningococcal conjugate vaccines: optimizing global impact
Terranella A
2011-09-01
Full Text Available Andrew Terranella1,2, Amanda Cohn2, Thomas Clark2 1Epidemic Intelligence Service, Division of Applied Sciences, Scientific Education and Professional Development Program Office, 2Meningitis and Vaccine Preventable Diseases Branch, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA Abstract: 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. Keywords: conjugate vaccine, meningitis, meningococcal vaccine, meningococcal disease
A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization
Ransikarn Ngambusabongsopa
2015-01-01
Full Text Available This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators. Three types of mutation operators (uniform, nonuniform, and polynomial were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimization algorithm for global numerical optimization. The optimal quality, convergence speed, and statistical hypothesis testing of our algorithm are superior to those previous high performance algorithms such as RCCRO, HP-CRO2, and OCRO.
Application of surrogate-based global optimization to aerodynamic design
Pérez, Esther
2016-01-01
Aerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive – this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations. The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques. In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogat...
Differential evolution algorithm for global optimizations in nuclear physics
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.
A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION
ZHAO Xinchao
2005-01-01
The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
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.
Modified constrained differential evolution for solving nonlinear global optimization problems
2013-01-01
Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforward issue. Differential evolut...
Global Optimization Using Diffusion Perturbations with Large Noise Intensity
G. Yin; K. Yin
2006-01-01
This work develops an algorithm for global optimization. The algorithm is of gradient ascent type and uses random perturbations. In contrast to the annealing type procedures, the perturbation noise intensity is large. We demonstrate that by properly varying the noise intensity, approximations to the global maximum can be achieved. We also show that the expected time to reach the domain of attraction of the global maximum,which can be approximated by the solution of a boundary value problem, is finite. Discrete-time algorithms are proposed; recursive algorithms with occasional perturbations involving large noise intensity are developed.Numerical examples are provided for illustration.
Global Optimization Approach to Non-convex Problems
LU Zi-fang; ZHENG Hui-li
2004-01-01
A new approach to find the global optimal solution of the special non-convex problems is proposed in this paper. The non-convex objective problem is first decomposed into two convex sub-problems. Then a generalized gradient is introduced to determine a search direction and the evolution equation is built to obtain a global minimum point. By the approach, we can prevent the search process from some local minima and search a global minimum point. Two numerical examples are given to prove the approach to be effective.
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.
Global, Multi-Objective Trajectory Optimization With Parametric Spreading
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.
Global Local Structural Optimization of Transportation Aircraft Wings
Ciampa, P.D.; Nagel, B.; Van Tooren, M.J.L.
2010-01-01
The study presents a multilevel optimization methodology for the preliminary structural design of transportation aircraft wings. A global level is defined by taking into account the primary wing structural components (i.e., ribs, spars and skin) which are explicitly modeled by shell layered finite e
On the Investigation of Stochastic Global Optimization Algorithms
Baritompa, B.; Hendrix, E.M.T.
2005-01-01
This discussion paper for the SGO 2001 Workshop considers the process of investigating stochastic global optimization algorithms. It outlines a general plan for the systematic study of their behavior. It raises questions about performance criteria, characteristics of test cases and classification of
Global Optimal Trajectory in Chaos and NP-Hardness
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.
Global-local optimization of flapping kinematics in hovering flight
Ghommem, Mehdi
2013-06-01
The kinematics of a hovering wing are optimized by combining the 2-d unsteady vortex lattice method with a hybrid of global and local optimization algorithms. The objective is to minimize the required aerodynamic power under a lift constraint. The hybrid optimization is used to efficiently navigate the complex design space due to wing-wake interference present in hovering aerodynamics. The flapping wing is chosen so that its chord length and flapping frequency match the morphological and flight properties of two insects with different masses. The results suggest that imposing a delay between the different oscillatory motions defining the flapping kinematics, and controlling the way through which the wing rotates at the end of each half stroke can improve aerodynamic power under a lift constraint. Furthermore, our optimization analysis identified optimal kinematics that agree fairly well with observed insect kinematics, as well as previously published numerical results.
Examining the Bernstein global optimization approach to optimal power flow problem
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 Optimization of Nonlinear Blend-Scheduling Problems
Pedro A. Castillo Castillo
2017-04-01
Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
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.
Global Sufficient Optimality Conditions for a Special Cubic Minimization Problem
Xiaomei Zhang
2012-01-01
Full Text Available We present some sufficient global optimality conditions for a special cubic minimization problem with box constraints or binary constraints by extending the global subdifferential approach proposed by V. Jeyakumar et al. (2006. The present conditions generalize the results developed in the work of V. Jeyakumar et al. where a quadratic minimization problem with box constraints or binary constraints was considered. In addition, a special diagonal matrix is constructed, which is used to provide a convenient method for justifying the proposed sufficient conditions. Then, the reformulation of the sufficient conditions follows. It is worth noting that this reformulation is also applicable to the quadratic minimization problem with box or binary constraints considered in the works of V. Jeyakumar et al. (2006 and Y. Wang et al. (2010. Finally some examples demonstrate that our optimality conditions can effectively be used for identifying global minimizers of the certain nonconvex cubic minimization problem.
Endgame implementations for the Efficient Global Optimization (EGO) algorithm
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].
A. P. Karpenko
2014-01-01
Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
borealis - A generalized global update algorithm for Boolean optimization problems
Zhu, Zheng; Katzgraber, Helmut G
2016-01-01
Optimization problems with Boolean variables that fall into the nondeterministic polynomial (NP) class are of fundamental importance in computer science, mathematics, physics and industrial applications. Most notably, solving constraint-satisfaction problems, which are related to spin-glass-like Hamiltonians in physics, remains a difficult numerical task. As such, there has been great interest in designing efficient heuristics to solve these computationally difficult problems. Inspired by parallel tempering Monte Carlo in conjunction with the rejection-free isoenergetic cluster algorithm developed for Ising spin glasses, we present a generalized global update optimization heuristic that can be applied to different NP-complete problems with Boolean variables. The global cluster updates allow for a wide-spread sampling of phase space, thus considerably speeding up optimization. By carefully tuning the pseudo-temperature (needed to randomize the configurations) of the problem, we show that the method can efficie...
Neoliberal Optimism: Applying Market Techniques to Global Health.
Mei, Yuyang
2017-01-01
Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.
Automated parameterization of intermolecular pair potentials using global optimization techniques
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.
Solving Packing Problems by a Distributed Global Optimization Algorithm
Nian-Ze Hu
2012-01-01
Full Text Available Packing optimization problems aim to seek the best way of placing a given set of rectangular boxes within a minimum volume rectangular box. Current packing optimization methods either find it difficult to obtain an optimal solution or require too many extra 0-1 variables in the solution process. This study develops a novel method to convert the nonlinear objective function in a packing program into an increasing function with single variable and two fixed parameters. The original packing program then becomes a linear program promising to obtain a global optimum. Such a linear program is decomposed into several subproblems by specifying various parameter values, which is solvable simultaneously by a distributed computation algorithm. A reference solution obtained by applying a genetic algorithm is used as an upper bound of the optimal solution, used to reduce the entire search region.
Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization
Chao Zhang
2015-01-01
Full Text Available Parameter optimization for the conceptual rainfall-runoff (CRR model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA global optimization method was used to calibrate the Xinanjiang (XAJ model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.
Global optimization for multisensor fusion in seismic imaging
Barhen, J.; Protopopescu, V.; Reister, D. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research
1997-06-01
The accurate imaging of subsurface structures requires the fusion of data collected from large arrays of seismic sensors. The fusion process is formulated as an optimization problem and yields an extremely complex energy surface. Due to the very large number of local minima to be explored and escaped from, the seismic imaging problem has typically been tackled with stochastic optimization methods based on Monte Carlo techniques. Unfortunately, these algorithms are very cumbersome and computationally intensive. Here, the authors present TRUST--a novel deterministic algorithm for global optimization that they apply to seismic imaging. The excellent results demonstrate that TRUST may provide the necessary breakthrough to address major scientific and technological challenges in fields as diverse as seismic modeling, process optimization, and protein engineering.
Tabu search method with random moves for globally optimal design
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.
2008-01-01
In this paper, to overcome the drawbacks of POT which minimize the reminder, not only the optimal functional with minimal residual is put forward, but also the method of global optimization is used. Under the condition of large reminder, the optimal bases can also be obtained through the method of POTres without the approaching of initial conditions. In addition, compared with local optimization, the advantage of global optimization is remarkable. On the one hand, we can expect that the dynamical system based on the global optimal bases will include more information than the ones based on the local optimal bases, since the global optimal bases are much more precise than the local optimal bases. On the other hand, from the point of view of error, the global optimal bases are independent of the choice of the object functional and the initial bases.
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)
2000-01-01
Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design
A global optimization approach to multi-polarity sentiment analysis.
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
An Optimal Method for Developing Global Supply Chain Management System
Hao-Chun Lu
2013-01-01
Full Text Available Owing to the transparency in supply chains, enhancing competitiveness of industries becomes a vital factor. Therefore, many developing countries look for a possible method to save costs. In this point of view, this study deals with the complicated liberalization policies in the global supply chain management system and proposes a mathematical model via the flow-control constraints, which are utilized to cope with the bonded warehouses for obtaining maximal profits. Numerical experiments illustrate that the proposed model can be effectively solved to obtain the optimal profits in the global supply chain environment.
Efficient global optimization of a limited parameter antenna design
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.
Imperialist competitive algorithm combined with chaos for global optimization
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.
Variable Neighborhood Simplex Search Methods for Global Optimization Models
Pongchanun Luangpaiboon
2012-01-01
Full Text Available Problem statement: Many optimization problems of practical interest are encountered in various fields of chemical, engineering and management sciences. They are computationally intractable. Therefore, a practical algorithm for solving such problems is to employ approximation algorithms that can find nearly optimums within a reasonable amount of computational time. Approach: In this study the hybrid methods combining the Variable Neighborhood Search (VNS and simplexs family methods are proposed to deal with the global optimization problems of noisy continuous functions including constrained models. Basically, the simplex methods offer a search scheme without the gradient information whereas the VNS has the better searching ability with a systematic change of neighborhood of the current solution within a local search. Results: The VNS modified simplex method has a better searching ability for optimization problems with noise. The VNS modified simplex method also outperforms in average on the characteristics of intensity and diversity during the evolution of design point moving stage for the constrained optimization. Conclusion: The adaptive hybrid versions have proved to obtain significantly better results than the conventional methods. The amount of computation effort required for successful optimization is very sensitive to the rate of noise decrease of the process yields. Under circumstances of constrained optimization and gradually increasing the noise during an optimization the most preferred approach is the VNS modified simplex method.
Global optimization over linear constraint non-convex programming problem
ZHANG Gui-Jun; WU Ti-Huan; YE Rong; YANG Hai-qing
2005-01-01
A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programmin g problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
Zhijun Luo
2014-01-01
Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
Achtziger, Wolfgang; Stolpe, Mathias
2007-01-01
.e., a 0/1 problem. In contrast to the heuristic methods considered in many other approaches, our goal is to compute guaranteed globally optimal structures. This is done by a branch-and-bound method for which convergence can be proven. In this branch-and-bound framework, lower bounds of the optimal......-integer problems. The main intention of this paper is to provide optimal solutions for single and multiple load benchmark examples, which can be used for testing and validating other methods or heuristics for the treatment of this discrete topology design problem....
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 global optimization approach to multi-polarity sentiment analysis.
Xinmiao Li
Full Text Available 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
Designing Complex Interplanetary Trajectories for the Global Trajectory Optimization Competitions
Izzo, Dario; Simões, Luís F; Märtens, Marcus
2015-01-01
The design of interplanetary trajectories often involves a preliminary search for options that are later refined into one final selected trajectory. It is this broad search that, often being intractable, inspires the international event called Global Trajectory Optimization Competition. In the first part of this chapter, we introduce some fundamental problems of space flight mechanics, building blocks of any attempt to participate successfully in these competitions and we describe the use of the open source software PyKEP to assemble them into a final global solution strategy. In the second part, we formulate an instance of a multiple asteroid rendezvous problem, related to the 7th edition of the competition, and we show step by step how to build a possible solution strategy. We introduce two new techniques useful in the design of this particular mission type: the use of an asteroid phasing value and its surrogates and the efficient computation of asteroid clusters. We show how basic building blocks, sided to...
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).
Global Optimization for Transport Network Expansion and Signal Setting
Haoxiang Liu
2015-01-01
Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.
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.
A concept for global optimization of topology design problems
Stolpe, Mathias; Achtziger, Wolfgang; Kawamoto, Atsushi
2006-01-01
on two applications. The first application is the design of stiff truss structures where the bar areas are chosen from a finite set of available areas. The second considered application is simultaneous topology and geometry design of planar articulated mechanisms. For each application we outline......We present a concept for solving topology design problems to proven global optimality. We propose that the problems are modeled using the approach of simultaneous analysis and design with discrete design variables and solved with convergent branch and bound type methods. This concept is illustrated...
Simulated Annealing-Based Krill Herd Algorithm for Global Optimization
Gai-Ge Wang
2013-01-01
Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.
Vaughan, E. T.
1977-01-01
Program aids in equipment assessment. Independent assembly-language utility program is designed to operate under level 27 or 31 of EXEC 8 Operating System. It scans user-selected portions of system log file, whether located on tape or mass storage, and searches for and processes 1/0 error (type 6) entries.
A global optimization algorithm for protein surface alignment
Guerra Concettina
2010-09-01
Full Text Available Abstract Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results In this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP method for three-dimensional (3D shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites.
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.
Global Optimization for Advertisement Selection in Sponsored Search
崔卿; 白峰杉; 高斌; 刘铁岩
2015-01-01
Advertisement (ad) selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components (e.g., the auction mechanism) to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue (we call the combination of these ob jective functions as the marketplace ob jective for ease of reference). To this end, we 1) extract a bunch of features to represent each pair of query and keyword, and 2) train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace ob jective. This formalization seems quite natural; however, it is technically diﬃcult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.
CONVEXIFICATION AND CONCAVIFICATION METHODS FOR SOME GLOBAL OPTIMIZATION PROBLEMS
WU Zhiyou; ZHANG Liansheng; BAI Fusheng; YANG Xinmin
2004-01-01
In this paper, firstly, we propose several convexification and concavification transformations to convert a strictly monotone function into a convex or concave function,then we propose several convexification and concavification transformations to convert a non-convex and non-concave objective function into a convex or concave function in the programming problems with convex or concave constraint functions, and propose several convexification and concavification transformations to convert a non-monotone objective function into a convex or concave function in some programming problems with strictly monotone constraint functions. Finally, we prove that the original programming problem can be converted into an equivalent concave minimization problem, or reverse convex programming problem or canonical D.C. Programming problem. Then the global optimal solution of the original problem can be obtained by solving the converted concave minimization problem, or reverse convex programming problem or canonical D.C. Programming problem using the existing algorithms about them.
Finding dominant transition pathways via global optimization of action
Lee, Juyong; Joung, InSuk; Lee, Jooyoung; Brooks, Bernard R
2016-01-01
We present a new computational approach, Action-CSA, to sample multiple reaction pathways with fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing method. This approach successfully samples not only the most dominant pathway but also many other possible paths without initial guesses on reaction pathways. Pathway space is efficiently sampled by crossover operations of a set of paths and preserving the diversity of sampled pathways. The sampling ability of the approach is assessed by finding pathways for the conformational changes of alanine dipeptide and hexane. The benchmarks demonstrate that the rank order and the transition time distribution of multiple pathways identified by the new approach are in good agreement with those of long molecular dynamics simulations. We also show that the lowest action folding pathway of the mini-protein FSD-1 identified by the new approach is consistent with previous molecular dynamics simulations a...
Adjusting process count on demand for petascale global optimization
Sosonkina, Masha
2013-01-01
There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running at the petascale are likely to be memory intensive, which makes thrashing a serious problem for many petascale applications. One way to overcome this challenge is to use a dynamic number of processes, so that the total amount of memory available for the computation can be increased on demand. This paper describes modifications made to the massively parallel global optimization code pVTdirect in order to allow for a dynamic number of processes. In particular, the modified version of the code monitors memory use and spawns new processes if the amount of available memory is determined to be insufficient. The primary design challenges are discussed, and performance results are presented and analyzed.
Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging
Mehdad, E.; Kleijnen, Jack P.C.
2014-01-01
In this paper we investigate global optimization for black-box simulations using metamodels to guide this optimization. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic simulation we study the famous `efficient global optimization'
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.
Global path planning approach based on ant colony optimization algorithm
WEN Zhi-qiang; CAI Zi-xing
2006-01-01
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
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.
A practical globalization of one-shot optimization for optimal design of tokamak divertors
Blommaert, Maarten, E-mail: maarten.blommaert@kuleuven.be [Institute of Energy and Climate Research (IEK-4), FZ Jülich GmbH, D-52425 Jülich (Germany); Dekeyser, Wouter; Baelmans, Martine [KU Leuven, Department of Mechanical Engineering, 3001 Leuven (Belgium); Gauger, Nicolas R. [TU Kaiserslautern, Chair for Scientific Computing, 67663 Kaiserslautern (Germany); Reiter, Detlev [Institute of Energy and Climate Research (IEK-4), FZ Jülich GmbH, D-52425 Jülich (Germany)
2017-01-01
In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes only state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved.
Geophysical Inversion With Multi-Objective Global Optimization Methods
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be
Optimizing global liver function in radiation therapy treatment planning
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
Optimizing global liver function in radiation therapy treatment planning
Wu, Victor W; Epelman, Marina A; Wang, Hesheng; Romeijn, H Edwin; Feng, Mary; Cao, Yue; Haken, Randall K Ten; Matuszak, Martha M
2017-01-01
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 (ℓEUD) (conventional ‘ℓEUD model’), the so-called perfusion-weighted ℓEUD (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 ℓEUD, fEUD, and GLF plans delivering the same target ℓEUD 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%(7.5%) more liver function than the fEUD (ℓEUD) plan does in 2D cases, and up to 4.5%(5.6%) in 3D cases. The GLF and fEUD plans worsen in ℓEUD 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 often achieves better GLF than ℓEUD model optimization does, the GLF model directly optimizes a more clinically
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
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.
Phenology as a strategy for carbon optimality: a global model
S. Caldararu
2013-09-01
Full Text Available Phenology is essential to our understanding of biogeochemical cycles and the climate system. We develop a global mechanistic model of leaf phenology based on the hypothesis that phenology is a strategy for optimal carbon gain at the canopy level so that trees adjust leaf gains and losses in response to environmental factors such as light, temperature and soil moisture, to achieve maximum carbon assimilation. We fit this model to five years of satellite observations of leaf area index (LAI using a Bayesian fitting algorithm. We show that our model is able to reproduce phenological patterns for all vegetation types and use it to explore variations in growing season length and the climate factors that limit leaf growth for different biomes. Phenology in wet tropical areas is limited by leaf age physiological constraints while at higher latitude leaf seasonality is limited by low temperature and light availability. Leaf growth in grassland regions is limited by water availability but often in combination with other factors. This model will advance the current understanding of phenology for ecosystem carbon models and our ability to predict future phenological behaviour.
Automatic Construction and Global Optimization of a Multisentiment Lexicon
Xiaoping Yang
2016-01-01
Full Text Available 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.
杨璐鸿; 刘顺安; 张冠宇; 王春雪
2015-01-01
To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.
Non-linear Global Optimization using Interval Arithmetic and Constraint Propagation
Kjøller, Steffen; Kozine, Pavel; Madsen, Kaj;
2006-01-01
In this Chapter a new branch-and-bound method for global optimization is presented. The method combines the classical interval global optimization method with constraint propagation techniques. The latter is used for including solutions of the necessary condition f'(x)=0. The constraint propagation...
Feng Zou
2016-01-01
Full Text Available 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.
Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints
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.
Globally Optimal Path Planning with Anisotropic Running Costs
2013-03-01
Proceedings of the American Control Conference , pp...Jacques, D. R. & Pachter, M. (2002) Air vehicle optimal trajectories between two radars, in Proceedings of the American Control Conference . Pachter...M. & Hebert, J. (2001) Optimal aircraft trajectories for radar exposure mini- mization, in Proceedings of the American Control Conference .
Global Optimization strategies for two-mode clustering
J.M. van Rosmalen (Joost); P.J.F. Groenen (Patrick); J. Trejos (Javier); W. Castilli
2005-01-01
textabstractTwo-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k-means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various meth
无
2006-01-01
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
Wu Zhi-jian; Tang Zhi-long; Kang Li-shan
2003-01-01
This paper presents a parallel two level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions.By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optirma and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.
A GLOBALLY AND SUPERLINEARLY CONVERGENT TRUST REGION METHOD FOR LC1 OPTIMIZATION PROBLEMS
ZhangLiping; LaiYanlian
2001-01-01
Abstract. A new trust region algorithm for solving convex LC1 optimization problem is present-ed. It is proved that the algorithm is globally convergent and the rate of convergence is superlin-ear under some reasonable assumptions.
A FILTER-TRUST-REGION METHOD FOR LC1 UNCONSTRAINED OPTIMIZATION AND ITS GLOBAL CONVERGENCE
Zhenghao Yang; Wenyu Sun; Chuangyin Dang
2008-01-01
In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative.We establish the global convergence of the algorithm under reasonable assumptions.
A Global Optimal Coherence Method for Multi-baseline InSAR Elevation Inversion
HUA Fenfen
2015-11-01
Full Text Available A global optimal coherence method for elevation inversion from multi-baseline polarimetric InSAR data is proposed. The multi-baseline polarimetric InSAR data used in experiments were obtained by Chinese X-SAR system and Germany's E-SAR system. Through combining several full polarimetric InSAR images, the proposed method constructs the multi-baseline polarimetric InSAR coherency matrix, and solves the optimal interferograms under global optimal coherence criterion. The optimal interferograms generated by global optimal coherence method were used to calculate the elevation of target with multi-baseline InSAR elevation inversion method. The proposed method reduces the influence of different scattering centers effectively using multi-baseline InSAR, which improves the accuracy and reliability of the interferometric phase and eventually improves the accuracy of DEM. The results verify the validity of the proposed method.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
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...
Globally Optimal Segmentation of Permanent-Magnet Systems
Insinga, Andrea Roberto; Bjørk, Rasmus; Smith, Anders
2016-01-01
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...
Global Launcher Trajectory Optimization for Lunar Base Settlement
Pagano, A.; Mooij, E.
2010-01-01
The problem of a mission to the Moon to set a permanent outpost can be tackled by dividing the journey into three phases: the Earth ascent, the Earth-Moon transfer and the lunar landing. In this paper we present an optimization analysis of Earth ascent trajectories of existing launch vehicles inject
Gaviano, Marco; Lera, Daniela; Sergeyev, Yaroslav D
2011-01-01
A procedure for generating non-differentiable, continuously differentiable, and twice continuously differentiable classes of test functions for multiextremal multidimensional box-constrained global optimization and a corresponding package of C subroutines are presented. Each test class consists of 100 functions. Test functions are generated by defining a convex quadratic function systematically distorted by polynomials in order to introduce local minima. To determine a class, the user defines the following parameters: (i) problem dimension, (ii) number of local minima, (iii) value of the global minimum, (iv) radius of the attraction region of the global minimizer, (v) distance from the global minimizer to the vertex of the quadratic function. Then, all other necessary parameters are generated randomly for all 100 functions of the class. Full information about each test function including locations and values of all local minima is supplied to the user. Partial derivatives are also generated where possible.
Analysis and Improvement of TCP Congestion Control Mechanism Based on Global Optimization Model
无
2001-01-01
Network flow control is formulated as a global optimization problem of user profit. A general global optimization flow control model is established. This model combined with the stochastic model of TCP is used to study the global rate allocation characteristic of TCP. Analysis shows when active queue manage ment is used in network TCP rates tend to be allocated to maximize the aggregate of a user utility function Us (called Us fairness). The TCP throughput formula is derived. An improved TCP congestion control mecha nism is proposed. Simulations show its throughput is TCP friendly when competing with existing TCP and its rate change is smoother. Therefore, it is suitable to carry multimedia applications.
A Filled Function with Adjustable Parameters for Unconstrained Global Optimization
SHANGYou-lin; LIXiao-yan
2004-01-01
A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two adjustable parameters. We will discuss the properties of the proposed filled function. Conditions on this function and on the values of parameters are given so that the constructed function has the desired properties of traditional filled function.
Global stability and optimal control of an SIRS epidemic model on heterogeneous networks
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.
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
Xuan Nguyen
2012-06-01
Full Text Available Abstract Background Dynamic Bayesian network (DBN is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN. Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT. GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
An evolutionary algorithm for global optimization based on self-organizing maps
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.
MA Wei; WANG Zheng-Ou
2003-01-01
Since there were few chaotic neural networks applicable to the global optimization, in this paper, we proposea new neural network model - chaotic parameters disturbance annealing (CPDA) network, which is superior to otherexisting neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the presentCPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escapefrom the attraction of a local minimal solution and with the parameter p1 annealing, our model will converge to theglobal optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper.The benchmark examples show the present CPDA neuralnetwork's merits in nonlinear global optimization.
Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior.
Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron
2013-01-01
We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.
Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm
V. D. Sulimov
2014-01-01
Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search
A Global Optimization Algorithm for Sum of Linear Ratios Problem
Yuelin Gao
2013-01-01
Full Text Available We equivalently transform the sum of linear ratios programming problem into bilinear programming problem, then by using the linear characteristics of convex envelope and concave envelope of double variables product function, linear relaxation programming of the bilinear programming problem is given, which can determine the lower bound of the optimal value of original problem. Therefore, a branch and bound algorithm for solving sum of linear ratios programming problem is put forward, and the convergence of the algorithm is proved. Numerical experiments are reported to show the effectiveness of the proposed algorithm.
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.
Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks
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, local adaptation and the role of growth in distribution networks
Ronellenfitsch, Henrik
2016-01-01
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 non-convex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that natural selection slowly guides the network towards an optimized state. 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.
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.
Global Synthesis Method for the Optimization of Multifeed EBG Antennas
Julien Drouet
2008-01-01
Full Text Available This paper presents a novel technique for synthesizing a given radiation pattern from an EBG antenna with an array feed. The method determines the optimum sets of input waves and input impedances for the feed ports in order to perform simultaneously the radiation pattern and the impedance matching of all the radiating probes that form the array feed. The method is validated through a numerical design of an EBG antenna excited with four patch antennas. The structure is designed to radiate with a single lobe scanned at =30∘ in the E-plane. The interactions between each patch inside the EBG resonator are characterized with the CST MWS software. The optimum weights and the input impedances which simultaneously perform the objective radiation and the matching of all feeding ports are calculated by the developed global synthesis method. The feed network is designed with the Agilent ADS software in order to perform the specified weights and the impedances matching.
Vertical bifacial solar farms: Physics, design, and global optimization
Khan, M. Ryyan
2017-09-04
There have been sustained interest in bifacial solar cell technology since 1980s, with prospects of 30–50% increase in the output power from a stand-alone panel. Moreover, a vertical bifacial panel reduces dust accumulation and provides two output peaks during the day, with the second peak aligned to the peak electricity demand. Recent commercialization and anticipated growth of bifacial panel market have encouraged a closer scrutiny of the integrated power-output and economic viability of bifacial solar farms, where mutual shading will erode some of the anticipated energy gain associated with an isolated, single panel. Towards that goal, in this paper we focus on geography-specific optimization of ground-mounted vertical bifacial solar farms for the entire world. For local irradiance, we combine the measured meteorological data with the clear-sky model. In addition, we consider the effects of direct, diffuse, and albedo light. We assume the panel is configured into sub-strings with bypass-diodes. Based on calculated light collection and panel output, we analyze the optimum farm design for maximum yearly output at any given location in the world. Our results predict that, regardless of the geographical location, a vertical bifacial farm will yield 10–20% more energy than a traditional monofacial farm for a practical row-spacing of 2 m (corresponding to 1.2 m high panels). With the prospect of additional 5–20% energy gain from reduced soiling and tilt optimization, bifacial solar farm do offer a viable technology option for large-scale solar energy generation.
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.
Global Optimization for Sum of Linear Ratios Problem Using New Pruning Technique
2009-02-01
Full Text Available A global optimization algorithm is proposed for solving sum of general linear ratios problem (P using new pruning technique. Firstly, an equivalent problem (P1 of the (P is derived by exploiting the characteristics of linear constraints. Then, by utilizing linearization method the relaxation linear programming (RLP of the (P1 can be constructed and the proposed algorithm is convergent to the global minimum of the (P through the successive refinement of the linear relaxation of feasible region and solutions of a series of (RLP. Then, a new pruning technique is proposed, this technique offers a possibility to cut away a large part of the current investigated feasible region by the optimization algorithm, which can be utilized as an accelerating device for global optimization of problem (P. Finally, the numerical experiments are given to illustrate the feasibility of the proposed algorithm.
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
Leilei Cao
2016-01-01
Full Text Available 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.
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.
2015-09-24
19. Colloquium lecture at College of Management , National Chiao Tung University, June 22, 2012. Title: Unified Framework in Global Supply Chain and...the well-known logistic equation in population dynamical systems can be reformulated as a global optimization problem, which could have at most 2n...making, supply chain , scheduling problems, and computational mechanics, etc. Impacts to the communities: The canonical duality theory is now
GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM
Zhang Guijun; Wu Tihua; Ye Rong
2004-01-01
An extended crowding genetic algorithm (ECGA) is introduced for solving optimal pump configuration problem,which was presented by T.Westerlund in 1994.This problem has been found to be non-convex,and the objective function contained several local optima and global optimality could not be ensured by all the traditional MINLP optimization method.The concepts of species conserving and composite encoding are introduced to crowding genetic algorithm (CGA) for maintain the diversity of population more effectively and coping with the continuous and/or discrete variables in MINLP problem.The solution of three-levels pump configuration got from DICOPT++ software (OA algorithm) is also given.By comparing with the solutions obtained from DICOPT++,ECP method,and MIN-MIN method,the ECGA algorithm proved to be very effective in finding the global optimal solution of multi-levels pump configuration via using the problem-specific information.
Towards a Global Optimization Scheme for Multi-Band Speech Recognition
Cerisara, Christophe; Haton, Jean-Paul; Fohr, Dominique
1999-01-01
Colloque avec actes et comité de lecture.; n this paper, we deal with a new method to globally optimize a Multi-Band Speech Recognition (MBSR) system. We have tested our algorithm with the TIMIT database and obtained a significant improvement in the accuracy over a basic HMM system for clean speech. The goal of this work is not to prove the effectiveness of MBSR, what has yet been done, but to improve the training scheme by introducing a global optimization procedure. A consequence of this me...
A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions
Fowkes, Jaroslav M.
2012-06-21
We present a branch and bound algorithm for the global optimization of a twice differentiable nonconvex objective function with a Lipschitz continuous Hessian over a compact, convex set. The algorithm is based on applying cubic regularisation techniques to the objective function within an overlapping branch and bound algorithm for convex constrained global optimization. Unlike other branch and bound algorithms, lower bounds are obtained via nonconvex underestimators of the function. For a numerical example, we apply the proposed branch and bound algorithm to radial basis function approximations. © 2012 Springer Science+Business Media, LLC.
Conference on "State of the Art in Global Optimization : Computational Methods and Applications"
Pardalos, P
1996-01-01
Optimization problems abound in most fields of science, engineering, and technology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solvin...
Dong, Huachao; Song, Baowei; Wang, Peng; Huang, Shuai [Northwestern Polytechnical University, Xi' an (China)
2015-05-15
In this paper, a novel kriging-based algorithm for global optimization of computationally expensive black-box functions is presented. This algorithm utilizes a multi-start approach to find all of the local optimal values of the surrogate model and performs searches within the neighboring area around these local optimal positions. Compared with traditional surrogate-based global optimization method, this algorithm provides another kind of balance between exploitation and exploration on kriging-based model. In addition, a new search strategy is proposed and coupled into this optimization process. The local search strategy employs a kind of improved 'Minimizing the predictor' method, which dynamically adjusts search direction and radius until finds the optimal value. Furthermore, the global search strategy utilizes the advantage of kriging-based model in predicting unexplored regions to guarantee the reliability of the algorithm. Finally, experiments on 13 test functions with six algorithms are set up and the results show that the proposed algorithm is very promising.
An adaptive metamodel-based global optimization algorithm for black-box type problems
Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan
2015-11-01
In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization
Zongsheng Wu
2016-07-01
Full Text Available The Teaching-Learning-Based Optimization (TLBO algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.
Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods
Rogers, Adam; Safi-Harb, Samar; Fiege, Jason
2015-08-01
The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.
Long-term stability of the Tevatron by verified global optimization
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.
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.
On estimating workload in branch-and-bound global optimization algorithms
Berenguel, J.L.; Casado, L.G.; Garcia, I.; Hendrix, E.M.T.
2013-01-01
In general, solving Global Optimization (GO) problems by Branch-and-Bound (B&B) requires a huge computational capacity. Parallel execution is used to speed up the computing time. As in this type of algorithms, the foreseen computational workload (number of nodes in the B&B tree) changes
Decomposition method of complex optimization model based on global sensitivity analysis
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.
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.
Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes
Sheng Liu
2013-01-01
Full Text Available 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.
Lee, Juyong; Lee, Jooyoung; Brooks, Bernard R; Ahn, Yong-Yeol
2016-01-01
We investigate the possibility of global optimization-based overlapping community detection, using link community framework. We first show that partition density, the original quality function used in link community detection method, is not suitable as a quality function for global optimization because it prefers breaking communities into triangles except in highly limited conditions. We analytically derive those conditions and confirm it with computational results on direct optimization of various synthetic and real-world networks. To overcome this limitation, we propose alternative approaches combining the weighted line graph transformation and existing quality functions for node-based communities. We suggest a new line graph weighting scheme, a normalized Jaccard index. Computational results show that community detection using the weighted line graphs generated with the normalized Jaccard index leads to a more accurate community structure.
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.
Łukasz Kubuś
2015-08-01
Full Text Available Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs. EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI. EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA and other EAs such as Real Coded Genetic Algorithm (RCGA, elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.
Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization
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
Xiangzhu He
2016-01-01
Full Text Available 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.
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.
Cho, Su Gil; Jang, Jun Yong; Kim, Ji Hoon; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Lee, Min Uk [Romax Technology Ltd., Seoul (Korea, Republic of); Choi, Jong Su; Hong, Sup [Korea Research Institute of Ships and Ocean Engineering, Daejeon (Korea, Republic of)
2015-04-15
Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.
Weitian Lin
2014-01-01
Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.
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.
Optimal satellite sampling to resolve global-scale dynamics in the I-T system
Rowland, D. E.; Zesta, E.; Connor, H. K.; Pfaff, R. F., Jr.
2016-12-01
The recent Decadal Survey highlighted the need for multipoint measurements of ion-neutral coupling processes to study the pathways by which solar wind energy drives dynamics in the I-T system. The emphasis in the Decadal Survey is on global-scale dynamics and processes, and in particular, mission concepts making use of multiple identical spacecraft in low earth orbit were considered for the GDC and DYNAMIC missions. This presentation will provide quantitative assessments of the optimal spacecraft sampling needed to significantly advance our knowledge of I-T dynamics on the global scale.We will examine storm time and quiet time conditions as simulated by global circulation models, and determine how well various candidate satellite constellations and satellite schemes can quantify the plasma and neutral convection patterns and global-scale distributions of plasma density, neutral density, and composition, and their response to changes in the IMF. While the global circulation models are data-starved, and do not contain all the physics that we might expect to observe with a global-scale constellation mission, they are nonetheless an excellent "starting point" for discussions of the implementation of such a mission. The result will be of great utility for the design of future missions, such as GDC, to study the global-scale dynamics of the I-T system.
SGO: A fast engine for ab initio atomic structure global optimization by differential evolution
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.
Biwei Tang
2016-05-01
Full Text Available Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard. Particle swarm optimization (PSO has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO, is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self adaptive DE (RBSADE, is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints
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.
GFS algorithm based on batch Monte Carlo trials for solving global optimization problems
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.
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.
A Hybrid LBFGS-DE Algorithm for Global Optimization of the Lennard-Jones Cluster Problem
Ernesto Padernal Adorio
2004-12-01
Full Text Available The Lennard-Jones cluster conformation problem is to determine a configuration of n atoms in three-dimensional space where the sum of the nonlinear pairwise potential function is at a minimum. In this formula, ri,j is the distance between atoms i and j. This optimization problem is a severe test for global optimization algorithms due to its computational complexity: the number of local minima grows exponentially large as the number of atoms in the cluster is increased. As a specific test case, a better cluster configuration than the previously published putative minimum for the 38-atom case was found in the mid-1990s.
Global optimization of truss topology with discrete bar areas—Part I: Theory of relaxed problems
Achtziger, Wolfgang; Stolpe, Mathias
2008-01-01
. The main issue of the paper and of the approach lies in the fact that the relaxed nonlinear optimization problem can be formulated as a quadratic program (QP). Here the paper generalizes and extends the available theory from the literature. Although the Hessian of this QP is indefinite, it is possible...... to circumvent the non-convexity and to calculate global optimizers. Moreover, the QPs to be treated in the branch-and-bound search tree differ from each other just in the objective function. In Part I we give an introduction to the problem and collect all theory and related proofs for the treatment...
Global stability-based design optimization of truss structures using multiple objectives
Tugrul Talaslioglu
2013-02-01
This paper discusses the effect of global stability on the optimal size and shape of truss structures taking into account of a nonlinear critical load, truss weight and serviceability at the same time. The nonlinear critical load is computed by arc-length method. In order to increase the accuracy of the estimation of critical load (ignoring material nonlinearity), an eigenvalue analysis is implemented into the arc-length method. Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives. The computational performance of the optimization model is increased by implementing an island model into its evolutionary search mechanism. The proposed design optimization approach is applied for both size and shape optimization of real world trusses including 101, 224 and 444 bars and successful in generating feasible designations in a large and complex design space. It is observed that the computational performance of pareto-ranking based island model is better than the pure pareto-ranking based model. Therefore, pareto-ranking based island model is recommended to optimize the design of truss structure possessing geometric nonlinearity
Zhang, Jiapu
2010-01-01
Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...
The optimization of global fault tolerant trajectory for redundant manipulator based on self-motion
Zhang Jian
2015-01-01
Full Text Available The redundancy feature of manipulators provides the possibility for the fault tolerant trajectory planning. Aiming at the completion of the specific task, an algorithm of global fault tolerant trajectory optimization for redundant manipulator based on the self-motion is proposed in this paper. Firstly, inverse kinematics equation of single redundancy manipulator based on self-motion variable and null-space velocity array of Jacobian are analyzed. Secondly, the mathematical description of fault tolerance criteria of the configuration of manipulator is established and the fault tolerance configuration group of manipulator is obtained by using iteration traversal under the fault tolerance criteria. Then, considering the joint limits and minimum the energy consumption as the optimization target, the global fault tolerant joint trajectory is achieved. Finally, simulation for 7 degree of freedom (DOF manipulator is performed, by which the effectiveness of the algorithm is validated.
A Quadratic precision generalized nonlinear global optimization migration velocity inversion method
Zhao Taiyin; Hu Guangmin; He Zhenhua; Huang Deji
2009-01-01
An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Marmousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis (MVA) in complex areas.
Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review
M. K. Sakharov
2015-01-01
Full Text Available In recent decades, evolutionary algorithms have proven themselves as the powerful optimization techniques of search engine. Their popularity is due to the fact that they are easy to implement and can be used in all areas, since they are based on the idea of universal evolution. For example, in the problems of a large number of local optima, the traditional optimization methods, usually, fail in finding the global optimum. To solve such problems using a variety of stochastic methods, in particular, the so-called population-based algorithms, which are a kind of evolutionary methods. The main disadvantage of this class of methods is their slow convergence to the exact solution in the neighborhood of the global optimum, as these methods incapable to use the local information about the landscape of the function. This often limits their use in largescale real-world problems where the computation time is a critical factor.One of the promising directions in the field of modern evolutionary computation are memetic algorithms, which can be regarded as a combination of population search of the global optimum and local procedures for verifying solutions, which gives a synergistic effect. In the context of memetic algorithms, the meme is an implementation of the local optimization method to refine solution in the search.The concept of memetic algorithms provides ample opportunities for the development of various modifications of these algorithms, which can vary the frequency of the local search, the conditions of its end, and so on. The practically significant memetic algorithm modifications involve the simultaneous use of different memes. Such algorithms are called multi-memetic.The paper gives statement of the global problem of nonlinear unconstrained optimization, describes the most promising areas of AI modifications, including hybridization and metaoptimization. The main content of the work is the classification and review of existing varieties of
Global bifurcation investigation of an optimal velocity traffic model with driver reaction time
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.
Design of articulated mechanisms with a degree of freedom constraint using global optimization
Kawamoto, Atsushi; Stolpe, Mathias
2004-01-01
This paper deals with design of articulated mechanisms using a truss ground structure representation. The considered mechanism design problem is to maximize the output displacement for a given input force by choosing a prescribed number of truss elements out of all the available elements, so...... displacements. The problem is formulated as a non-convex mixed integer problem and solved using a convergent deterministic global optimization method based on branch and bound with convex relaxations....
Randomized Search Methods for Solving Markov Decision Processes and Global Optimization
2006-01-01
over relaxation (SOR) method ([81]). Puterman and Shin [62] proposed a modified policy iteration algorithm, which takes the basic form of PI, with the...99018) (1999). [61] Pintér, J. D., Global Optimization in Action, Kluwer Academic Publisher, The Netherlands, 1996. [62] Puterman , M. L. and Shin, M. C...Modified policy iteration algorithms for dis- counted Markov decision processes,” Management Science, 24, 1127–1137 (1978). [63] Puterman , M. L
Aijun Zhu; Chuanpei Xu; Zhi Li; Jun Wu; Zhenbing Liu
2015-01-01
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evo-lution (HGWO). Because basic grey wolf optimization (GWO) is easy to fal into stagnation when it carries out the operation of at-tacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE’s strong searching ability. The proposed algorithm can accele-rate the convergence speed of GWO and improve its performance. Twenty-three wel-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
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
2014-12-30
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.
Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions
Odat, Enas M.
2011-05-01
The purpose of this dissertation is to present a methodology to model global sequence alignment problem as directed acyclic graph which helps to extract all possible optimal alignments. Moreover, a mechanism to sequentially optimize sequence alignment problem relative to different cost functions is suggested. Sequence alignment is mostly important in computational biology. It is used to find evolutionary relationships between biological sequences. There are many algo- rithms that have been developed to solve this problem. The most famous algorithms are Needleman-Wunsch and Smith-Waterman that are based on dynamic program- ming. In dynamic programming, problem is divided into a set of overlapping sub- problems and then the solution of each subproblem is found. Finally, the solutions to these subproblems are combined into a final solution. In this thesis it has been proved that for two sequences of length m and n over a fixed alphabet, the suggested optimization procedure requires O(mn) arithmetic operations per cost function on a single processor machine. The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.
SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization
Li, Dengwang; Wang, Jie [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States)
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
Annealing evolutionary stochastic approximation Monte Carlo for global optimization
Liang, Faming
2010-04-08
In this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AESAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.
Global optimization of tool path for five-axis flank milling with a cylindrical cutter
无
2009-01-01
In this paper, optimum positioning of cylindrical cutter for five-axis flank milling of non-developable ruled surface is addressed from the perspective of surface approximation. Based on the developed interchangeability principle, global optimization of the five-axis tool path is modeled as approximation of the tool envelope surface to the data points on the design surface following the minimum zone criterion recommended by ANSI and ISO standards for tolerance evaluation. By using the signed point-to-surface distance function, tool path plannings for semi-finish and finish millings are formulated as two constrained optimization problems in a unified framework. Based on the second order Taylor approximation of the distance function, a sequential approximation algorithm along with a hierarchical algorithmic structure is developed for the optimization. Numerical examples are presented to confirm the validity of the proposed approach.
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.
Stellar Structure Modeling using a Parallel Genetic Algorithm for Objective Global Optimization
Metcalfe, T S
2002-01-01
Genetic algorithms are a class of heuristic search techniques that apply basic evolutionary operators in a computational setting. We have designed a fully parallel and distributed hardware/software implementation of the generalized optimization subroutine PIKAIA, which utilizes a genetic algorithm to provide an objective determination of the globally optimal parameters for a given model against an observational data set. We have used this modeling tool in the context of white dwarf asteroseismology, i.e., the art and science of extracting physical and structural information about these stars from observations of their oscillation frequencies. The efficient, parallel exploration of parameter-space made possible by genetic-algorithm-based numerical optimization led us to a number of interesting physical results: (1) resolution of a hitherto puzzling discrepancy between stellar evolution models and prior asteroseismic inferences of the surface helium layer mass for a DBV white dwarf; (2) precise determination of...
M.A. Ali
2015-06-01
Full Text Available This study presents a new multi-objective approach for optimal placement of Static Compensator (STATCOM for global (overall voltage sag mitigation as well as for power system performance improvement. The problem is formulated as a non linear constrained multi-objective optimization problem and solved using Genetic Algorithm (GA. The proposed method determines optimal locations of STATCOMs which simultaneously minimizes the overall voltage sags at network buses, bus voltage deviation and system real power loss and maximizes the voltage stability margin of the system. The proposed approach has been applied on IEEE 24-bus Reliability Test System (RTS and IEEE 57-bus test systems. The details of implementation and simulation results are presented. The application results are promising and encouraging.
Global optimization of tool path for five-axis flank milling with a cylindrical cutter
DING Han; ZHU LiMin
2009-01-01
In this paper,optimum positioning of cylindrical cutter for five-axis flank milling of non-developable ruled surface is addressed from the perspective of surface approximation.Based on the developed interchangeability principle,global optimization of the five-axis tool path is modeled as approximation of the tool envelope surface to the data points on the design surface following the minimum zone criterion recommended by ANSI and ISO standards for tolerance evaluation.By using the signed point-to-surface distance function,tool path plannings for semi-finish and finish millings are formulated as two constrained optimization problems in a unified framework.Based on the second order Taylor approximation of the distance function,a sequential approximation algorithm along with a hierarchical algorithmic structure is developed for the optimization.Numerical examples are presented to confirm the validity of the proposed approach.
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.
Stolpe, Mathias; Bendsøe, Martin P.
2007-01-01
This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities...
A global carbon assimilation system based on a dual optimization method
H. Zheng
2014-10-01
Full Text Available 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
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.
The Analysis and Compensation of Output Error for Voltage Source PWM Inverter%电压型PWM逆变器输出误差分析及其补偿
陈硕; 薛昭武
2001-01-01
由于死区时间和电力电子器件IGBT非理想特性的影响，PWM逆变器存在输出误差，使得众多的调速方案在低速运行时性能不佳或无法实现调速。本文详细分析死区时间和IGBT非理想特性对输出电压的影响，提出测量IGBT非理想特性方法和输出误差补偿法，并通过实验验证了误差补偿法的有效性。%Because a tWM inverter has an output error due to the dead time and the non-tdeal characteristic of IGBT, a number of variable speed schemes cannot obtain a good performance or operate at low speed. This paper analyzes the influence of the dead time and the non-ideal characteristic on the output voltage in detail, and proposes a measurement of IGBT and a compensating algorithm for the inverter. The validity of the proposed method has been verified by experimentation.
Global Sea Surface Temperature and Sea Level Rise Estimation with Optimal Historical Time Lag Data
Mustafa M. Aral
2016-11-01
Full Text Available Prediction of global temperatures and sea level rise (SLR is important for sustainable development planning of coastal regions of the world and the health and safety of communities living in these regions. In this study, climate change effects on sea level rise is investigated using a dynamic system model (DSM with time lag on historical input data. A time-invariant (TI-DSM and time-variant dynamic system model (TV-DSM with time lag is developed to predict global temperatures and SLR in the 21st century. The proposed model is an extension of the DSM developed by the authors. The proposed model includes the effect of temperature and sea level states of several previous years on the current temperature and sea level over stationary and also moving scale time periods. The optimal time lag period used in the model is determined by minimizing a synthetic performance index comprised of the root mean square error and coefficient of determination which is a measure for the reliability of the predictions. Historical records of global temperature and sea level from 1880 to 2001 are used to calibrate the model. The optimal time lag is determined to be eight years, based on the performance measures. The calibrated model was then used to predict the global temperature and sea levels in the 21st century using a fixed time lag period and moving scale time lag periods. To evaluate the adverse effect of greenhouse gas emissions on SLR, the proposed model was also uncoupled to project the SLR based on global temperatures that are obtained from the Intergovernmental Panel on Climate Change (IPCC emission scenarios. The projected SLR estimates for the 21st century are presented comparatively with the predictions made in previous studies.
Reza Sirjani; Melkamu Gamene Bade
2015-01-01
Shunt capacitors are broadly applied in distribution systems to scale down power losses, improve voltage profile and boost system capacity. The amount of capacitors added and location of deployment in the system highly determine the advantage of compensation. A novel global harmony search (GHS) algorithm in parallel with the backward/ forward sweep power flow technique and radial harmonic power flow was used to investigate the optimal placement and sizing of capacitors in radial distribution networks for minimizing power loss and total cost by taking account load unbalancing, mutual coupling and harmonics. The optimal capacitor placement outcomes show that the GHS algorithm can reduce total power losses up to 60 kW and leads to more than 18% of cost saving. The results also demonstrate that the GHS algorithm is more effective in minimization of power loss and total costs compared with genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) algorithm. Moreover, the proposed algorithm converges within 800 iterations and is faster in terms of computational time and gives better performance in finding optimal capacitor location and size compared with other optimization techniques.
W. J. Vanhaute
2011-11-01
Full Text Available The use of rainfall time series for various applications is widespread. However, in many cases historical rainfall records lack in length or quality for certain practical purposes, resulting in a reliance on rainfall models to supply simulated rainfall time series, e.g., in the design of hydraulic structures. One way to obtain such simulations is by means of stochastic point process rainfall models, such as the Bartlett-Lewis type of model. It is widely acknowledged that the calibration of such models suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their abilities to calibrate the Modified Bartlett-Lewis Model (MBL. The list of tested methods consists of: the Downhill Simplex Method (DSM, Simplex-Simulated Annealing (SIMPSA, Particle Swarm Optimization (PSO and Shuffled Complex Evolution (SCE-UA. The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the MBL model. Furthermore, this paper addresses the issue of subjectivity in the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method are influenced by the choice of weights.
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.
Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing
Aguiar e Oliveira Junior, Hime; Petraglia, Antonio; Rembold Petraglia, Mariane; Augusta Soares Machado, Maria
2012-01-01
Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving several problems by means of Fuzzy Adaptive Simulated Annealing (Fuzzy ASA), a fuzzy-controlled version of ASA, and by ASA itself. ASA is a sophisticated global optimization algorithm that is based upon ideas of the simulated annealing paradigm, coded in the C programming language and developed to statistically find the best global fit of a nonlinear constrained, non-convex cost function over a multi-dimensional space. By presenting detailed examples of its application we want to stimulate the reader’s intuition and make the use of Fuzzy ASA (or regular ASA) easier for everyone wishing to use these tools to solve problems. We kept formal mathematical requirements to a...
Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution
Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing
2016-12-01
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.
An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization
Jia, Liyuan; Gong, Wenyin; Wu, Hongbin
Differential evolution (DE), a fast and robust evolutionary algorithm for global optimization, has been widely used in many areas. However, the success of DE for solving different problems mainly depends on properly choosing the control parameter values. On the other hand, DE is good at exploring the search space and locating the region of global minimum, but it is slow at exploiting the solution. In order to alleviate these drawbacks of DE, this paper proposes an improved self-adaptive control parameter of DE, referred to as ISADE, for global numerical optimization. The proposed approach employs the individual fitness information to adapt the parameter settings. Hence, it can exploit the information of the individual and generate the promising offspring efficiently. To verify the viability of the proposed ISADE, 10 high-dimensional benchmark problems are chosen from literature. Experiment results indicate that this approach is efficient and effective. It is proved that this approach performs better than the original DE in terms of the convergence rate and the quality of the final solutions. Moreover, ISADE obtains faster convergence than the original self-adaptive control parameter of DE (SADE).
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.
The L_infinity constrained global optimal histogram equalization technique for real time imaging
Ren, Qiongwei; Niu, Yi; Liu, Lin; Jiao, Yang; Shi, Guangming
2015-08-01
Although the current imaging sensors can achieve 12 or higher precision, the current display devices and the commonly used digital image formats are still only 8 bits. This mismatch causes significant waste of the sensor precision and loss of information when storing and displaying the images. For better usage of the precision-budget, tone mapping operators have to be used to map the high-precision data into low-precision digital images adaptively. In this paper, the classic histogram equalization tone mapping operator is reexamined in the sense of optimization. We point out that the traditional histogram equalization technique and its variants are fundamentally improper by suffering from local optimum problems. To overcome this drawback, we remodel the histogram equalization tone mapping task based on graphic theory which achieves the global optimal solutions. Another advantage of the graphic-based modeling is that the tone-continuity is also modeled as a vital constraint in our approach which suppress the annoying boundary artifacts of the traditional approaches. In addition, we propose a novel dynamic programming technique to solve the histogram equalization problem in real time. Experimental results shows that the proposed tone-preserved global optimal histogram equalization technique outperforms the traditional approaches by exhibiting more subtle details in the foreground while preserving the smoothness of the background.
Liang, Faming
2014-04-03
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 use this much CPU time. This article 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, 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, for example, 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. Supplementary materials for this article are available online.
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.
Rashida Adeeb Khanum
2016-02-01
Full Text Available JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE. It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA. The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive perfor-mance of the algorithm.
Comparison of global optimization approaches for robust calibration of hydrologic model parameters
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.
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.
SUN Churen
2005-01-01
It is difficult to judge whether a given point is a global maximizer of an unconstrained optimization problem. This paper deals with this problem by considering globa linformation via integral and gives a necessary and sufficient condition judging whether a given point is a global maximizer of an unconstrained optimization problem. An algorithm is offered under such a condition and finally two test problems are verified via the offered algorithm.
A Fast Hybrid Algorithm of Global Optimization for Feedforward Neural Networks
JIANG Minghu; ZHANG Bo; ZHU Xiaoyan; JINAG Mingyan
2001-01-01
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a generalized conjugate gradient method was proposed, showing global convergence for error backpagation of MLFNN. It overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithms that maybe plunge into local minima. The hybrid algorithm's recognition rate is higher than that of Polak-Ribieve algorithm and convergence BP for test data, its training time is less than that of Fletcher-Reeves algorithm and far less than that of convergence BP, and it has a less complicated and stronger robustness to real speech data.
A New Global Optimization Algorithm for Solving a Class of Nonconvex Programming Problems
Xue-Gang Zhou
2014-01-01
Full Text Available A new two-part parametric linearization technique is proposed globally to a class of nonconvex programming problems (NPP. Firstly, a two-part parametric linearization method is adopted to construct the underestimator of objective and constraint functions, by utilizing a transformation and a parametric linear upper bounding function (LUBF and a linear lower bounding function (LLBF of a natural logarithm function and an exponential function with e as the base, respectively. Then, a sequence of relaxation lower linear programming problems, which are embedded in a branch-and-bound algorithm, are derived in an initial nonconvex programming problem. The proposed algorithm is converged to global optimal solution by means of a subsequent solution to a series of linear programming problems. Finally, some examples are given to illustrate the feasibility of the presented algorithm.
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.
JongHyup Lee
2016-08-01
Full Text Available 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.
Khac Duc Do
2015-01-01
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.
Protopopescu, V; Barhen, J
2003-01-01
A constant-time solution of the continuous global optimization problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Brueschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed. (letter to the editor)
Darup, Moritz Schulze; Mross, Stefan; Mönnigmann, Martin
2012-01-01
We compare two established and a new method for the calculation of spectral bounds for Hessian matrices on hyperrectangles by applying them to a large collection of 1522 objective and constraint functions extracted from benchmark global optimization problems. Both the tightness of the spectral bounds and the computational effort are assessed. Specifically, we compare eigenvalue bounds obtained with the interval variant of Gershgorin's circle criterion [2,6], Hertz and Rohn's [7,16] method for tight bounds of interval matrices, and a recently proposed Hessian matrix eigenvalue arithmetic [12], which deliberately avoids the computation of interval Hessians.
Zhang Zhijiun
2008-01-01
By Karamata regular variation theory and constructing comparison functions, the author shows the existence and global optimal asymptotic behaviour of solutions for a semilinear elliptic problem △u = k(x)g(u),u>0, x∈Ω, u|(e)Ω = +∞, where Ω is a bounded domain with smooth boundary in RN; g ∈ C1[0,∞), g(0) = g'(0) = 0, and there exists p > 1, such that lims→∞ g(sξ)/g(s)=ξp, (A)ξ > 0, and k∈Cαloc(Ω) is non-negative non-trivial in Ω which may be singular on the boundary.
Global stability, periodic solutions, and optimal control in a nonlinear differential delay model
Anatoli F. Ivanov
2010-09-01
Full Text Available A nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsey. An optimization problem for a maximal consumption is stated and solved for the latter.
Model–data fusion across ecosystems: from multi-site optimizations to global simulations
S. Kuppel
2014-05-01
Full Text Available 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
FILLED FUNCTIONS FOR UNCONSTRAINED GLOBAL OPTIMIZATION%无约束整体优化的填充函数
徐峥; 徐成贤
2000-01-01
The paper is concerned with the filled functions for global optimization of a continu-ous function of several variables. More general forms of filled functions are presented forsmooth and nonsmooth optimizations. These functions have either two adjustable parameters orone adjustable parameter. Conditions on functions and on the values of parameters are given so that the constructed functions are desired filled functions.
Optimized System Identification
Juang, Jer-Nan; Longman, Richard W.
1999-01-01
In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.
Zhang, Jiapu
2013-01-01
Simulated annealing (SA) was inspired from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects, both are attributes of the material that depend on its thermodynamic free energy. In this Paper, firstly we will study SA in details on its practical implementation. Then, hybrid pure SA with local (or global) search optimization methods allows us to be able to design several effective and efficient global search optimization methods. In order to keep the original sense of SA, we clarify our understandings of SA in crystallography and molecular modeling field through the studies of prion amyloid fibrils.
Global design optimization for an axial-flow tandem pump based on surrogate method
Li, D. H.; Zhao, Y.; Y Wang, G.
2013-12-01
Tandem pump, compared with multistage pump, goes without guide vanes between impellers. Better cavitation performance and significant reduction of the axial geometry scale is important for high-speed propulsion. This study presents a global design optimization method based on surrogated method for an axial-flow tandem pump to enhance trade-off performances: energy and cavitation performances. At the same time, interactions between impellers and impacts on the performances are analyzed. Fixed angle of blades in impellers and phase angle are performed as design variables. Efficiency and minimum average pressure coefficient (MAPC) on axial sectional surface in front impeller are the objective function, which can represent energy and cavitation performances well. Different surrogate models are constructed, and Global Sensitivity Analysis and Pareto Front method are used. The results show that, 1) Influence from phase angle on performances can be neglected compared with other two design variables, 2) Impact ratio of fixed angle of blades in two impellers on efficiency are the same as their designed loading distributions, which is 4:6, 3) The optimization results can enhance the trade-off performances well: efficiency is improved by 0.6%, and the MAPC is improved by 4.5%.
Global Convergence of a New restarting Conjugate Gradient Method for Nonlinear Optimizations
SUNQing-ying
2003-01-01
Conjugate gradient optimization algorithms depend on the search directions.with different choices for the parameters in the search directions.In this note,by combining the nice numerical performance of PR and HS methods with the global convergence property of the class of conjugate gradient methods presented by HU and STOREY(1991),a class of new restarting conjugate gradient methods is presented.Global convergences of the new method with two kinds of common line searches,are proved .Firstly,it is shown that,using reverse modulus of continuity funciton and forcing function,the new method for solving unconstrained optimization can work for a continously differentiable function with Curry-Altman's step size rule and a bounded level set .Secondly,by using comparing technique,some general convergence propecties of the new method with other kind of step size rule are established,Numerical experiments show that the new method is efficient by comparing with FR conjugate gradient method.
A novel evolutionary algorithm for global numerical optimization with continuous variables
Wenhong Zhao; Wei Wang; Yuping Wang
2008-01-01
Evolutionary algorithms (EAs) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimization with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones, and a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved in detail. Finally, the presented algorithm is executed to solve 24 benchmark problems, and the results show that the convergence-rate of the proposed algorithm is much faster than that of the compared algorithms.
The Multipoint Global Shape Optimization of Flying Configuration with Movable Leading Edges Flaps
Adriana NASTASE
2012-12-01
Full Text Available The aerodynamical global optimized (GO shape of flying configuration (FC, at two cruising Mach numbers, can be realized by morphing. Movable leading edge flaps are used for this purpose. The equations of the surfaces of the wing, of the fuselage and of the flaps in stretched position are approximated in form of superpositions of homogeneous polynomes in two variables with free coefficients. These coefficients together with the similarity parameters of the planform of the FC are the free parameters of the global optimization. Two enlarged variational problems with free boundaries occur. The first one consists in the determination of the GO shape of the wing-fuselageFC, with the flaps in retracted position, which must be of minimum drag, at higher cruising Mach number. The second enlarged variational problem consists in the determination of the GO shape of the flaps in stretched position in such a manner that the entire FC shall be of minimum drag at the second lower Mach number. The iterative optimum-optimorum (OO theory of the author is used for the solving of these both enlarged variational problems. The inviscid GO shape of the FC is used only in the first step of iteration and the own developed hybrid solutions for the compressible Navier-Stokes partial-differential equations (PDEs are used for the determination of the friction drag coefficient and up the second step of iteration of OO theory.
CH4 parameter estimation in CLM4.5bgc using surrogate global optimization
J. Müller
2015-01-01
Full Text Available Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility, only relatively few simulations can be allowed in order to find a near optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50%. The CLM predictions with the optimized parameters agree for northern and tropical latitudes more with the observed data than when using the default parameters and the emission predictions are higher than with default settings in northern latitudes and lower than default settings in the
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.
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.
Zhaoliang Song
Full Text Available 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.
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
Export dynamics as an optimal growth problem in the network of global economy
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.
Parameter identification theory of a complex model based on global optimization method
2008-01-01
With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.
Searchlight Correlation Detectors: Optimal Seismic Monitoring Using Regional and Global Networks
Gibbons, Steven J.; Kværna, Tormod; Näsholm, Sven Peter
2015-04-01
The sensitivity of correlation detectors increases greatly when the outputs from multiple seismic traces are considered. For single-array monitoring, a zero-offset stack of individual correlation traces will provide significant noise suppression and enhanced sensitivity for a source region surrounding the hypocenter of the master event. The extent of this region is limited only by the decrease in waveform similarity with increasing hypocenter separation. When a regional or global network of arrays and/or 3-component stations is employed, the zero-offset approach is only optimal when the master and detected events are co-located exactly. In many monitoring situations, including nuclear test sites and geothermal fields, events may be separated by up to many hundreds of meters while still retaining sufficient waveform similarity for correlation detection on single channels. However, the traveltime differences resulting from the hypocenter separation may result in significant beam loss on the zero-offset stack and a deployment of many beams for different hypothetical source locations in geographical space is required. The beam deployment necessary for optimal performance of the correlation detectors is determined by an empirical network response function which is most easily evaluated using the auto-correlation functions of the waveform templates from the master event. The correlation detector beam deployments for providing optimal network sensitivity for the North Korea nuclear test site are demonstrated for both regional and teleseismic monitoring configurations.
Chun-Liang Lu
2014-12-01
Full Text Available Differential evolution (DE is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP. Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.
WANG Hui; WU Di; AGOULMINE Nazim; MA Mao-de
2009-01-01
The multi-source and single-sink (MSSS) topology in wireless sensor networks (WSNs) is defined as a network topology, where all of nodes can gather, receive and transmit data to the sink. In energy-constrained WSNs with such a topology, the joint optimal design in the physical, medium access control (MAC) and network layers is considered for network lifetime maximization (NLM). The problem of integrating multi-layer information to compute NLM, which involves routing flow, link schedule and transmission power, is formulated as a non-linear optimization problem. Specially under time division multiple access (TDMA) scheme, this problem can be transformed into a convex optimization problem. To solve it analytically we make use of the property that local optimization is global optimization in convex problem. This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression, i.e., the globally optimal network lifetime (NL). NL is derived as a function of number of nodes, their initial energy and data rate arrived at them.Based on the analysis of analytical approach, it takes the influence of data rates, link access and routing method over NLM into account. Moreover, the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT to compare with other suboptimal schemes.
Efficient Parallel Global Optimization for High Resolution Hydrologic and Climate Impact Models
Shoemaker, C. A.; Mueller, J.; Pang, M.
2013-12-01
High Resolution hydrologic models are typically computationally expensive, requiring many minutes or perhaps hours for one simulation. Optimization can be used with these models for parameter estimation or for analyzing management alternatives. However Optimization of these computationally expensive simulations requires algorithms that can obtain accurate answers with relatively few simulations to avoid infeasibly long computation times. We have developed a number of efficient parallel algorithms and software codes for optimization of expensive problems with multiple local minimum. This is open source software we are distributing. It runs in Matlab and Python, and has been run on Yellowstone supercomputer. The talk will quickly discuss the characteristics of the problem (e.g. the presence of integer as well as continuous variables, the number of dimensions, the availability of parallel/grid computing, the number of simulations that can be allowed to find a solution, etc. ) that determine which algorithms are most appropriate for each type of problem. A major application of this optimization software is for parameter estimation for nonlinear hydrologic models, including contaminant transport in subsurface (e.g. for groundwater remediation or multi-phase flow for carbon sequestration), nutrient transport in watersheds, and climate models. We will present results for carbon sequestration plume monitoring (multi-phase, multi-constiuent), for groundwater remediation, and for the CLM climate model. The carbon sequestration example is based on the Frio CO2 field site and the groundwater example is for a 50,000 acre remediation site (with model requiring about 1 hour per simulation). Parallel speed-ups are excellent in most cases, and our serial and parallel algorithms tend to outperform alternative methods on complex computationally expensive simulations that have multiple global minima.
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.
Population Structures in Russia: Optimality and Dependence on Parameters of Global Evolution
Yuri Yegorov
2016-07-01
Full Text Available The paper is devoted to analytical investigation of the division of geographical space into urban and rural areas with application to Russia. Yegorov (2005, 2006, 2009 has suggested the role of population density on economics. A city has an attractive potential based on scale economies. The optimal city size depends on the balance between its attractive potential and the cost of living that can be approximated by equilibrium land rent and commuting cost. For moderate scale effects optimal population of a city depends negatively on transport costs that are related positively with energy price index. The optimal agricultural density of population can also be constructed. The larger is a land slot per peasant, the higher will be the output from one unit of his labour force applied to this slot. But at the same time, larger farm size results in increase of energy costs, related to land development, collecting the crop and bringing it to the market. In the last 10 years we have observed substantial rise of both food and energy prices at the world stock markets. However, the income of farmers did not grow as fast as food price index. This can shift optimal rural population density to lower level, causing migration to cities (and we observe this tendency globally. Any change in those prices results in suboptimality of existing spatial structures. If changes are slow, the optimal infrastructure can be adjusted by simple migration. If the shocks are high, adaptation may be impossible and shock will persist. This took place in early 1990es in the former USSR, where after transition to world price for oil in domestic markets existing spatial infrastructure became suboptimal and resulted in persistent crisis, leading to deterioration of both industry and agriculture. Russia is the largest country but this is also its problem. Having large resource endowment per capita, it is problematic to build sufficient infrastructure. Russia has too low population
Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization
无
2002-01-01
Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.
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.
SIMULTANEOUS SHAPE AND TOPOLOGY OPTIMIZATION OF TRUSS UNDER LOCAL AND GLOBAL STABILITY CONSTRAINTS
GuoXu; LiuWei; LiHongyan
2003-01-01
A new approach for the solution of truss shape and topology optimization problem sunder local and global stability constraints is proposed. By employing the cross sectional areas of each bar and some shape parameters as topology design variables, the difficulty arising from the jumping of buckling length phenomenon can be easily overcome without the necessity of introducing the overlapping bars into the initial ground structure. Therefore computational efforts can be saved for the solution of this kind of problem. By modifying the elements of the stiffness matrix using Sigmoid function, the continuity of the objective and constraint functions with respect to shape design parameters can be restored to some extent. Some numerical examples demonstrate the effectiveness of the proposed method.
A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing
Yong Wu; Arun Kumar
2012-01-01
In this paper,we propose a decentralized parallel computation model for global optimization using interval analysis.The model is adaptive to any number of processors and the workload is automatically and evenly distributed among all processors by alternative message passing.The problems received by each processor are processed based on their local dominance properties,which avoids unnecessary interval evaluations.Further,the problem is treated as a whole at the beginning of computation so that no initial decomposition scheme is required.Numerical experiments indicate that the model works well and is stable with different number of parallel processors,distributes the load evenly among the processors,and provides an impressive speedup,especially when the problem is time-consuming to solve.
GENOPT 2016: Design of a generalization-based challenge in global optimization
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.
A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm
Santhan Kumar Cherukuri
2016-11-01
Full Text Available To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG systems are to be operated at their maximum power point (MPP under both variable climatic and partial shaded condition (PSC. From literature most of conventional MPP tracking (MPPT methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented. From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed. Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available online How to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016 A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3, 225-232. http://dx.doi.org/10.14710/ijred.5.3.225-232
Carlos Pozo
Full Text Available 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
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
Guo, Chengan; Yang, Qingshan
2015-07-01
Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal solution, unless using other objective functions (e.g., the l1 norm or lp norm) for approximate solutions or using greedy search methods for locally optimal solutions (e.g., the orthogonal matching pursuit type algorithms). In this paper, a neurodynamic optimization method is proposed to solve the l0 -norm minimization problems for obtaining the global optimum using a recurrent neural network (RNN) model. For the RNN model, a group of modified Gaussian functions are constructed and their sum is taken as the objective function for approximating the l0 norm and for optimization. The constructed objective function sets up a convexity condition under which the neurodynamic system is guaranteed to obtain the globally convergent optimal solution. An adaptive adjustment scheme is developed for improving the performance of the optimization algorithm further. Extensive experiments are conducted to test the proposed approach in this paper and the output results validate the effectiveness of the new method.
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.
Jian-Guo Zheng
2015-01-01
Full Text Available Artificial bee colony (ABC algorithm is a popular swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. However, ABC is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. To improve the performance of ABC, a novel ABC combined with grenade explosion method (GEM and Cauchy operator, namely, ABCGC, is proposed. GEM is embedded in the onlooker bees’ phase to enhance the exploitation ability and accelerate convergence of ABCGC; meanwhile, Cauchy operator is introduced into the scout bees’ phase to help ABCGC escape from local optimum and further enhance its exploration ability. Two sets of well-known benchmark functions are used to validate the better performance of ABCGC. The experiments confirm that ABCGC is significantly superior to ABC and other competitors; particularly it converges to the global optimum faster in most cases. These results suggest that ABCGC usually achieves a good balance between exploitation and exploration and can effectively serve as an alternative for global optimization.
Optimizing global CO concentrations and emissions based on DART/CAM-CHEM
Gaubert, B.; Arellano, A. F.; Barre, J.; Worden, H. M.; Emmons, L. K.; Wiedinmyer, C.; Anderson, J. L.; Deeter, M. N.; Mizzi, A. P.; Edwards, D. P.
2014-12-01
Atmospheric Carbon Monoxide (CO) is an important trace gas in tropospheric chemistry through its impact on the oxidizing capacity of the troposphere, as precursor of ozone, and as a good tracer of combustion from both anthropogenic sources and wildfires. We will investigate the potential of the assimilation of TERRA/MOPITT observations to constrain the regional to global CO budget using DART (Data assimilation Research Testbed) together with the global Community Atmospheric Model (CAM-Chem). DART/CAM-Chem is based on an ensemble adjustment Kalman filter (EAKF) framework which facilitates statistical estimation of error correlations between chemical states (CO and related species) and parameters (including sources) in the model using the ensemble statistics derived from dynamical and chemical perturbations in the model. Here, we estimate CO emissions within DART/CAM-Chem using a state augmentation approach where CO emissions are added to the CO state vector being analyzed. We compare these optimized emissions to estimates derived from a traditional Bayesian synthesis inversion using the CO analyses (assimilated CO states) as observational constraints. The spatio-temporal distribution of CO and other chemical species will be compared to profile measurements from aircraft and other satellite instruments (e.g., INTEX-B, ARCTAS).
Nacelle Chine Installation Based on Wind-Tunnel Test Using Efficient Global Optimization
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.
Hong Xia YIN; Dong Lei DU
2007-01-01
The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenvalues in the Hessian approximation matrices of the objective function. It has been proved in the literature that this method has the global and superlinear convergence when the objective function is convex (or even uniformly convex). We propose to solve unconstrained nonconvex optimization problems by a self-scaling BFGS algorithm with nonmonotone linear search. Nonmonotone line search has been recognized in numerical practices as a competitive approach for solving large-scale nonlinear problems. We consider two different nonmonotone line search forms and study the global convergence of these nonmonotone self-scale BFGS algorithms. We prove that, under some weaker condition than that in the literature, both forms of the self-scaling BFGS algorithm are globally convergent for unconstrained nonconvex optimization problems.
How can we Optimize Global Satellite Observations of Glacier Velocity and Elevation Changes?
Willis, M. J.; Pritchard, M. E.; Zheng, W.
2015-12-01
We have started a global compilation of glacier surface elevation change rates measured by altimeters and differencing of Digital Elevation Models and glacier velocities measured by Synthetic Aperture Radar (SAR) and optical feature tracking as well as from Interferometric SAR (InSAR). Our goal is to compile statistics on recent ice flow velocities and surface elevation change rates near the fronts of all available glaciers using literature and our own data sets of the Russian Arctic, Patagonia, Alaska, Greenland and Antarctica, the Himalayas, and other locations. We quantify the percentage of the glaciers on the planet that can be regarded as fast flowing glaciers, with surface velocities of more than 50 meters per year, while also recording glaciers that have elevation change rates of more than 2 meters per year. We examine whether glaciers have significant interannual variations in velocities, or have accelerated or stagnated where time series of ice motions are available. We use glacier boundaries and identifiers from the Randolph Glacier Inventory. Our survey highlights glaciers that are likely to react quickly to changes in their mass accumulation rates. The study also identifies geographical areas where our knowledge of glacier dynamics remains poor. Our survey helps guide how frequently observations must be made in order to provide quality satellite-derived velocity and ice elevation observations at a variety of glacier thermal regimes, speeds and widths. Our objectives are to determine to what extent the joint NASA and Indian Space Research Organization Synthetic Aperture Radar mission (NISAR) will be able to provide global precision coverage of ice speed changes and to determine how to optimize observations from the global constellation of satellite missions to record important changes to glacier elevations and velocities worldwide.
Zdravko Bazdan
2010-12-01
Full Text Available The aim of this study is to point to the fact that economic diplomacy is a relatively new practice in international economics, specifically the expansion of the occurrence of Intelligence Revolution. The history in global relations shows that without economic diplomacy there is no optimal economic growth and social development. It is important to note that economic diplomacy should be important for our country and the political elite, as well as for the administration of Croatian economic subjects that want to compete in international market economy. Comparative analysis are particularly highlighted by French experience. Therefore, Croatia should copy the practice of those countries that are successful in economic diplomacy. And in the curricula - especially of our economic faculties - we should introduce the course of Economic Diplomacy. It is important to note, that in order to form our optimal model of economic diplomacy which would be headed by the President of Republic of Croatia formula should be based on: Intelligence Security Agency (SOA, Intelligence Service of the Ministry of Foreign Affairs and European Integration, Intelligence Service of the Croatian Chamber of Commerce and the Intelligence Service of the Ministry of Economy, Labor and Entrepreneurship. Described model would consist of intelligence subsystem with at least twelve components.
Development of a fuzzy optimization model, supporting global warming decision-making
Leimbach, M. [Potsdam Inst. for Climate Impact Research, Potsdam (Germany)
1996-03-01
An increasing number of models have been developed to support global warming response policies. The model constructors are facing a lot of uncertainties which limit the evidence of these models. The support of climate policy decision-making is only possible in a semi-quantitative way, as presented by a Fuzzy model. The model design is based on an optimization approach, integrated in a bounded risk decision-making framework. Given some regional emission-related and impact-related restrictions, optimal emission paths can be calculated. The focus is not only on carbon dioxide but on other greenhouse gases too. In the paper, the components of the model will be described. Cost coefficients, emission boundaries and impact boundaries are represented as Fuzzy parameters. The Fuzzy model will be transformed into a computational one by using an approach of Rommelfanger. In the second part, some problems of applying the model to computations will be discussed. This includes discussions on the data situation and the presentation, as well as interpretation of results of sensitivity analyses. The advantage of the Fuzzy approach is that the requirements regarding data precision are not so strong. Hence, the effort for data acquisition can be reduced and computations can be started earlier. 9 figs., 3 tabs., 17 refs., 1 appendix
Protein structure prediction using global optimization by basin-hopping with NMR shift restraints.
Hoffmann, Falk; Strodel, Birgit
2013-01-14
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 hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
Ali Wagdy Mohamed
2014-11-01
Full Text Available In this paper, a novel version of Differential Evolution (DE algorithm based on a couple of local search mutation and a restart mechanism for solving global numerical optimization problems over continuous space is presented. The proposed algorithm is named as Restart Differential Evolution algorithm with Local Search Mutation (RDEL. In RDEL, inspired by Particle Swarm Optimization (PSO, a novel local mutation rule based on the position of the best and the worst individuals among the entire population of a particular generation is introduced. The novel local mutation scheme is joined with the basic mutation rule through a linear decreasing function. The proposed local mutation scheme is proven to enhance local search tendency of the basic DE and speed up the convergence. Furthermore, a restart mechanism based on random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme is combined to avoid stagnation and/or premature convergence. Additionally, an exponent increased crossover probability rule and a uniform scaling factors of DE are introduced to promote the diversity of the population and to improve the search process, respectively. The performance of RDEL is investigated and compared with basic differential evolution, and state-of-the-art parameter adaptive differential evolution variants. It is discovered that the proposed modifications significantly improve the performance of DE in terms of quality of solution, efficiency and robustness.
Park, Y. C.; Chang, M. H.; Lee, T.-Y.
2007-06-01
A deterministic global optimization method that is applicable to general nonlinear programming problems composed of twice-differentiable objective and constraint functions is proposed. The method hybridizes the branch-and-bound algorithm and a convex cut function (CCF). For a given subregion, the difference of a convex underestimator that does not need an iterative local optimizer to determine the lower bound of the objective function is generated. If the obtained lower bound is located in an infeasible region, then the CCF is generated for constraints to cut this region. The cutting region generated by the CCF forms a hyperellipsoid and serves as the basis of a discarding rule for the selected subregion. However, the convergence rate decreases as the number of cutting regions increases. To accelerate the convergence rate, an inclusion relation between two hyperellipsoids should be applied in order to reduce the number of cutting regions. It is shown that the two-hyperellipsoid inclusion relation is determined by maximizing a quadratic function over a sphere, which is a special case of a trust region subproblem. The proposed method is applied to twelve nonlinear programming test problems and five engineering design problems. Numerical results show that the proposed method converges in a finite calculation time and produces accurate solutions.
Shen, Bo [ORNL; Abdelaziz, Omar [ORNL; Shrestha, Som S [ORNL
2017-01-01
Oak Ridge National laboratory (ORNL) recently conducted extensive laboratory, drop-in investigations for lower Global Warming Potential (GWP) refrigerants to replace R-22 and R-410A. ORNL studied propane, DR-3, ARM-20B, N-20B and R-444B as lower GWP refrigerant replacement for R-22 in a mini-split room air conditioner (RAC) originally designed for R-22; and, R-32, DR-55, ARM-71A, and L41-2, in a mini-split RAC designed for R-410A. We obtained laboratory testing results with very good energy balance and nominal measurement uncertainty. Drop-in studies are not enough to judge the overall performance of the alternative refrigerants since their thermodynamic and transport properties might favor different heat exchanger configurations, e.g. cross-flow, counter flow, etc. This study compares optimized performances of individual refrigerants using a physics-based system model tools. The DOE/ORNL Heat Pump Design Model (HPDM) was used to model the mini-split RACs by inputting detailed heat exchangers geometries, compressor displacement and efficiencies as well as other relevant system components. The RAC models were calibrated against the lab data for each individual refrigerant. The calibrated models were then used to conduct a design optimization for the cooling performance by varying the compressor displacement to match the required capacity, and changing the number of circuits, refrigerant flow direction, tube diameters, air flow rates in the condenser and evaporator at 100% and 50% cooling capacities. This paper compares the optimized performance results for all alternative refrigerants and highlights best candidates for R-22 and R-410A replacement.
Pivot method for global optimization: A study of structures and phase changes in water clusters
Nigra, Pablo Fernando
In this thesis, we have carried out a study of water clusters. The research work has been developed in two stages. In the first stage, we have investigated the properties of water clusters at zero temperature by means of global optimization. The clusters were modeled by using two well known pairwise potentials having distinct characteristics. One is the Matsuoka-Clementi-Yoshimine potential (MCY) that is an ab initio fitted function based on a rigid-molecule model, the other is the Sillinger-Rahman potential (SR) which is an empirical function based on a flexible-molecule model. The algorithm used for the global optimization of the clusters was the pivot method, which was developed in our group. The results have shown that, under certain conditions, the pivot method may yield optimized structures which are related to one another in such a way that they seem to form structural families. The structures in a family can be thought of as formed from the aggregation of single units. The particular types of structures we have found are quasi-one dimensional tubes built from stacking cyclic units such as tetramers, pentamers, and hexamers. The binding energies of these tubes form sequences that span smooth curves with clear asymptotic behavior; therefore, we have also studied the sequences applying the Bulirsch-Stoer (BST) algorithm to accelerate convergence. In the second stage of the research work, we have studied the thermodynamic properties of a typical water cluster at finite temperatures. The selected cluster was the water octamer which exhibits a definite solid-liquid phase change. The water octamer also has several low lying energy cubic structures with large energetic barriers that cause ergodicity breaking in regular Monte Carlo simulations. For that reason we have simulated the octamer using paralell tempering Monte Carlo combined with the multihistogram method. This has permited us to calculate the heat capacity from very low temperatures up to T = 230 K. We
范玮丽
2008-01-01
This paper mainly talks about the currently hot topic-globalization. Firstly, it brings out the general trend about globalization and how to better understand its implication. Secondly, it largely focuses on how to deal with it properly, especially for international marketers. Then, facing with the overwhelming trend, it is time for us to think about seriously what has globalization brought to us. Last but not least, it summarized the author's personal view about the future of globalization and how should we go.
Using R for Global Optimization of a Fully-distributed Hydrologic Model at Continental Scale
Zambrano-Bigiarini, M.; Zajac, Z.; Salamon, P.
2013-12-01
Nowadays hydrologic model simulations are widely used to better understand hydrologic processes and to predict extreme events such as floods and droughts. In particular, the spatially distributed LISFLOOD model is currently used for flood forecasting at Pan-European scale, within the European Flood Awareness System (EFAS). Several model parameters can not be directly measured, and they need to be estimated through calibration, in order to constrain simulated discharges to their observed counterparts. In this work we describe how the free software 'R' has been used as a single environment to pre-process hydro-meteorological data, to carry out global optimization, and to post-process calibration results in Europe. Historical daily discharge records were pre-processed for 4062 stream gauges, with different amount and distribution of data in each one of them. The hydroTSM, raster and sp R packages were used to select ca. 700 stations with an adequate spatio-temporal coverage. Selected stations span a wide range of hydro-climatic characteristics, from arid and ET-dominated watersheds in the Iberian Peninsula to snow-dominated watersheds in Scandinavia. Nine parameters were selected to be calibrated based on previous expert knowledge. Customized R scripts were used to extract observed time series for each catchment and to prepare the input files required to fully set up the calibration thereof. The hydroPSO package was then used to carry out a single-objective global optimization on each selected catchment, by using the Standard Particle Swarm 2011 (SPSO-2011) algorithm. Among the many goodness-of-fit measures available in the hydroGOF package, the Nash-Sutcliffe efficiency was used to drive the optimization. User-defined functions were developed for reading model outputs and passing them to the calibration engine. The long computational time required to finish the calibration at continental scale was partially alleviated by using 4 multi-core machines (with both GNU
Tulio Rosembuj
2006-12-01
Full Text Available There is no singular globalization, nor is the result of an individual agent. We could start by saying that global action has different angles and subjects who perform it are different, as well as its objectives. The global is an invisible invasion of materials and immediate effects.
Vaarkamp, Jaap [Joint Department of Physics, Royal Marsden NHS Trust, Sutton, Surrey (United Kingdom). E-mail: jaap@icr.ac.uk
2001-06-01
Full text: I would like to make four comments on three papers by two authors (Wu and Zhu 2000, 2001, Wu et al 2000) on one topic: optimization of 3D conformal radiotherapy treatment planning. In the papers, genetic and global algorithms are proposed for this optimization, and the authors claim to be able to generate better treatment plans than those produced manually and used for patient treatment (Wu and Zhu 2000). However, the data in the papers do not warrant such a conclusion and the work contains such serious methodological flaws that only the opposite can have been true. First, in the papers a few treatment plans for patients with different brain tumours are discussed. Dose volume histograms (DVHs) are presented for the target, sometimes the planning target volume, sometimes the clinical target volume, and the organs at risk (OARs): left and right eye, and thyroid or spinal cord. However, other OARs limit dose more in clinical treatment planning, and it is those OARs to which the planner must direct all effort when optimizing the treatment plan. One such important OAR when treating children is the temporal lobes because the dose to the temporal lobes has been associated with a reduction in IQ points (Fuss et al 2000). Also particularly important when treating children are the hypothalamus and pituitary, because they influence growth and the further hormonal development (Schmiegelow et al 1999, 2000). Furthermore, rather than the eyes themselves, the optic chiasm usually gets more serious attention (Fuss et al 1999) and is considered so important that it is often blocked from the treatment fields during the final fractions, thus compromising dose homogeneity in the target. Finally, irradiating the auditory apparatus can lead to a loss of hearing (Lin et al 2000), and, in particular when one side receives a high dose, every effort is made to at least spare the other side. Hence, it is not surprising to find a treatment plan that is superior in some of the
Climate, Agriculture, Energy and the Optimal Allocation of Global Land Use
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
Zhang, X.; Cai, X.; Zhu, T.
2013-12-01
Biofuels is booming in recent years due to its potential contributions to energy sustainability, environmental improvement and economic opportunities. Production of biofuels not only competes for land and water with food production, but also directly pushes up food prices when crops such as maize and sugarcane are used as biofuels feedstock. Meanwhile, international trade of agricultural commodities exports and imports water and land resources in a virtual form among different regions, balances overall water and land demands and resource endowment, and provides a promising solution to the increasingly severe food-energy competition. This study investigates how to optimize water and land resources uses for overall welfare at global scale in the framework of 'virtual resources'. In contrast to partial equilibrium models that usually simulate trades year-by-year, this optimization model explores the ideal world where malnourishment is minimized with optimal resources uses and trade flows. Comparing the optimal production and trade patterns with historical data can provide meaningful implications regarding how to utilize water and land resources more efficiently and how the trade flows would be changed for overall welfare at global scale. Valuable insights are obtained in terms of the interactions among food, water and bioenergy systems. A global hydro-economic optimization model is developed, integrating agricultural production, market demands (food, feed, fuel and other), and resource and environmental constraints. Preliminary results show that with the 'free market' mechanism and land as well as water resources use optimization, the malnourished population can be reduced by as much as 65%, compared to the 2000 historical value. Expected results include: 1) optimal trade paths to achieve global malnourishment minimization, 2) how water and land resources constrain local supply, 3) how policy affects the trade pattern as well as resource uses. Furthermore, impacts of
GOSIM: A multi-scale iterative multiple-point statistics algorithm with global optimization
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.
Efficiency of Pareto joint inversion of 2D geophysical data using global optimization methods
Miernik, Katarzyna; Bogacz, Adrian; Kozubal, Adam; Danek, Tomasz; Wojdyła, Marek
2016-04-01
Pareto joint inversion of two or more sets of data is a promising new tool of modern geophysical exploration. In the first stage of our investigation we created software enabling execution of forward solvers of two geophysical methods (2D magnetotelluric and gravity) as well as inversion with possibility of constraining solution with seismic data. In the algorithm solving MT forward solver Helmholtz's equations, finite element method and Dirichlet's boundary conditions were applied. Gravity forward solver was based on Talwani's algorithm. To limit dimensionality of solution space we decided to describe model as sets of polygons, using Sharp Boundary Interface (SBI) approach. The main inversion engine was created using Particle Swarm Optimization (PSO) algorithm adapted to handle two or more target functions and to prevent acceptance of solutions which are non - realistic or incompatible with Pareto scheme. Each inversion run generates single Pareto solution, which can be added to Pareto Front. The PSO inversion engine was parallelized using OpenMP standard, what enabled execution code for practically unlimited amount of threads at once. Thereby computing time of inversion process was significantly decreased. Furthermore, computing efficiency increases with number of PSO iterations. In this contribution we analyze the efficiency of created software solution taking under consideration details of chosen global optimization engine used as a main joint minimization engine. Additionally we study the scale of possible decrease of computational time caused by different methods of parallelization applied for both forward solvers and inversion algorithm. All tests were done for 2D magnetotelluric and gravity data based on real geological media. Obtained results show that even for relatively simple mid end computational infrastructure proposed solution of inversion problem can be applied in practice and used for real life problems of geophysical inversion and interpretation.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014.
Kleijnen, Jack P.C.; van Beers, W.C.M.; van Nieuwenhuyse, I.
2011-01-01
This article uses a sequentialized experimental design to select simulation input com- binations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code).
A global rate-distortion optimized approach for H.26L low bit rate robust video over the Internet
Yang Hua; Yu Songyu; Yang Songan
2005-01-01
In recent years, more and more applications of video communication over the Internet have been extended, so the demand for reliable transmission of compressed video in a packet loss environment is ever increasing. Rate-Distortion optimized mode selection is a fundamental problem of video communication over packet-switched networks, but the classical R-D method only considers quantization distortion in the source and hence it cannot achieve global optimality. Here we introduce a new global R-D optimal Macro-Block coding mode decision scheme for the new H.26L video compression standard. Based on the Internet packet loss model of Bernoulli and Gilbert, this R-D mode decision approach can result in better error robustness than classical method. Furthermore, our experimental results also demonstrate its superior adaptive error resilience and feasibility.
Two-stage collaborative global optimization design model of the CHPG microgrid
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.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
Javier Martínez-López
2016-09-01
Full Text Available Protected areas (PAs need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-21
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
Improving Operability of Lab-Scale Spouted Bed Using Global Stochastic Optimization
Dr.Ghanim.M. Alwan
2015-01-01
Full Text Available A spouted bed is a special case of fluidization. It is an effective means of contacting gas with coarse solid particles .Gas-solid spouted beds are either cylindrical bed with cone base or the whole bed is in a cone shape where the gas enters as a jet. The gas forms a spout region that carries the solids upward in a diluted phase that forms a fountain at the top of the bed where the solids fall down and move downward in the annular region. Performance of gas-solid spouted bed benefit from solids uniformity structure with lower pressure drop (PD.Dropping of PD across a spouted bed could reduce the dissipated pumping energy and improve stability and uniformity of solid particles. The objective of this work is to study and selecting best operating conditions that could minimize PD across the bed. Optimization technique is a powerful tool would guide the experimental work and reduce the risk and cost for design and operation Hence, PD is to be considered as objective function of the optimization process .Three selected decision variables are affecting objective function. These decision variables are gas velocity, particle density and particle diameter. Steady-state measurements were carried out in a narrow 3-inch (0.076 m ID cylindrical spouted bed made of Plexiglas that used 60° conical shape base. Radial concentration of particles (glass and steel beads at various bed heights under different flow patterns were measured using sophisticated optical probes. A superficial velocity of air ranging from 0.74 to 1.0 m/s .PD was measured across the bed by high accuracy pressure transducers. Stochastic Genetic Algorithm (GA has found suitable global search for the non-linear hybrid spouted bed. Optimum results could select the best operating conditions for high-performance and stable conditions. Uniformity and stability of solid particles in the bed would enhance hydrodynamic parameters, heat and mass transfer. Best Operability of the bed was observed with
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient.
Chattopadhyay, Ishanu
2011-01-01
The problem of near-optimal distributed path planning to locally sensed targets is investigated in the context of large swarms. The proposed algorithm uses only information that can be locally queried, and rigorous theoretical results on convergence, robustness, scalability are established, and effect of system parameters such as the agent-level communication radius and agent velocities on global performance is analyzed. The fundamental philosophy of the proposed approach is to percolate local information across the swarm, enabling agents to indirectly access the global context. A gradient emerges, reflecting the performance of agents, computed in a distributed manner via local information exchange between neighboring agents. It is shown that to follow near-optimal routes to a target which can be only sensed locally, and whose location is not known a priori, the agents need to simply move towards its "best" neighbor, where the notion of "best" is obtained by computing the state-specific language measure of an...
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.
Plum, Maja
Globalization is often referred to as external to education - a state of affair facing the modern curriculum with numerous challenges. In this paper it is examined as internal to curriculum; analysed as a problematization in a Foucaultian sense. That is, as a complex of attentions, worries, ways...... of reasoning, producing curricular variables. The analysis is made through an example of early childhood curriculum in Danish Pre-school, and the way the curricular variable of the pre-school child comes into being through globalization as a problematization, carried forth by the comparative practices of PISA...
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
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
Eline L Korenromp
Full Text Available BACKGROUND: The Global Plan to Stop TB estimates funding required in low- and middle-income countries to achieve TB control targets set by the Stop TB Partnership within the context of the Millennium Development Goals. We estimate the contribution and impact of Global Fund investments under various scenarios of allocations across interventions and regions. METHODOLOGY/PRINCIPAL FINDINGS: Using Global Plan assumptions on expected cases and mortality, we estimate treatment costs and mortality impact for diagnosis and treatment for drug-sensitive and multidrug-resistant TB (MDR-TB, including antiretroviral treatment (ART during DOTS for HIV-co-infected patients, for four country groups, overall and for the Global Fund investments. In 2015, China and India account for 24% of funding need, Eastern Europe and Central Asia (EECA for 33%, sub-Saharan Africa (SSA for 20%, and other low- and middle-income countries for 24%. Scale-up of MDR-TB treatment, especially in EECA, drives an increasing global TB funding need--an essential investment to contain the mortality burden associated with MDR-TB and future disease costs. Funding needs rise fastest in SSA, reflecting increasing coverage need of improved TB/HIV management, which saves most lives per dollar spent in the short term. The Global Fund is expected to finance 8-12% of Global Plan implementation costs annually. Lives saved through Global Fund TB support within the available funding envelope could increase 37% if allocations shifted from current regional demand patterns to a prioritized scale-up of improved TB/HIV treatment and secondly DOTS, both mainly in Africa--with EECA region, which has disproportionately high per-patient costs, funded from alternative resources. CONCLUSIONS/SIGNIFICANCE: These findings, alongside country funding gaps, domestic funding and implementation capacity and equity considerations, should inform strategies and policies for international donors, national governments and
Lithological and Surface Geometry Joint Inversions Using Multi-Objective Global Optimization Methods
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.
Southall, Hugh L.; O'Donnell, Teresa H.; Derov, John S.
2010-04-01
EGO is an evolutionary, data-adaptive algorithm which can be useful for optimization problems with expensive cost functions. Many antenna design problems qualify since complex computational electromagnetics (CEM) simulations can take significant resources. This makes evolutionary algorithms such as genetic algorithms (GA) or particle swarm optimization (PSO) problematic since iterations of large populations are required. In this paper we discuss multiparameter optimization of a wideband, single-element antenna over a metamaterial ground plane and the interfacing of EGO (optimization) with a full-wave CEM simulation (cost function evaluation).
Pechak, Celia M; Thompson, Mary
2009-11-01
There is growing involvement by US clinicians, faculty members, and students in global health initiatives, including international service-learning (ISL). Limited research has been done to examine the profession's increasing global engagement, or the ISL phenomenon in particular, and no research has been done to determine best practices. This study was intended as an early step in the examination of the physical therapy profession's role and activities in the global health arena within and beyond academics. The purposes of this study were: (1) to identify and analyze the common structures and processes among established ISL programs within physical therapist education programs and (2) to develop a conceptual model of optimal ISL within physical therapist education programs. A descriptive, exploratory study was completed using grounded theory. Telephone interviews were completed with 14 faculty members who had been involved in international service, international learning, or ISL in physical therapist education programs. Interviews were transcribed, and transcriptions were analyzed using the grounded theory method. Four major themes emerged from the data: structure, reciprocity, relationship, and sustainability. A conceptual model of and a proposed definition for optimal ISL in physical therapist education were developed. Seven essential components of the conceptual model are: a partner that understands the role of physical therapy, community-identified needs, explicit service and learning objectives, reflection, preparation, risk management, and service and learning outcome measures. Essential consequences are positive effects on students and community. The conceptual model and definition of optimal ISL can be used to direct development of new ISL programs and to improve existing programs. In addition, they can offer substantive guidance to any physical therapist involved in global health initiatives.
Jarrar, Mu'taman; Abdul Rahman, Hamzah; Don, Mohammad Sobri
2015-10-20
Demand for health care service has significantly increased, while the quality of healthcare and patient safety has become national and international priorities. This paper aims to identify the gaps and the current initiatives for optimizing the quality of care and patient safety in Malaysia. Review of the current literature. Highly cited articles were used as the basis to retrieve and review the current initiatives for optimizing the quality of care and patient safety. The country health plan of Ministry of Health (MOH) Malaysia and the MOH Malaysia Annual Reports were reviewed. The MOH has set four strategies for optimizing quality and sustaining quality of life. The 10th Malaysia Health Plan promotes the theme "1 Care for 1 Malaysia" in order to sustain the quality of care. Despite of these efforts, the total number of complaints received by the medico-legal section of the MOH Malaysia is increasing. The current global initiatives indicted that quality performance generally belong to three main categories: patient; staffing; and working environment related factors. There is no single intervention for optimizing quality of care to maintain patient safety. Multidimensional efforts and interventions are recommended in order to optimize the quality of care and patient safety in Malaysia.
罗袆青; 袁希钢
2008-01-01
The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna-fives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolu-tionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are presented to illustrate the global optimization of integrated water networks using the proposed algorithm.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
Liu, Liang; Chua, Kee-Chaing
2012-01-01
Characterizing the global maximum of weighted sum-rate (WSR) for the K-user Gaussian interference channel (GIC), with the interference treated as Gaussian noise, is a key problem in wireless communication. However, due to the users' mutual interference, this problem is in general non-convex and thus cannot be solved directly by conventional convex optimization techniques. In this paper, by jointly utilizing the monotonic optimization and rate profile techniques, we develop a new framework to obtain the globally optimal power control and/or beamforming solutions to the WSR maximization problems for the GICs with single-antenna transmitters and single-antenna receivers (SISO), single-antenna transmitters and multi-antenna receivers (SIMO), or multi-antenna transmitters and single-antenna receivers (MISO). Different from prior work, this paper proposes to maximize the WSR in the achievable rate region of the GIC directly by exploiting the facts that the achievable rate region is a "normal" set and the users' WSR...
Global Optimization of Damping Ring Designs Using a Multi-Objective Evolutionary Algorithm
Emery, Louis
2005-01-01
Several damping ring designs for the International Linear Collider have been proposed recently. Some of the specifications, such as circumference and bunch train, are not fixed yet. Designers must make a choice anyway, select a geometry type (dog-bone or circular), an arc cell type (TME or FODO), and optimize linear and nonlinear part of the optics. The design process include straightforward steps (usually the linear optics), and some steps not so straightforward (when nonlinear optics optimization is affected by the linear optics). A first attempt at automating this process for the linear optics is reported. We first recognize that the optics is defined by just a few primary parameters (e.g., phase advance per cell) that determine the rest (e.g., quadrupole strength). In addition to the exact specification of circumference, equilibrium emittance and damping time there are some other quantities which could be optimized that may conflict with each other. A multiobjective genetic optimizer solves this problem b...
On unified modeling, theory, and method for solving multi-scale global optimization problems
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.
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
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
Global optimization of manipulator base placement by means of rapidly-exploring random tree
赵京
2016-01-01
Due to the interrelationship between the base placement of the manipulator and its operation ob-ject , it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base location.A new method is presented to optimize the base placement of manipulators through motion planning optimization and location optimization in the feasible area for manipulators. Firstly, research problems and contents are outlined.And then the feasible area for the manipulator base installation is discussed.Next, index depended on the joint movements and used to evaluate the kinematic performance of manipulators is defined.Although the mentioned indices in last section are regarded as the cost function of the latter,rapidly-exploring random tree ( RRT) and rapidly-ex-ploring random tree*( RRT*) algorithms are analyzed.And then, the proposed optimization method of manipulator base placement is studied by means of simulation research based on kinematic per-formance criteria.Finally, the conclusions could be proved effective from the simulation results.
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness......In a DC microgrid, several paralleled conversion systems are installed in distributed substations for transferring power from external grid to a DC microgrid. Droop control is used for the distributed load sharing among all the DC/DC converters. Considering the typical efficiency feature of power...... electronic converters, optimization method can be implemented in tertiary level for improving the overall system efficiency. However, optimization purposes usually require centralized communication, data acquisition and computation which might be either impractical or costly for dispersed systems...
Multi-objective global optimization of a butterfly valve using genetic algorithms.
Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio
2016-07-01
A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints
Hinckley, David; Englander, Jacob; Hitt, Darren
2015-01-01
Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.
Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck
2016-01-01
This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process.......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri......-objective problems having 10+ design variables are both highly constrained, nonlinear and non-smooth but nevertheless the algorithm converges to the Pareto-front within a hours of computation (20k function evaluations). Additionally, the robustness and convergence speed of the algorithm are investigated using...
Global optimization for integrated design and control of computationally expensive process models
Egea, J.A.; Vries, D.; Alonso, A.A.; Banga, J.R.
2007-01-01
The problem of integrated design and control optimization of process plants is discussed in this paper. We consider it as a nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently multimodal and "costly" (i.e., computationally expensive to ev
Global optimization for integrated design and control of computationally expensive process models
Egea, J.A.; Vries, D.; Alonso, A.A.; Banga, J.R.
2007-01-01
The problem of integrated design and control optimization of process plants is discussed in this paper. We consider it as a nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently multimodal and "costly" (i.e., computationally expensive to ev
Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael
2009-09-01
There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.
Akemi Gálvez
2014-01-01
for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
Kiyotaka Masuda
2016-01-01
In Japan, greenhouse gas emissions from rice production, especially CH4 emissions in rice paddy fields, are the primary contributors to global warming from agriculture. When prolonged midseason drainage for mitigating CH4 emissions from rice paddy fields is practiced with environmentally friendly rice production based on reduced use of synthetic pesticides and chemical fertilizers, Japanese rice farmers can receive an agri-environmental direct payment. This paper examines the economic and env...
A global earthquake discrimination scheme to optimize ground-motion prediction equation selection
Garcia, Daniel; Wald, David J.; Hearne, Michael
2012-01-01
We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.
Kiyotaka Masuda
2016-06-01
Full Text Available In Japan, greenhouse gas emissions from rice production, especially CH4 emissions in rice paddy fields, are the primary contributors to global warming from agriculture. When prolonged midseason drainage for mitigating CH4 emissions from rice paddy fields is practiced with environmentally friendly rice production based on reduced use of synthetic pesticides and chemical fertilizers, Japanese rice farmers can receive an agri-environmental direct payment. This paper examines the economic and environmental effects of the agri-environmental direct payment on the adoption of a measure to mitigate global warming in Japanese rice farms using a combined application of linear programming and life cycle assessment at the farm scale. Eco-efficiency, which is defined as net farm income divided by global warming potential, is used as an integrated indicator for assessing the economic and environmental feasibilities. The results show that under the current direct payment level, the prolonged midseason drainage technique does not improve the eco-efficiency of Japanese rice farms because the practice of this technique in environmentally friendly rice production causes large economic disadvantages in exchange for small environmental advantages. The direct payment rates for agri-environmental measures should be determined based on the condition that environmentally friendly agricultural practices improve eco-efficiency compared with conventional agriculture.
Englander, Arnold C.; Englander, Jacob A.
2017-01-01
Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.
Solving non-standard packing problems by global optimization and heuristics
Fasano, Giorgio
2014-01-01
This book results from a long-term research effort aimed at tackling complex non-standard packing issues which arise in space engineering. The main research objective is to optimize cargo loading and arrangement, in compliance with a set of stringent rules. Complicated geometrical aspects are also taken into account, in addition to balancing conditions based on attitude control specifications. Chapter 1 introduces the class of non-standard packing problems studied. Chapter 2 gives a detailed explanation of a general model for the orthogonal packing of tetris-like items in a convex domain. A number of additional conditions are looked at in depth, including the prefixed orientation of subsets of items, the presence of unusable holes, separation planes and structural elements, relative distance bounds as well as static and dynamic balancing requirements. The relative feasibility sub-problem which is a special case that does not have an optimization criterion is discussed in Chapter 3. This setting can be exploit...
Clausen, Jens; Zilinskas, A,
2002-01-01
two schemes for sampling points of the function: midpoint sampling and vertex sampling. The convergence of the algorithm is proved, and numerical results are presented for the two dimensional case, for which also a special initial covering is presented. (C) 2002 Elsevier Science Ltd. All rights......We consider the problem of optimizing a Lipshitzian function. The branch and bound technique is a well-known solution method, and the key components for this are the subdivision scheme, the bound calculation scheme, and the initialization. For Lipschitzian optimization, the bound calculations...... are based on the sampling of function values. We propose a branch and bound algorithm based on regular simplexes. Initially, the domain in question is covered with regular simplexes, and our subdivision scheme maintains this property. The bound calculation becomes both simple and efficient, and we describe...
Miyazaki, Takahiko; Akisawa, Atsushi; Kashiwagi, Takao; Akahira, Akira
The study used the particle swarm optimization to maximize the specific cooling capacity (SCC) of a single-stage adsorption chiller, as well as to maximize the coefficient of performance (COP) at part load conditions of the chiller. The cycle time, which consists of adsorption/desorption time and pre-heating/ pre-cooling time, was chosen as a design parameter. The simulation results of a mathematical model showed a good agreement with experimental results on SCC and COP. It was shown that the SCC could be improved by the optimum cycle time as much as by 30% compared with that by the fixed cycle time. It was also presented that the part load COP would be significantly increased by the cycle time optimization at part load conditions.
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
2017-06-01
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. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
Global Binary Optimization on Graphs for Classification of High Dimensional Data
2014-09-01
Gilboa, G., Osher, S.: Nonlocal operators with ap- plications to image processing. Multiscale Model . Simul. 7(3), 1005–1028 (2008) 24. Goldberg, A.V...high dimen- sional data into two classes. It combines recent convex optimization methods from imaging with recent graph based variational models for data...benchmark data sets show that our models produce segmen- tation results that are comparable with or outper- form the state-of-the-art algorithms. In
Zoric, Nenad; Livshits, Irina; Dilworth, Don; Okishev, Sergey
2017-02-01
This paper describes a method for designing an ultraviolet (UV) projection lens for microlithography. Our approach for meeting this objective is to use a starting design automatically obtained by the DSEARCH feature in the SYNOPSYS™ lens design program. We describe the steps for getting a desired starting point for the projection lens and discuss optimization problems unique to this system, where the two parts of the projection lens are designed independently.
Chevrot, Sébastien; Martin, Roland; Komatitsch, Dimitri
2012-12-01
Wavelets are extremely powerful to compress the information contained in finite-frequency sensitivity kernels and tomographic models. This interesting property opens the perspective of reducing the size of global tomographic inverse problems by one to two orders of magnitude. However, introducing wavelets into global tomographic problems raises the problem of computing fast wavelet transforms in spherical geometry. Using a Cartesian cubed sphere mapping, which grids the surface of the sphere with six blocks or 'chunks', we define a new algorithm to implement fast wavelet transforms with the lifting scheme. This algorithm is simple and flexible, and can handle any family of discrete orthogonal or bi-orthogonal wavelets. Since wavelet coefficients are local in space and scale, aliasing effects resulting from a parametrization with global functions such as spherical harmonics are avoided. The sparsity of tomographic models expanded in wavelet bases implies that it is possible to exploit the power of compressed sensing to retrieve Earth's internal structures optimally. This approach involves minimizing a combination of a ℓ2 norm for data residuals and a ℓ1 norm for model wavelet coefficients, which can be achieved through relatively minor modifications of the algorithms that are currently used to solve the tomographic inverse problem.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Pearce, Charles
2009-01-01
Focuses on mathematical structure, and on real-world applications. This book includes developments in several optimization-related topics such as decision theory, linear programming, turnpike theory, duality theory, convex analysis, and queuing theory.
Loizzo, Joseph
2009-08-01
This overview surveys the new optimism about the aging mind/brain, focusing on the potential for self-regulation practices to advance research in stress-protection and optimal health. It reviews recent findings and offers a research framework. The review links the age-related biology of stress and regeneration to the variability of mind/brain function found under a range of conditions from trauma to enrichment. The framework maps this variation along a biphasic continuum from atrophic dysfunction to peak performance. It adopts the concept of allostatic load as a measure of the wear-and-tear caused by stress, and environmental enrichment as a measure of the use-dependent enhancement caused by positive reinforcement. It frames the dissociation, aversive affect and stereotyped reactions linked with stress as cognitive, affective and behavioral forms of allostatic drag; and the association, positive affect, and creative responses in enrichment as forms of allostatic lift. It views the human mind/brain as a heterarchy of higher intelligence systems that shift between a conservative, egocentric mode heightening self-preservation and memory and a generative, altruistic mode heightening self-correction and learning. Cultural practices like meditation and psychotherapy work by teaching the self-regulation of shifts from the conservative to the generative mode. This involves a systems shift from allostatic drag to allostatic lift, minimizing wear-and-tear and optimizing plasticity and learning. For cultural practices to speed research and application, a universal typology is needed. This framework includes a typology aligning current brain models of stress and learning with traditional Indo-Tibetan models of meditative stress-cessation and learning enrichment.
Optimization of semi-global stereo matching for hardware module implementation
Roszkowski, Mikołaj
2014-11-01
Stereo vision is one of the most intensively studied areas in the field of computer vision. It allows the creation of a 3D model of a scene given two images of the scene taken with optical cameras. Although the number of stereo algorithms keeps increasing, not many are suitable candidates for hardware implementations that could guarantee real-time processing in embedded systems. One of such algorithms is semi-global matching, which seems to balance well the quality of the disparity map and computational complexity. However, it still has quite high memory requirements, which can be a problem if the low-cost FPGAs are to be used. This is because they often suffer from a low external DRAM memory throughput. In this article, a few methods to reduce both the semi-global matching algorithm complexity and memory usage, and thus required bandwidth, are proposed. First of all, it is shown that a simple pyramid matching scheme can be used to efficiently reduce the number of disparities checked per pixel. Secondly, a method of dividing the image into independent blocks is proposed, which allows the reduction of the amount of memories required by the algorithm. Finally the exact requirements for the bandwidth and the size of the on-chip memories are given.
Rasmussen, Marie-Louise Højlund; Stolpe, Mathias
2008-01-01
the physics, and the cuts (Combinatorial Benders’ and projected Chvátal–Gomory) come from an understanding of the particular mathematical structure of the reformulation. The impact of a stronger representation is investigated on several truss topology optimization problems in two and three dimensions....... to a mixed-integer linear program, which is solved with a parallel implementation of branch-and-bound. Additional valid inequalities and cuts are introduced to give a stronger representation of the problem, which improves convergence and speed up of the parallel method. The valid inequalities represent...
Global Optimization Algorithm for Nonlinear Sum of Ratios Problems%非线性比式和问题的全局优化算法
焦红伟; 郭运瑞; 陈永强
2008-01-01
In this paper,a global optimization algorithm is proposed for nonlinear sum of ratios problem(P).The algorithm works by globally solving problem(P1)that is equivalent to problem(P),by utilizing linearization technique a linear relaxation programming of the(P1)is then obtained.The proposed algorithm is convergent to the global minimum of(P1)through the successive refinement of linear relaxation of the feasible region of objective function and solutions of a series of linear relaxation programming.Numerical results indicate that the proposed algorithm is feasible and can be used to globally solve nonlinear sum of ratios problems(P).
Ma, Hongliang; Xu, Shijie
2016-11-01
By defining two open-time impulse points, the optimization of a two-impulse, open-time terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit is proposed in this paper. The purpose of optimization is to minimize the velocity increment for a terminal elliptic-reference-orbit rendezvous and docking. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The non-linear Tschauner- Hempel(TH) equations of the state transitions for a terminal elliptic target orbit are transformed form time domain to target orbital true anomaly domain. Their homogenous solutions and approximate state transition matrix for the control with a short true anomaly interval can be used to avoid interval integration. The interval branch and bound optimization algorithm is introduced for solving the presented rendezvous and docking optimization problem and optimizing two open-time impulse points and thruster pulse amplitudes, which systematically eliminates parts of the control and open-time input spaces that do not satisfy the path and final time state constraints. Several numerical examples are undertaken to validate the interval optimization algorithm. The results indicate that the sufficiently narrow spaces containing the global optimization solution for the open-time two-impulse terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit can be obtained by the interval algorithm (IA). Combining the gradient-based method, the global optimization solution for the discontinuous nonconvex optimization problem in the specifically remained search space can be found. Interval analysis is shown to be a useful tool and preponderant in the discontinuous nonconvex optimization problem of the terminal rendezvous and
Export dynamics as an optimal growth problem in the network of global economy
Caraglio, Michele; 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 cali...
Optimizing Global Coronal Magnetic Field Models Using Image-Based Constraints
Jones, Shaela I; Uritsky, Vadim M
2015-01-01
The coronal magnetic field directly or indirectly affects a majority of the phenomena studied in space physics. It provides energy for coronal heating, controls the release of coronal mass ejections (CMEs), 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 which could be derived from coronal images. Here we report promising initial tests of this approach on two theoretical problems, and discuss opportunities for application.
Rahman, Tuan A. Z.; Jalil, N. A. Abdul; As'arry, A.; Raja Ahmad, R. K.
2017-06-01
Support vector machine (SVM) has been known as one-state-of-the-art pattern recognition method. However, the SVM performance is particularly influenced byits parameter selection. This paper presents the parameter optimization of an SVM classifier using chaos-enhanced stochastic fractal search (SFS) algorithm to classify conditions of a ball bearing. The vibration data for normal and damaged conditions of the ball bearing system obtained from the Case Western Reserve University Bearing Data Centre. Features based on time and frequency domains were generated to characterize the ball bearing conditions. The performance of chaos-enhanced SFS algorithms in comparison to their predecessor algorithm is evaluated. In conclusion, the injection of chaotic maps into SFS algorithm improved its convergence speed and searching accuracy based on the statistical results of CEC 2015 benchmark test suites and their application to ball bearing fault diagnosis.
Towards a globally optimized crop distribution: Integrating water use, nutrition, and economic value
Davis, K. F.; Seveso, A.; Rulli, M. C.; D'Odorico, P.
2016-12-01
Human demand for crop production is expected to increase substantially in the coming decades as a result of population growth, richer diets and biofuel use. In order for food production to keep pace, unprecedented amounts of resources - water, fertilizers, energy - will be required. This has led to calls for `sustainable intensification' in which yields are increased on existing croplands while seeking to minimize impacts on water and other agricultural resources. Recent studies have quantified aspects of this, showing that there is a large potential to improve crop yields and increase harvest frequencies to better meet human demand. Though promising, both solutions would necessitate large additional inputs of water and fertilizer in order to be achieved under current technologies. However, the question of whether the current distribution of crops is, in fact, the best for realizing sustainable production has not been considered to date. To this end, we ask: Is it possible to increase crop production and economic value while minimizing water demand by simply growing crops where soil and climate conditions are best suited? Here we use maps of yields and evapotranspiration for 14 major food crops to identify differences between current crop distributions and where they can most suitably be planted. By redistributing crops across currently cultivated lands, we determine the potential improvements in calorie (+12%) and protein (+51%) production, economic output (+41%) and water demand (-5%). This approach can also incorporate the impact of future climate on cropland suitability, and as such, be used to provide optimized cropping patterns under climate change. Thus, our study provides a novel tool towards achieving sustainable intensification that can be used to recommend optimal crop distributions in the face of a changing climate while simultaneously accounting for food security, freshwater resources, and livelihoods.
Contact-assisted protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Cheng, Qianyi; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
Semantic Segmentation of Forest Stands of Pure Species as a Global Optimization Problem
Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V.
2017-05-01
Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.
Guiding automated NMR structure determination using a global optimization metric, the NMR DP score
Huang, Yuanpeng Janet, E-mail: yphuang@cabm.rutgers.edu; Mao, Binchen; Xu, Fei; Montelione, Gaetano T., E-mail: gtm@rutgers.edu [Rutgers, The State University of New Jersey, Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium (United States)
2015-08-15
ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD–NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases {sup 15}N–{sup 1}H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD–NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.
Sergey V. Zykov
2012-04-01
Full Text Available It is generally known that software system development lifecycle (SSDL should be managed adequately. The global economy crisis and subsequent depression have taught us certain lessons on the subject, which is so vital for enterprises. The paper presents the adaptive methodology of enterprise SSDL, which allows to avoid "local crises" while producing large-scale software. The methodology is based on extracting common ERP module level patterns and applying them to series of heterogeneous implementations. The approach includes a lifecycle model, which extends conventional spiral model by formal data representation/management models and DSL-based "low-level" CASE tools supporting the formalisms. The methodology has been successfully implemented as a series of portal-based ERP systems in ITERA oil-and-gas corporation, and in a number of trading/banking enterprise applications for other enterprises. Semantic network-based airline dispatch system, and a 6D-model-driven nuclear power plant construction support system are currently in progress. Combining various SSDL models is discussed. Terms-and-cost reduction factors are examined. Correcting SSDL according to project size and scope is overviewed. The so-called “human factor errors” resulting from non-systematic SSDL approach, and their influencing crisis and depression, are analyzed. The ways to systematic and efficient SSDL are outlined. Troubleshooting advises are given for the problems concerned.
Wang, Hong; Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Yang, Yongguo
2017-09-01
This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950-2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.
Arkoprovo Biswas
2011-07-01
Full Text Available In the presence of conducting inhomogeneities in near-surface structures, apparent resistivity data in magnetotelluric sounding can be severely distorted. This is due to electric fields generated from boundary charges on surficial inhomogeneities. Such distortion persists throughout the entire recording range and is known as static shift in magnetotellurics. Frequency-independent static shifts manifest as vertical, parallel shifts that occur in plots of the dual logarithmic scale of apparent resistivity versus time period. The phase of magnetotelluric sounding data remains unaffected by the static shift and can be used to remove the static shift to some extent. However, individual inversion of phase data yields highly nonunique results, and alone it will not work to correctly remove the static shift. Inversions of uncorrected magnetotelluric data yield erroneous and unreliable estimations, while static-shift-corrected magnetotelluric data provide better and reliable estimations of the resistivities and thicknesses of subsurface structures. In the present study, static shift (a frequency-independent real constant is also considered as one of the model parameters and is optimized together with other model parameters (resistivity and thickness using the very fast simulated annealing global inversion technique. This implies that model parameters are determined simultaneously with the estimate of the static shift in the data. Synthetic and noisy data generated for a number of models are interpreted, to demonstrate the efficacy of the approach to yield reliable estimates of subsurface structures when the apparent resistivity data are affected by static shift. Individual inversions of static-shift-affected apparent resistivity data and phase data yield unreliable estimations of the model parameters. Furthermore, the estimated model parameters after individual data inversions do not show any systematic correlations with the amount of static shift in the
Jia Hui Ong
2016-07-01
Full Text Available Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluation
Moment-tensor solutions estimated using optimal filter theory: Global seismicity, 2001
Sipkin, S.A.; Bufe, C.G.; Zirbes, M.D.
2003-01-01
This paper is the 12th in a series published yearly containing moment-tensor solutions computed at the US Geological Survey using an algorithm based on the theory of optimal filter design (Sipkin, 1982 and Sipkin, 1986b). An inversion has been attempted for all earthquakes with a magnitude, mb or MS, of 5.5 or greater. Previous listings include solutions for earthquakes that occurred from 1981 to 2000 (Sipkin, 1986b; Sipkin and Needham, 1989, Sipkin and Needham, 1991, Sipkin and Needham, 1992, Sipkin and Needham, 1993, Sipkin and Needham, 1994a and Sipkin and Needham, 1994b; Sipkin and Zirbes, 1996 and Sipkin and Zirbes, 1997; Sipkin et al., 1998, Sipkin et al., 1999, Sipkin et al., 2000a, Sipkin et al., 2000b and Sipkin et al., 2002).The entire USGS moment-tensor catalog can be obtained via anonymous FTP at ftp://ghtftp.cr.usgs.gov. After logging on, change directory to “momten”. This directory contains two compressed ASCII files that contain the finalized solutions, “mt.lis.Z” and “fmech.lis.Z”. “mt.lis.Z” contains the elements of the moment tensors along with detailed event information; “fmech.lis.Z” contains the decompositions into the principal axes and best double-couples. The fast moment-tensor solutions for more recent events that have not yet been finalized and added to the catalog, are gathered by month in the files “jan01.lis.Z”, etc. “fmech.doc.Z” describes the various fields.
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi
2016-04-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
Pleban, Dariusz
2014-01-01
This paper describes the results of a study aimed at developing a tool for optimizing the location of machinery and workstations. A global index of acoustic assessment of machines was developed for this purpose. This index and a genetic algorithm were used in a computer tool for predicting noise emission of machines as well as optimizing the location of machines and workstations in industrial rooms. The results of laboratory and simulation tests demonstrate that the developed global index and the genetic algorithm support measures aimed at noise reduction at workstations.
Simpson, J. J.; Taflove, A.
2005-12-01
We report a finite-difference time-domain (FDTD) computational solution of Maxwell's equations [1] that models the possibility of detecting and characterizing ionospheric disturbances above seismic regions. Specifically, we study anomalies in Schumann resonance spectra in the extremely low frequency (ELF) range below 30 Hz as observed in Japan caused by a hypothetical cylindrical ionospheric disturbance above Taiwan. We consider excitation of the global Earth-ionosphere waveguide by lightning in three major thunderstorm regions of the world: Southeast Asia, South America (Amazon region), and Africa. Furthermore, we investigate varying geometries and characteristics of the ionospheric disturbance above Taiwan. The FDTD technique used in this study enables a direct, full-vector, three-dimensional (3-D) time-domain Maxwell's equations calculation of round-the-world ELF propagation accounting for arbitrary horizontal as well as vertical geometrical and electrical inhomogeneities and anisotropies of the excitation, ionosphere, lithosphere, and oceans. Our entire-Earth model grids the annular lithosphere-atmosphere volume within 100 km of sea level, and contains over 6,500,000 grid-points (63 km laterally between adjacent grid points, 5 km radial resolution). We use our recently developed spherical geodesic gridding technique having a spatial discretization best described as resembling the surface of a soccer ball [2]. The grid is comprised entirely of hexagonal cells except for a small fixed number of pentagonal cells needed for completion. Grid-cell areas and locations are optimized to yield a smoothly varying area difference between adjacent cells, thereby maximizing numerical convergence. We compare our calculated results with measured data prior to the Chi-Chi earthquake in Taiwan as reported by Hayakawa et. al. [3]. Acknowledgement This work was suggested by Dr. Masashi Hayakawa, University of Electro-Communications, Chofugaoka, Chofu Tokyo. References [1] A
Carroll, Rosemary W. H.; Pohll, Greg M.; Earman, Sam; Hershey, Ronald L.
2007-10-01
SummaryAs part of a larger study to estimate groundwater recharge volumes in the area of the eastern Nevada Test Site (NTS), [Campana, M.E., 1975. Finite-state models of transport phenomena in hydrologic systems, PhD Dissertation: University of Arizona, Tucson] Discrete-state compartment model (DSCM) was re-coded to simulate steady-state groundwater concentrations of a conservative tracer. It was then dynamically linked with the shuffled complex evolution (SCE) optimization algorithm [Duan, Q., Soroosh, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28(4), 1015-1031] in which both flow direction and magnitude were adjusted to minimize errors in predicted tracer concentrations. Code validation on a simple four-celled model showed the algorithm consistent in model predictions and capable of reproducing expected cell outflows with relatively little error. The DSCM-SCE code was then applied to a 15-basin (cell) eastern NTS model developed for the DSCM. Auto-calibration of the NTS model was run given two modeling scenarios, (a) assuming known groundwater flow directions and solving only for magnitudes and, (b) solving for groundwater flow directions and magnitudes. The SCE is a fairly robust algorithm, unlike simulated annealing or modified Gauss-Newton approaches. The DSCM-SCE improves upon its original counterpart by being more user-friendly and by auto-calibrating complex models in minutes to hours. While the DSCM-SCE can provide numerical support to a working hypothesis, it can not definitively define a flow system based solely on δD values given few hydrogeologic constraints on boundary conditions and cell-to-cell interactions.
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.
2012-02-01
of-the-ground model ( Frankenstein and Koenig, 2004), and a sixteen parameter Gridded Surface Subsurface Hydrologic Analysis (GSSHA) (Downer and...efficient global minimization. Journal of Optimization Theory and its Applications, 76 (3), 501-521. Frankenstein , S., and G. Koenig. 2004. Fast All... Frankenstein , and C. W. Downer. 2009. Efficient Levenberg- Marquardt Method Based Model Independent Calibration. Environmental Modelling & Software (24
Keresztes, Janos C; John Koshel, R; D'huys, Karlien; De Ketelaere, Bart; Audenaert, Jan; Goos, Peter; Saeys, Wouter
2016-12-26
A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.
Tatsumi, Keiji
2017-06-01
Recently, the gradient method with perturbation (GP) was proposed for metaheuristic methods of solving continuous global optimization problems. Its updating system based on the steepest descent method is chaotic because of its perturbations along the standard basis vectors, which can strengthen the diversification of search. The sufficient condition for its chaoticity was theoretically shown, which implies that two kinds of influence degrees of the perturbations in the updating system should be greater than some constants. In this paper, we extend the updating system of the GP into a more general one for metaheuristic methods, which does not necessarily require the descent direction of the objective function, and which can have perturbations along appropriate orthogonal basis vectors for each problem. Furthermore, since the condition for the chaoticity shown in the previous work is too restricted for practical use, we theoretically show a weaker sufficient condition for the extended system, which is derived by varying the constant lower bounds for the two kinds of influence degrees.
Zhang, N; Chen, F Y; Wu, X Q
2015-07-07
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is -3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells.
Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU
Guangyuan Kan
2016-01-01
Full Text Available The famous global optimization SCE-UA method, which has been widely used in the field of environmental model parameter calibration, is an effective and robust method. However, the SCE-UA method has a high computational load which prohibits the application of SCE-UA to high dimensional and complex problems. In recent years, the hardware of computer, such as multi-core CPUs and many-core GPUs, improves significantly. These much more powerful new hardware and their software ecosystems provide an opportunity to accelerate the SCE-UA method. In this paper, we proposed two parallel SCE-UA methods and implemented them on Intel multi-core CPU and NVIDIA many-core GPU by OpenMP and CUDA Fortran, respectively. The Griewank benchmark function was adopted in this paper to test and compare the performances of the serial and parallel SCE-UA methods. According to the results of the comparison, some useful advises were given to direct how to properly use the parallel SCE-UA methods.
E.L. Korenromp (Eline); P. Glaziou (Philippe); C. Fitzpatrick (Christopher); K. Floyd (Katherine); M. Hosseini (Mehran); M.C. Raviglione (Mario); R. Atun (Rifat); B. Williams (Brian)
2012-01-01
textabstractBackground: The Global Plan to Stop TB estimates funding required in low- and middle-income countries to achieve TB control targets set by the Stop TB Partnership within the context of the Millennium Development Goals. We estimate the contribution and impact of Global Fund investments un
Kuhn, Sebastian [Gelsenwasser AG, Gelsenkirchen (Germany)
2012-06-15
PriceForwardCurves, the commonly used abbreviation is PFCs, represent the expected future price development of commodities. Thereby, they base on current market prices as well as historical information. In addition to constructing an arbitration free curve harmonizing with current market data, seasonal effects seriously influence the resulting PFC. The article at hand introduces a quadratic optimization model in its general description. Its optimal solution then reflects the implicitly given seasonal patterns best possible. The optimization model is in a description allowing to verifiably reach the global optimal solution with standard solvers. Subsequently, the computed seasonal function is transformed in an analytic form using the discrete Fourier transformation. Numerical computations are presented for high caloric gas in the market area NCG, because the gas market is particularly subjected to strong seasonal trends. Thereby, the official trade data of the EEX are used. (orig.)
Hendrix, E.M.T.
1998-01-01
In many research situations where mathematical models are used, researchers try to find parameter values such that a given performance criterion is at an optimum. If the parameters can be varied in a continuous way, this in general defines a so-called Nonlinear Programming Problem. Methods for Nonli
Hendrix, E.M.T.
1998-01-01
In many research situations where mathematical models are used, researchers try to find parameter values such that a given performance criterion is at an optimum. If the parameters can be varied in a continuous way, this in general defines a so-called Nonlinear Programming Problem. Methods
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan.
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-04-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm3 g- 1). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils.
Cabal Cuesta, H.
2007-07-01
The aim of this work is the analysis of the effects of the GHG external costs internalization in the energy systems. This may provide a useful tool to support decision makers to help reaching the energy systems sustain ability. External costs internalization has been carried out using two methods. First, CO{sub 2} externalities of different power generation technologies have been internalized to evaluate their effects on the economic competitiveness of these present and future technologies. The other method consisted of analysing and optimizing the global energy system, from an economic and environmental point of view, using the global energy optimization model generator, TIMES, with a time horizon of 50 years. Finally, some scenarios regarding environmental and economic strategic measures have been analysed. (Author)
Alimohammadi, Shahrouz; Cavaglieri, Daniele; Beyhaghi, Pooriya; Bewley, Thomas R.
2016-11-01
This work applies a recently developed Derivative-free optimization algorithm to derive a new mixed implicit-explicit (IMEX) time integration scheme for Computational Fluid Dynamics (CFD) simulations. This algorithm allows imposing a specified order of accuracy for the time integration and other important stability properties in the form of nonlinear constraints within the optimization problem. In this procedure, the coefficients of the IMEX scheme should satisfy a set of constraints simultaneously. Therefore, the optimization process, at each iteration, estimates the location of the optimal coefficients using a set of global surrogates, for both the objective and constraint functions, as well as a model of the uncertainty function of these surrogates based on the concept of Delaunay triangulation. This procedure has been proven to converge to the global minimum of the constrained optimization problem provided the constraints and objective functions are twice differentiable. As a result, a new third-order, low-storage IMEX Runge-Kutta time integration scheme is obtained with remarkably fast convergence. Numerical tests are then performed leveraging the turbulent channel flow simulations to validate the theoretical order of accuracy and stability properties of the new scheme.
Rossi, Giulia; Ferrando, Riccardo; Rapallo, Arnaldo; Fortunelli, Alessandro; Curley, Benjamin C.; Lloyd, Lesley D.; Johnston, Roy L.
2005-05-01
Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.
求解全局优化问题的三角进化算法%Triangle Evolution-A Hybrid Heuristic for Global Optimization
罗长童; 于波
2009-01-01
This paper presents a hybrid heuristic-triangle evolution(TE)for global optimization.It is a real coded evolutionary algorithm.As in differential evolution(DE),TE targets each individual in current population and attempts to replace it by a new better individual.However,the way of generating new individuals is different.TE generates new individuals in a NelderMead way,while the simplices used in TE is 1 or 2 dimensional.The proposed algorithm is very easy to use and efficient for global optimization problems with continuous variables.Moreover.it requires only one(explicit)control parameter.Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems.
Auluck, S K H
2014-01-01
Dense Plasma Focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding of short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool which can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited [S K H Auluck, Physics of Plasmas 20, 112501 (2013)] Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather type plasma focus fitted to thousands of automated computations, which enables construction of such design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a 4-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy ...
Hanson, Lars Peter Grüner; Adalsteinsson, E; Pfefferbaum, A
2000-01-01
Quantification of gray and white matter levels of spectroscopically visible metabolites can provide important insights into brain development and pathological conditions. Chemical shift imaging offers a gain in efficiency for estimation of global gray and white matter metabolite concentrations...
Kang, Heesuk; Hollister, Scott J; La Marca, Frank; Park, Paul; Lin, Chia-Ying
2013-10-01
Biodegradable cages have received increasing attention for their use in spinal procedures involving interbody fusion to resolve complications associated with the use of nondegradable cages, such as stress shielding and long-term foreign body reaction. However, the relatively weak initial material strength compared to permanent materials and subsequent reduction due to degradation may be problematic. To design a porous biodegradable interbody fusion cage for a preclinical large animal study that can withstand physiological loads while possessing sufficient interconnected porosity for bony bridging and fusion, we developed a multiscale topology optimization technique. Topology optimization at the macroscopic scale provides optimal structural layout that ensures mechanical strength, while optimally designed microstructures, which replace the macroscopic material layout, ensure maximum permeability. Optimally designed cages were fabricated using solid, freeform fabrication of poly(ε-caprolactone) mixed with hydroxyapatite. Compression tests revealed that the yield strength of optimized fusion cages was two times that of typical human lumbar spine loads. Computational analysis further confirmed the mechanical integrity within the human lumbar spine, although the pore structure locally underwent higher stress than yield stress. This optimization technique may be utilized to balance the complex requirements of load-bearing, stress shielding, and interconnected porosity when using biodegradable materials for fusion cages.
Auluck, S. K. H.
2014-12-01
Dense plasma focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool that can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited (Auluck 2013 Phys. Plasmas 20 112501) Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather-type plasma focus fitted to thousands of automated computations, which enables the construction of such a design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a four-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy of the drive parameter over eight decades in capacitor bank energy. The optimized geometry of plasma focus normalized to the anode radius is shown to be independent of voltage, while the optimized anode radius is shown to be related to capacitor bank inductance.
Dharmadi, Yandi; Gonzalez, Ramon
2005-04-08
HPLC optimization strategy consists of four elements; experimental design, retention modeling, quality criteria function, and optimum search method. In this paper we present a simple, superior alternative to general classes of classical resolution functions (S function) and a novel optimum search algorithm (iterative stochastic search, ISS) for HPLC optimization. Comparison of S with general classes of resolution-based quality criteria functions (Rs, Rp, and Rmin) shows superior features such as correct assessment of favorable separation conditions, preservation of peak pair contributions, elimination of arbitrary cut-off values, and a unique capability to interpret absolute significance of function values through a simple inequality. The proposed ISS algorithm is more robust than standard methods and it is easily applicable to hyperdimensional optimization. ISS also shows clear advantages in its ability to correctly identify the global optimum (instead of local optimum), with higher precision, with more efficient use of computation cycles, and with easier implementation. Successful application of S and ISS to HPLC optimization was demonstrated in the separation of representative functionalities (sugars, alcohols, and organic acids) present in microbial fermentations. Both the optimal and pathological (worst) conditions were successfully predicted and experimentally verified.
Biswas, A.; Sharma, S. P.
2012-12-01
Self-Potential anomaly is an important geophysical technique that measures the electrical potential due natural source of current in the Earth's subsurface. An inclined sheet type model is a very familiar structure associated with mineralization, fault plane, groundwater flow and many other geological features which exhibits self potential anomaly. A number of linearized and global inversion approaches have been developed for the interpretation of SP anomaly over different structures for various purposes. Mathematical expression to compute the forward response over a two-dimensional dipping sheet type structures can be described in three different ways using five variables in each case. Complexities in the inversion using three different forward approaches are different. Interpretation of self-potential anomaly using very fast simulated annealing global optimization has been developed in the present study which yielded a new insight about the uncertainty and equivalence in model parameters. Interpretation of the measured data yields the location of the causative body, depth to the top, extension, dip and quality of the causative body. In the present study, a comparative performance of three different forward approaches in the interpretation of self-potential anomaly is performed to assess the efficacy of the each approach in resolving the possible ambiguity. Even though each forward formulation yields the same forward response but optimization of different sets of variable using different forward problems poses different kinds of ambiguity in the interpretation. Performance of the three approaches in optimization has been compared and it is observed that out of three methods, one approach is best and suitable for this kind of study. Our VFSA approach has been tested on synthetic, noisy and field data for three different methods to show the efficacy and suitability of the best method. It is important to use the forward problem in the optimization that yields the
Global mnemonic enhancement optimization based on taboo searchs%基于禁忌搜索的全局记忆增强型优化方法
方学毅; 王志强; 邵之江; 王可心
2012-01-01
针对过程系统优化问题中存在多解、而目前通用高性能实时优化算法均为局部算法的情况，提出了基于禁忌搜索的全局记忆增强型优化方法（global mnemonic enhance-ment optimization, GMEO）。该方法证明了在一定条件下局部最优解是过程系统参数的连续可微函数，并采用禁忌搜索方法以背景计算的方式获得优化计算经验点上的多解信息。基于这些多解信息，GMEO可为优化计算提供全体局部最优解的逼近值。以这些逼近值为初值，可使局部优化算法达到全局优化的效果，并能有效提高过程系统运行的稳健性。基于原油混合问题的数值仿真试验验证了GMEO方法的有效性。在不过多损失实时性的前提下，优化计算的目标函数值得以显著改善。%There are many multi-solution optimization problems in process system engineeringl however, almost all practical solvers are local. This paper presents a global mnemonic enhancement optimization （GMEO） approach to endow solvers to find global solutions. This analysis shows first-order differentiable function that each local optimum is a of the system parameters, so a taboo search can be used in the background to explore the local solutions. Thus, the method is able to approximate all the local solutions of the current optimization problem. The local solvers provide global optimization by using these approximate results as initial points. This increases the chance of successful convergence, making the system more robust. Numerical tests on a pooling problem validate the method. The obiective function is significantly improved at an acceptable loss of real-time performance.
邓枫; 覃宁; 伍贻兆
2013-01-01
建立了并行EGO全局优化算法框架,并将其应用于二维和三维激波控制鼓包减阻优化设计当中.EGO全局优化算法是一类基于克里金(Kriging)代理模型的全局优化算法,具有自动平衡全局搜索与局部搜索的优点,适合于处理具有少量设计参数但是函数计算耗时很长的优化问题,例如基于计算流体力学的气动优化设计.为考察并行EGO全局优化算法的效率,选用了4个解析优化算例以及二维和三维激波控制鼓包减阻优化问题对克里金信任法、常数取代法以及罚点法等3种启发式并行策略进行了比较.数值实验表明,并行EGO全局优化算法每次取点个数可多达16个之多,加速将近16倍,其中在3种并行策略中又以罚点法效率最高,因而在计算耗时很长的气动外形优化问题当中具有很大的应用前景.%The parallel efficient global optimization (EGO) algorithm is applied to the 2-D/3-D shock control bump optimizations.The EGO algorithm is a global optimization method based on the Kriging model,which is especially designed for expensive black box functions,such as high-fidelity aerodynamic shape optimizations.Three heuristic parallel strategies including "Kriging believer","constant liar" and " imputed point" strategies are investigated based on four benchmark optimization problems and two shock control bump optimization problems.In shock control bump optimization problems,the parallel EGO algorithm is nearly 16 times faster than the serial EGO algorithm.And our research also shows that' Imputed point' strategy is the best strategy in these optimization problems.
2013-08-01
vehicle platform itself, but to external systems and platforms. All power systems can be connected wirelessly, or through a microgrid . Therefore...restoration and contingency operations. Because a microgrid is designed for a period of time, our optimization problem considers factors such as cost...to operate, maintenance, reliability, repair time and logistics. This paper also focuses on optimizing the vehicle- microgrid system using these
张立平; 赖炎连
2001-01-01
A new trust region algorithm for solving convex LC1 optimization problem is presented.It is proved that the algorithm is globally convergent and the rate of convergence is superlinear under some reasonable assumptions.
2014-01-01
The aim of our paper is to contribute to the debate on optimal fiscal rules in a monetary union: in terms of global budgetary deficit, of structural budgetary deficit, or of public debt. Indeed, these rules seem to be mixed in the framework of the European Economic and Monetary Union, with the new Fiscal Compact. With the help of a simple macroeconomic model, we show that a goal in terms of public debt is the most appropriate in order to decrease the indebtedness levels, but that it could inc...
全局数值寻优的一种混合遗传算法%Hybrid Simplex-improved Genetic Algorithm for Global Numerical Optimization
任子武; 伞冶; 陈俊风
2007-01-01
In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection,competition and selection among several crossover offspring,adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three approaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experiments are included to demonstrate effectiveness of the proposed algorithm.
Hongwen He
2013-01-01
Full Text Available Energy management strategy influences the power performance and fuel economy of plug-in hybrid electric vehicles greatly. To explore the fuel-saving potential of a plug-in hybrid electric bus (PHEB, this paper searched the global optimal energy management strategy using dynamic programming (DP algorithm. Firstly, the simplified backward model of the PHEB was built which is necessary for DP algorithm. Then the torque and speed of engine and the torque of motor were selected as the control variables, and the battery state of charge (SOC was selected as the state variables. The DP solution procedure was listed, and the way was presented to find all possible control variables at every state of each stage in detail. Finally, the appropriate SOC increment is determined after quantizing the state variables, and then the optimal control of long driving distance of a specific driving cycle is replaced with the optimal control of one driving cycle, which reduces the computational time significantly and keeps the precision at the same time. The simulation results show that the fuel economy of the PEHB with the optimal energy management strategy is improved by 53.7% compared with that of the conventional bus, which can be a benchmark for the assessment of other control strategies.
X. Xiao
2010-06-01
Full Text Available Methyl chloride (CH_{3}Cl is a chlorine-containing trace gas in the atmosphere contributing significantly to stratospheric ozone depletion. Large uncertainties in estimates of its source and sink magnitudes and temporal and spatial variations currently exist. GEIA inventories and other bottom-up emission estimates are used to construct a priori maps of the surface fluxes of CH_{3}Cl. The Model of Atmospheric Transport and Chemistry (MATCH, driven by NCEP interannually varying meteorological data, is then used to simulate CH_{3}Cl mole fractions and quantify the time series of sensitivities of the mole fractions at each measurement site to the surface fluxes of various regional and global sources and sinks. We then implement the Kalman filter (with the unit pulse response method to estimate the surface fluxes on regional/global scales with monthly resolution from January 2000 to December 2004. High frequency observations from the AGAGE, SOGE, NIES, and NOAA/ESRL HATS in situ networks and low frequency observations from the NOAA/ESRL HATS flask network are used to constrain the source and sink magnitudes. The inversion results indicate global total emissions around 4100 ± 470 Gg yr^{−1} with very large emissions of 2200 ± 390 Gg yr^{−1} from tropical plants, which turn out to be the largest single source in the CH_{3}Cl budget. Relative to their a priori annual estimates, the inversion increases global annual fungal and tropical emissions, and reduces the global oceanic source. The inversion implies greater seasonal and interannual oscillations of the natural sources and sink of CH_{3}Cl compared to the a priori. The inversion also reflects the strong effects of the 2002/2003 globally widespread heat waves and droughts on global emissions from tropical plants, biomass burning and salt marshes, and on the soil sink.
X. Xiao
2009-12-01
Full Text Available Methyl chloride (CH_{3}Cl is a chlorine-containing trace gas in the atmosphere contributing significantly to stratospheric ozone depletion. Large uncertainties in estimates of its source and sink magnitudes and temporal and spatial variations currently exist. GEIA inventories and other bottom-up emission estimates are used to construct a priori maps of the surface fluxes of CH_{3}Cl. The Model of Atmospheric Transport and Chemistry (MATCH, driven by NCEP interannually varying meteorological data, is then used to simulate CH_{3}Cl mole fractions and quantify the time series of sensitivities of the mole fractions at each measurement site to the surface fluxes of various regional and global sources and sinks. We then implement the Kalman filter (with the unit pulse response method to estimate the surface fluxes on regional/global scales with monthly resolution from January 2000 to December 2004. High frequency observations from the AGAGE, SOGE, NIES, and NOAA/ESRL HATS in situ networks and low frequency observations from the NOAA/ESRL HATS flask network are used to constrain the source and sink magnitudes. The inversion results indicate global total emissions around 4100±470 Gg yr^{−1} with very large emissions of 2200±390 Gg yr^{−1} from tropical plants, which turn out to be the largest single source in the CH_{3}Cl budget. Relative to their a priori annual estimates, the inversion increases global annual fungal and tropical emissions, and reduces the global oceanic source. The inversion implies greater seasonal and interannual oscillations of the natural sources and sink of CH_{3}Cl compared to the a priori. The inversion also reflects the strong effects of the 2002/2003 globally widespread heat waves and droughts on global emissions from tropical plants, biomass burning and salt marshes, and on the soil sink.
Gressent, A.; Muhle, J.; Rigby, M. L.; Lunt, M. F.; Ganesan, A.; Prinn, R. G.; Krummel, P. B.; Fraser, P. J.; Steele, P.; Weiss, R. F.; Harth, C. M.; O'Doherty, S.; Young, D.; Park, S.; Li, S.; Yao, B.; Reimann, S.; Vollmer, M. K.; Maione, M.; Arduini, I.; Lunder, C. R.
2016-12-01
Sulfuryl fluoride (SO2F2) is used increasingly as a fumigant to replace methyl bromide (CH3Br), which was regulated under the Montreal Protocol (1986). Mühle et al., J. Geophys. Res., 2009) showed that SO2F2 had been accumulating in the global atmosphere with a growth rate of 5±1% per year from 1978 to 2007. They also determined, using the 2D AGAGE box model, that SO2F2 has a total atmospheric lifetime of 36±11 years mainly driven by the oceanic uptake. In addition, the global warming potential of SO2F2 has been estimated to be ≈4780 for a 100-year time horizon (Papadimitriou et al., J. Phys. Chem., 2008), which is similar to the CFC-11 (CCl3F) GWP. Thus it is a potent greenhouse gas and its emissions are expected to continue to increase in the future. Here we report the first estimations of the SO2F2 emissions and its ocean sink from January 2006 to the end of 2015 on both the global scale using a 3D Eulerian chemical transport model (MOZART-4) solving a Main Chain Monte Carlo (MCMC) inversion, and on the regional scale using a 3D Lagrangian dispersion model (NAME) via the reversible-jump trans-dimensional MCMC approach (Lunt et al., Geosci. Model Dev., 2016). The mole fractions calculated on the global scale are used as boundary conditions for emission calculations over the NAME regions in North America, Europe, East Asia and Australia. For this 10-year inversion we use observations from the AGAGE (Advanced Global Atmospheric Gases Experiment) starting with six stations in 2006, which are La Jolla (California), Mace Head (Ireland), Cape Grim (Australia), Ragged Point (Barbados), Trinidad Head (California) and Cape Matatula (Samoa). We then add observations from Gosan (South Korea) in 2007, Jungfraujoch (Switzerland) in 2008, Shandiangzi (China) and Ny-Alesund (Norway) in 2010, and Monte Cimone (Italy) in 2011, reducing the uncertainty associated with the regions located close to these stations. Results are compared to (i) the total global SO2F2 emissions
刘振军; 杨迪雄
2016-01-01
In recent years ,a new potential tool for global optimization ,namely ,chaos optimization algorithm (COA) ,which is based on nonlinear dynamics characteristics such as the sensitivity on initial value ,pseudo‐randomness ,ergodicity and self‐similar fractals of chaos etc ,has been widely applied in various disciplinary areas of science and technology .According to the development history of COA ,this paper reviews four kinds of major COAs focusing on their basic ideas and the research situations of engineering application :chaotic neural network optimization method ,the first type of hybrid COA (based on chaotic search) ,the second type of hybrid COA (chaotic sequence instead of random sequence ) and chaos and fractal optimization algorithm .Chaotic map was firstly introduced to neural network ,and chaotic neural network optimization method was developed ,which can solve the complex problems of global optimization ,such as combinatorial optimization .The heuristic random algorithms like genetic algorithm and particle swarm algorithm have the capability of global searching ,but they are prone to prematurity and falling into local optima .After the chaotic search concept appeared ,researchers estab‐lished the first type of hybrid COA w hich embeds chaotic search into heuristic algorithm ,and can over‐come effectively the shortcoming of premature convergence of original heuristic algorithm .Subsequently , replacing random parameters of heuristic algorithm with chaotic sequences of chaotic maps ,the second type of hybrid COA is formed .Hybrid chaos optimization algorithms facilitate to achieve rapid global convergence and improve the computational accuracy .Finally ,using the chaotic and fractal characteris‐tics ,the chaos and fractal optimization algorithm ,as a novel approach proposed from the fractal theory ,is proposed and can search for all the global optimum solutions of the optimization problem .In addition , several research directions of COA are
Fu, Rong-Huan; Zhang, Xing
2016-09-01
Supercritical carbon dioxide operated in a Brayton cycle offers a numerous of potential advantages for a power generation system, and a lot of thermodynamics analyses have been conducted to increase its efficiency. Because there are a lot of heat-absorbing and heat-lossing subprocesses in a practical thermodynamic cycle and they are implemented by heat exchangers, it will increase the gross efficiency of the whole power generation system to optimize the system combining thermodynamics and heat transfer theory. This paper analyzes the influence of the performance of heat exchangers on the actual efficiency of an ideal Brayton cycle with a simple configuration, and proposes a new method to optimize the power generation system, which aims at the minimum energy consumption. Although the method is operated only for the ideal working fluid in this paper, its merits compared to that only with thermodynamic analysis are fully shown.
Tinggui Chen
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
Wei, Ruiying; Guo, Boling; Li, Yin
2017-09-01
The Cauchy problem for the three-dimensional compressible magneto-micropolar fluid equations is considered. Existence of global-in-time smooth solutions is established under the condition that the initial data are small perturbations of some given constant state. Moreover, we obtain the time decay rates of the higher-order spatial derivatives of the solution by combining the Lp-Lq estimates for the linearized equations and the Fourier splitting method, if the initial perturbation is small in H3-norm and bounded in L1-norm.
谭冠政; 贺欢; SLOMAN Aaron
2007-01-01
A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path,and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.
Gómez Susana
2014-07-01
Full Text Available The aim of this work is to study the automatic characterization of Naturally Fractured Vuggy Reservoirs via well test analysis, using a triple porosity-dual permeability model. The inter-porosity flow parameters, the storativity ratios, as well as the permeability ratio, the wellbore storage effect, the skin and the total permeability will be identified as parameters of the model. In this work, we will perform the well test interpretation in Laplace space, using numerical algorithms to transfer the discrete real data given in fully dimensional time to Laplace space. The well test interpretation problem in Laplace space has been posed as a nonlinear least squares optimization problem with box constraints and a linear inequality constraint, which is usually solved using local Newton type methods with a trust region. However, local methods as the one used in our work called TRON or the well-known Levenberg-Marquardt method, are often not able to find an optimal solution with a good fit of the data. Also well test analysis with the triple porosity-double permeability model, like most inverse problems, can yield multiple solutions with good match to the data. To deal with these specific characteristics, we will use a global optimization algorithm called the Tunneling Method (TM. In the design of the algorithm, we take into account issues of the problem like the fact that the parameter estimation has to be done with high precision, the presence of noise in the measurements and the need to solve the problem computationally fast. We demonstrate that the use of the TM in this study, showed to be an efficient and robust alternative to solve the well test characterization, as several optimal solutions, with very good match to the data were obtained.
具有全局收敛性的改进萤火虫优化算法%Research on Improved Glowworm Swarm Optimization with Globally Convergence
马小雨; 高继勋
2013-01-01
对萤火虫优化(Glowworm swarm optimization,GSO)算法全局收敛性及其改进算法性能进行了研究.分析了GSO全局收敛性,针对其收敛效率低的缺陷,提出了一种基于族群划分的改进GSO算法,借鉴混合蛙跳算法思想,将萤火虫群体进行族群划分,局部搜索及全局信息交换的方式改善了算法性能,通过引入萤火虫移动组元概念,改进了萤火虫更新策略,在此基础上,利用混沌优化技术,对萤火虫群体进行初始化,使得算法获得较高质量的初始解群体,并证明了改进算法以概率1收敛于全局最优,最后,采用经典测试函数进行测试,仿真结果表明,改进的萤火虫优化算法在收敛速度及求解精度上有明显改善.%The global convergence of GSO ( Glowworm swarm optimization) algorithm and its improved algorithm performance are studied. The global convergence analysis of basic GSO is made. In order to improve the GSO convergence efficiency, an improved GSO (IGSO) is presented. Using shuffled frog leaping algorithm (SFLA), glowworms are divide into different ethnic groups, and local search and global information exchange method improves the GSO performance. The introduction of the concept of mobile group element is proposed in order to improve glowworm diversity. By using chaos optimization technique, glowworm groups are initialized, and the algorithm can obtain high quality initial solutions group. Finally, with the classical test functions, the simulation results show that, the GSO with hybrid behavior has better convergence speed and precision.
Wu Xingen; Zhu Yunping [Department of Radiation Oncology, St. Jude Children' s Research Hospital, Memphis, TN (United States)
2001-06-01
. Response to the second and third comments: The objective function is a big issue. Dr. Vaarkamp thinks that our cost functions seem too simple to be capable of finding any clinically meaningful solutions. We do not agree with him on this issue. Finding clinically meaningful solutions is mainly the task of optimization methods, not of just the cost function. The cost function we used is the most common least-squares function. It has been used not only in radiotherapy optimization research but also in some commercial planning systems. It can be used to impose the dose homogeneity to planning target volume (PTV), and to ensure that the doses in OARs do not surpass the maximum allowable dose (or DVH in our new investigations). In terms of beam weighting optimization, we started out with existing manually placed beams that were conformal to the PTV. The optimization procedure tends to keep those beams that will contribute to the dose uniformity within the PTV while sparing OARs. Because our patients do not normally have a 'perfectly' shaped small PTV in the exact centre of a small sized ('ideally' cubic shaped) brain, we normally do not find a solution that will eliminate all beams except two lateral beams. Radiotherapy optimization problems based on objective function are usually non-convex because of the addition of some constraints. There exist many local extremes. Traditional methods based on gradient are easily trapped into local minima. Other methods, like simulated annealing, genetic algorithm and global optimization methods, usually find better solutions, because they adopt some mechanism to avoid such local traps. Our efforts in these three papers were intended mainly to prove in principle that the new methods presented are applicable to our radiotherapy planning optimization problems. Many researchers have been devoting their efforts to similar goals. Response to the fourth comment: Because the patient contour and the tumour are not exactly
Denning, A.; Lokupitiya, R. S.; Zupanski, D.; Kawa, S. R.; Baker, D. F.; Doney, S. C.; Gurney, K. R.
2009-12-01
We present a system to analyze GOSAT/Tanso data using a combination of existing models of CO2 exchanges due to hourly photosynthesis and respiration, daily air-sea gas exchange, biomass burning, Fossil Fuel Emissions, and atmospheric transport. This comprehensive system allows direct comparison to the observed record of both in-situ and remotely sensed atmospheric CO2 at hourly timescales. We have previously demonstrated that a lower-resolution version of the system has good skill at replicating diurnal, synoptic, and seasonal variations over vegetated land surfaces. The system is driven by meteorological output from the NASA Goddard EOS Data Assimilation System, version 5. Surface weather from the system drives calculations of terrestrial ecosystem metabolism (radiation, precipitation, humidity, temperature) and air-sea gas exchange (wind), with other input data coming from satellite data products (e.g., fPAR and LAI from MODIS, and ocean color from SeaWiFS and MODIS). The analysis system is evaluated using synthetic data on a 2 x 2.5 degree (lat x lon) global grid. Synthetic data are sampled in cloud-free columns along the GOSAT orbital ephemeris and used to estimate multiplicative biases to component fluxes by Ensemble Data Assimilation. The system is quite successful at retrieving mechanistic estimates of spatial patterns of surface carbon fluxes on monthly and annual timescales over land, but is less skillful over the oceans.
Salceda, Susana; Barican, Arnaldo; Buscaino, Jacklyn; Goldman, Bruce; Klevenberg, Jim; Kuhn, Melissa; Lehto, Dennis; Lin, Frank; Nguyen, Phong; Park, Charles; Pearson, Francesca; Pittaro, Rick; Salodkar, Sayali; Schueren, Robert; Smith, Corey; Troup, Charles; Tsou, Dean; Vangbo, Mattias; Wunderle, Justus; King, David
2017-05-01
The RapidHIT(®) ID is a fully automated sample-to-answer system for short tandem repeat (STR)-based human identification. The RapidHIT ID has been optimized for use in decentralized environments and processes presumed single source DNA samples, generating Combined DNA Index System (CODIS)-compatible DNA profiles in less than 90min. The system is easy to use, requiring less than one minute of hands-on time. Profiles are reviewed using centralized linking software, RapidLINK™ (IntegenX, Pleasanton, CA), a software tool designed to collate DNA profiles from single or multiple RapidHIT ID systems at different geographic locations. The RapidHIT ID has been designed to employ GlobalFiler(®) Express and AmpFLSTR(®) NGMSElect™, Thermo Fisher Scientific (Waltham, MA) STR chemistries. The Developmental Validation studies were performed using GlobalFiler(®) Express with single source reference samples according to Scientific Working Group for DNA Analysis Methods guidelines. These results show that multiple RapidHIT ID systems networked with RapidLINK software form a highly reliable system for wide-scale deployment in locations such as police booking stations and border crossings enabling real-time testing of arrestees, potential human trafficking victims, and other instances where rapid turnaround is essential. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza
2013-02-07
Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.
Fabio Chiappa
2016-02-01
Full Text Available This paper reviews the theoretical aspects and the practical issues of different types of geophysical integration approaches. Moreover it shows how these approaches can be combined and optimized into the same platform. We discuss both cooperative modeling and Simultaneous Joint Inversion (SJI as complementary methods for integration of multi-domain geophysical data: these data can be collected at surface (seismic, electromagnetic, gravity as well as in borehole (composite well logs. The main intrinsic difficulties of any SJI approach are the high computational requirements, the non-uniqueness of the final models, the proper choice of the relations between the different geophysical domains, the quantitative evaluation of reliability indicators. In order to face efficiently all these problems we propose and describe here a “systemic approach”: the algorithms of modeling and SJI are merged with an integration architecture that permits the selection of workflows and links between different algorithms, the management of data and models coming from different domains, the smart visualization of partial and final results. This Quantitative Integration System (QUIS has been implemented into a complex software and hardware platform, comprising many advanced codes working in cooperation and running on powerful computer clusters. The paper is divided into two main parts. First we discuss the theoretical formulation of SJI and the key concepts of the QUIS platform. In the second part we present a synthetic SJI test and a case history of QUIS application to a real exploration problem.
Figueiro, Thiago; Choi, Kang-Hoon; Gutsch, Manuela; Freitag, Martin; Hohle, Christoph; Tortai, Jean-Hervé; Saib, Mohamed; Schiavone, Patrick
2012-11-01
In electron proximity effect correction (PEC), the quality of a correction is highly dependent on the quality of the model. Therefore it is of primary importance to have a reliable methodology to extract the parameters and assess the quality of a model. Among others the model describes how the energy of the electrons spreads out in the target material (via the Point Spread Function, PSF) as well as the influence of the resist process. There are different models available in previous studies, as well as several different approaches to obtain the appropriate value for their parameters. However, those are restricted in terms of complexity, or require a prohibitive number of measurements, which is limited for a certain PSF model. In this work, we propose a straightforward approach to obtain the value of parameters of a PSF. The methodology is general enough to apply for more sophisticated models as well. It focused on improving the three steps of model calibration procedure: First, it is using a good set of calibration patterns. Secondly, it secures the optimization step and avoids falling into a local optimum. And finally the developed method provides an improved analysis of the calibration step, which allows quantifying the quality of the model as well as enabling a comparison of different models. The methodology described in the paper is implemented as specific module in a commercial tool.
Barry, Jeremy A; Muddiman, David C
2011-12-15
Design of experiments (DOE) is a systematic and cost-effective approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response can be measured in few experiments. Herein, we describe the use of statistical DOE to improve a few of the analytical figures of merit of the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source for mass spectrometry. In a typical experiment, bovine cytochrome c was ionized via electrospray, and equine cytochrome c was desorbed and ionized by IR-MALDESI such that the ratio of equine:bovine was used as a measure of the ionization efficiency of IR-MALDESI. This response was used to rank the importance of seven source parameters including flow rate, laser fluence, laser repetition rate, ESI emitter to mass spectrometer inlet distance, sample stage height, sample plate voltage, and the sample to mass spectrometer inlet distance. A screening fractional factorial DOE was conducted to designate which of the seven parameters induced the greatest amount of change in the response. These important parameters (flow rate, stage height, sample to mass spectrometer inlet distance, and laser fluence) were then studied at higher resolution using a full factorial DOE to obtain the globally optimized combination of parameter settings. The optimum combination of settings was then compared with our previously determined settings to quantify the degree of improvement in detection limit. The limit of detection for the optimized conditions was approximately 10 attomoles compared with 100 femtomoles for the previous settings, which corresponds to a four orders of magnitude improvement in the detection limit of equine cytochrome c.
叶明; 任洪; 李鑫
2016-01-01
Integrating motor efficiency,battery state of charge (SOC)and EMCVT efficiency, the global optimization of EV equipped with EMCVT running in new Europe driving cycle (NEDC) was carried out based on dynamic programming.In this optimization,SOC and transmission ratio were state variable and decision variable respectively.Ratio shifting model was built to estimate enerG gy consumption during shifting.New optimal control strategies were achieved by taking shifting enerG gy consumption into cost function.With this control strategy,shift times and fuel consumed are both reduced.%综合考虑电机、电池、机电控制无级自动变速器效率，采用全局动态规划的方法，以电池荷电状态值为状态变量、变速器速比为决策变量，获取装备机电控制无级自动变速器的电动汽车在 NEDC工况下的全局最优控制策略。建立了机电控制无级自动变速器调速模型，获取调速过程中的能量消耗。在全局优化控制策略的基础上，将调速能量消耗纳入指标函数，进一步优化无级变速器速比和电机扭矩。优化结果表明，采取所提出的优化策略可有效减少调速次数，进一步降低系统能量消耗。
基于蚁群优化的AUV全局路径规划研究%Research on global path planning based on ant colony optimization for AUV
王宏健; 熊伟
2009-01-01
路径规划是自主式水下潜器(AUV)导航研究的重要课题,AUV可用于未知环境如海洋空间探测.在大范围海洋环境中,应用蚁群优化原理对自主式水下潜器的全局路径规划问题进行了研究.引入栅格建模方法建立了蚁群可视图模型,设计了蚁群信息素更新规则;给出了蚁群全局路径规划的操作步骤;针对蚁群规划路径不平滑问题,设计了切割算予和插点算子.仿真实验结果表明,蚁群全局规划算法非常适合于求解复杂环境中的规划问题,规划时间短、路径平滑,其原型系统可应用于非结构化无人环境监测.%Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
Optimization of the GOSAT global observation from space with region-by-region target-mode operations
kuze, A.; Suto, H.; Shiomi, K.; Kawakami, S.; Nakajima, M.
2013-12-01
observations, by uploading the pointing angles from the ground every day, TANSO-FTS can target a maximum of about 1,000 points per day. Dithering over fractional clouds area and targeting coast and islands avoiding bay and channels can increase yield rate. GOSAT has a UV band (380nm) in TANSO-CAI to observe dark land and FTS-SWIR bands has been acquiring two linear polarizations simultaneously. Multi-angle observations with forward, nadir and backward viewing with two axis pointing mirror will distinguish aerosol scattering from surface reflection and reduce aerosol related errors. The optimized target mode allocation are now considered. We will add the classification information of the target such as validation site, mega cities, volcano in the future Level 1B product to identify high bias possibility in XCO2. In addition, the geo-location information after the best estimate pointing-offset correction will be added. Lastly, after optimizing the observation locations, consistency between different gains, target brightness and aerosol optical thickness has to be confirmed. Validation other than TCCON site is also discussed.
Rodiet, Christophe; Remy, Benjamin; Degiovanni, Alain
2016-05-01
In this paper, it is shown how to select the optimal wavelengths minimizing the relative error and the standard deviation of the temperature. Furthermore, it is shown that the optimal wavelengths in mono-spectral and bi-spectral methods (for a Planck's law) can be determined by laws analogous to the displacement Wien's law. The simplicity of these laws can thus allow real-time selection of optimal wavelengths for a control/optimization of industrial processes, for example. A more general methodology to obtain the optimal wavelengths selection in a multi-spectral method (taking into account the spectral variations of the global transfer function including the emissivity variations) for temperature measurement of surfaces exhibiting non-uniform emissivity, is also presented. This latter can then find an interest in glass furnaces temperature measurement with spatiotemporal non-uniformities of emissivity, the control of biomass pyrolysis, the surface temperature measurement of buildings or heating devices, for example. The goal consists of minimizing the standard deviation of the estimated temperature (optimal design experiment). For the multi-spectral method, two cases will be treated: optimal global and optimal constrained wavelengths selection (to the spectral range of the detector, for example). The estimated temperature results obtained by different models and for different number of parameters and wavelengths are compared. These different points are treated from theoretical, numerical and experimental points of view.
基于XTP的ITER广域网链路优化方案研究%Research on optimization of global network for ITER based on XTP
马宗萼; 谭海波; 李晓风
2012-01-01
To meet the growing performance challenge of international thermonuclear experimental reactor (ITER) program' s global network, a WAN optimization scheme is proposed, combining high-speed protocol and improved transport mechanisms. Based on deep research for the Xpress transport protocol (XTP), made use of feature-rich interface provided by XTP, adjust the burst/rate model to avoid congestion effectively, marked sequence numbers of packet to improve go-back-n retransmission algo-rithm, also employed protocol spoofing to reduce the latency, some relevant protocol fields are analyzed to implement accurate QoS policy, and the characteristics of XTP are used for a flexible network improvement. The experimental results show that the solution can significantly improve the performance of China-France global network for ITER.%为了满足国际热核聚变实验堆(ITER)计划全球网络不断增长的性能需求,提出了一种结合高速协议和改进传输机制的广域网链路优化解决方案.通过分析和研究快速传输协议XTP,根据XTP提供的丰富功能接口,调整burst/rate模型有效避免拥塞产生,标记分组序号改进go-back-n重传机制,同时采用协议欺骗技术减少响应延迟,分析相关协议字段实施精确QoS策略,充分利用协议的特性对网络传输实现了灵活改进.测试结果表明该方案能够显著提高ITER中法国际链路的性能.
Global optimization of quadratic programming problem with quadratic constraints%带有二次约束二次规划问题的全局最优化
马小华; 魏飞; 高岳林
2013-01-01
According to the special structure of quadratic programming model with quadratic constraints and using the convex envelope and concave envelope of the product,the relaxation linear programming problem of quadratic programming with quadratic constraints was proposed so as to determine the lower bound of the global optimum.Using the hyper-rectangular reduction technique,the convergence speed of the branch-and-bound algorithm was accelerated.Thus,a global optimization algorithm was presented for solving quadratic programming problem with quadratic constraints and the convergence of the algorithm was proved.The new algorithm was actually an organically combined one of branch-and-bound with outer approximation.Numerical example showed that the proposed algorithm was feasible.%根据带有二次约束二次规划模型的特殊结构,利用乘积的凸包络和凹包络,给出带有二次约束二次规划问题的松弛线性规划问题,以确定全局最优值的下界,使用超矩形缩减技术以加快分支定界算法的收敛速度,从而提出一个求解带有二次约束二次规划问题的全局最优化算法,证明该算法的收敛性,这个新算法实际上是把分支定界方法与外逼近方法有机地结合起来.数值算例表明所提出的算法是可行的.
Shen, Tie; Rui, Bin; Zhou, Hong; Zhang, Ximing; Yi, Yin; Wen, Han; Zheng, Haoran; Wu, Jihui; Shi, Yunyu
2013-01-27
The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E
Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel
2016-04-01
The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences.
童小娇; 周叔子
2000-01-01
A trust region algorithm for equality constrained optimization is given in this paper.The algorithm does not enforce strict monotonicity of the merit function for every iteration.Global convergence of the algorithm is proved under the same conditions of usual trust region method.
童小娇; 周叔子
2003-01-01
This paper presents a trust region two-phase model algorithm for solving the equality and bound constrained nonlinear optimization problem. A concept of substationary point is given. Under suitable assumptions,the global convergence of this algorithm is proved without assuming the linear independence of the gradient of active constraints. A numerical example is also presented.
Carter, Patrick M.; Desmond, Jeffery S.; Akanbobnaab, Christopher; Oteng, Rockefeller A.; Rominski, Sarah; Barsan, William G.; Cunningham, Rebecca
2012-01-01
Background Although many global health programs focus on providing clinical care or medical education, improving clinical operations can have a significant effect on patient care delivery, especially in developing health systems without high-level operations management. Lean manufacturing techniques have been effective in decreasing emergency department (ED) length of stay, patient waiting times, numbers of patients leaving without being seen, and door-to-balloon times for ST-elevation myocardial infarction in developed health systems; but use of Lean in low to middle income countries with developing emergency medicine systems has not been well characterized. Objectives To describe the application of Lean manufacturing techniques to improve clinical operations at Komfo Anokye Teaching Hospital in Ghana and to identify key lessons learned to aid future global EM initiatives. Methods A three-week Lean improvement program focused on the hospital admissions process at Komfo Anokye Teaching Hospital was completed by a 14-person team in six stages: problem definition, scope of project planning, value stream mapping, root cause analysis, future state planning, and implementation planning. Results The authors identified eight lessons learned during our use of Lean to optimize the operations of an ED in a global health setting: 1) the Lean process aided in building a partnership with Ghanaian colleagues; 2) obtaining and maintaining senior institutional support is necessary and challenging; 3) addressing power differences among the team to obtain feedback from all team members is critical to successful Lean analysis; 4) choosing a manageable initial project is critical to influence long-term Lean use in a new environment; 5) data intensive Lean tools can be adapted and are effective in a less resourced health system; 6) several Lean tools focused on team problem solving techniques worked well in a low resource system without modification; 7) using Lean highlighted that
Carter, Patrick M; Desmond, Jeffery S; Akanbobnaab, Christopher; Oteng, Rockefeller A; Rominski, Sarah D; Barsan, William G; Cunningham, Rebecca M
2012-03-01
Although many global health programs focus on providing clinical care or medical education, improving clinical operations can have a significant effect on patient care delivery, especially in developing health systems without high-level operations management. Lean manufacturing techniques have been effective in decreasing emergency department (ED) length of stay, patient waiting times, numbers of patients leaving without being seen, and door-to-balloon times for ST-elevation myocardial infarction in developed health systems, but use of Lean in low to middle income countries with developing emergency medicine (EM) systems has not been well characterized. To describe the application of Lean manufacturing techniques to improve clinical operations at Komfo Anokye Teaching Hospital (KATH) in Ghana and to identify key lessons learned to aid future global EM initiatives. A 3-week Lean improvement program focused on the hospital admissions process at KATH was completed by a 14-person team in six stages: problem definition, scope of project planning, value stream mapping, root cause analysis, future state planning, and implementation planning. The authors identified eight lessons learned during our use of Lean to optimize the operations of an ED in a global health setting: 1) the Lean process aided in building a partnership with Ghanaian colleagues; 2) obtaining and maintaining senior institutional support is necessary and challenging; 3) addressing power differences among the team to obtain feedback from all team members is critical to successful Lean analysis; 4) choosing a manageable initial project is critical to influence long-term Lean use in a new environment; 5) data intensive Lean tools can be adapted and are effective in a less resourced health system; 6) several Lean tools focused on team problem-solving techniques worked well in a low-resource system without modification; 7) using Lean highlighted that important changes do not require an influx of resources; and
Global Optimization of Gravity-assist Trajectory With Deep Space Maneuvers%附加深空机动的借力飞行全局优化
李小玉; 郑建华
2013-01-01
The problem of optimal design of a multi-gravity-assist space trajectory with deep space maneuvers is studied. Based on the zero-sphere-of-influence and patched conic hypothesis, the deep space trajectory is split into segments linked by deep space maneuvers and gravity assists. After introducing an auxiliary angle and B plane, the outgoing excess velocity could be expressed analytically. The deep space maneuver was computed by solving Lambert problem and trajectory propagation. The differential evolution algorithm is used to handle afore mentioned global optimization problem. Three cases to Jupiter, with gravity sequences of Venus-Earth-Earth (VEE), Venus-Earth-Mars-Earth (VEME) and Venus-Earth-Venus-Earth (VEVE) have been optimized.%借力飞行轨道设计是一个多变量强约束的非线性优化问题,初始方案通常采用不需要初值猜测的全局优化算法进行优化,但是借力点处的C3匹配原则等较强的约束条件极大影响了全局算法的收敛性能.针对这一问题,研究了附加深空机动的借力飞行模型,在借力点处引入B平面和辅助转角,推导了离开超越速度的解析表达式,通过求解Lambert问题和轨道递推得到日心转移段的深空机动脉冲.利用微分进化算法对问题进行优化,结合木星探测算例,对VEE (Venus-Earth-Earth),VEME (Venus-Eart h-Mars-Earth)和VEVE (Venus-Earth-Venus-Earth)三种深空机动借力飞行方案进行优化,给出了优化结果.
QoS全局最优化的服务选择策略%Service Selection Strategy Offering Global Optimal Qulity of Service
董元元; 倪宏; 邓浩江; 刘磊
2011-01-01
Selecting a service instance for each service node to form the execution plan of the composite service with optimal QoS under the conditions of satisfying the global QoS constraints, is the key issue of service selection. The paper firstly analysed the optimization objective, then proposed an improved genetic algorithm, which was self-Adaptive Mutation Genetic Algorithm (AMGA) to solve the combinatorial optimization problem. In the fitness function of the algorithm, the difference between aggregated QoS values of the plan and the constraints is used as a punishment. In die mutation operator, the excellent level of service instance adaptive mutation probability is used to enhance the efficiency of genetically modified and decaying exponential function is used to guarantee the convergence of the algorithm. Experimental results show that AMGA-based service selection strategy obtains better solution than the other existing Generic Algorithm based ones.%为每个服务节点选择一个服务实例,形成在满足全局QoS限制的条件下,具有综合QoS最优的执行计划是服务选择的关键问题.本文分析了优化目标,提出了基于自适应变异遗传算法(self-Adaptive Mutation Genetic Algorithm,AMGA)求解服务选择的组合优化问题.在算法中,适应度函数设计采用对执行计划聚合QoS属性值与约束条件的偏差量作为惩罚约束,变异算子设计采用对服务实例的优良程度自适应的变异概率,提高了基因改良的效率,并引入指数衰减函数保证了算法的收敛性.实验结果表明,基于AMGA算法的服务选择策略比现有基于其它遗传算法的策略能够获得更优的解.
A new approach on designing l1 optimal regulator with minimum order for SISO linear systems
Xiang LIU
2006-01-01
For a SISO linear discrete-time system with a specified input signal, a novel method to realize optimal l1 regulation control is presented. Utilizing the technique of converting a polynomial equation to its corresponding matrix equation, a linear programming problem to get an optimal l1 norm of the system output error map is developed which includes the first term and the last term of the map sequence in the objective function and the right vector of its constraint matrix equation, respectively. The adjustability for the width of the constraint matrix makes the trade-off between the order of the optimal regulator and the value of the minimum objective norm become possible, especially for achieving the optimal regulator with minimum order. By norm scaling rules for the constraint matrix equation, the optimal solution can be scaled directly or be obtained by solving a linear programming problem with l1 norm objective.
A fast global optimization algorithm for regularized migration imaging%正则化偏移成像的全局优化快速算法
李振华; 王彦飞; 杨长春
2011-01-01
目前,偏移后的地震剖面往往只是一个地质构造图像,还不能为后续的岩性分析和油气储层属性的提取提供更精确的信息.为了得到高分辨率真振幅的图像,建议采用正则化偏移成像方法.针对本问题数据规模大和正演算子矩阵稀疏的特点,提出采用一种新的算法--无记忆拟牛顿-模拟退火法对偏移算子方程进行求解.该方法综合了无记忆拟牛顿法优良的局部搜索能力以及模拟退火法的全局最优性质.用该方法得到的全局最优解不仅成像位置正确,还能提供更准确的振幅及其他属性信息.通过理论模型试算和实际资料处理,表明在正演算子准确的情况下,该方法能减弱Kirchhoff偏移的假象,得出接近真实的反射系数分布.因此,该方法在地震成像方面是有实际应用前景的.%At the present, seismic migration usually only yields an image of the positions of geological structures, and it cannot supply more accurate information for subsequent lithology analysis and attributes extraction. To get an image with high resolution and true amplitude, we suggest that regularized migration imaging should be used. Since the amount of cost for solving the problem is huge and the kernel matrix is sparse, we propose a new hybrid algorithm which is called Memoryless Quasi-Newton-Simulated Annealing Method. The algorithm not only shows as good performance in searching a local optimized solution as memoryless quasi-Newton method does, but also reaches the global optimized solution just as simulated annealing algorithm does.The global optimized solution obtained by this method not only gets the right positions, but also contains more reliable information for amplitude and other attributes. Theoretic simulations and field data applications are performed. It reveals that the proposed algorithm can attenuate the migration artifacts and provide a better frequency distribution of estimated reflectivity when a
Fujiwara, Ippei; NAKAJIMA Tomoyuki; Sudo, Nao; Teranishi, Yuki
2011-01-01
In this paper we consider a two-country New Open Economy Macroeconomics model, and analyze the optimal monetary policy when countries cooperate in the face of a "global liquidity trap" -- i.e., a situation where the two countries are simultaneously caught in liquidity traps. The notable features of the optimal policy in the face of a global liquidity trap are history dependence and international dependence. The optimality of history dependent policy is confirmed as in local liquidity trap. A ...
蒋凤
2014-01-01
By employing Lagrangian function and L - subdifferential approach,the global optimality condi-tions for special non-convex polynomial programming problems with quadratic constraints were studied,and its optimality sufficient condition were obtained, and some conclusions in the existing literature were promoted. Finally,how to use the global optimality sufficient conditions were illustrated in this article to de-termine the current feasible solution is global optimal solution.%利用一些学者提出的研究全局优化问题的全局最优性条件的新方法，讨论一些带有二次约束的特殊非凸多项式规划问题的全局最优性条件。通过利用拉格朗日函数和L-次微分相结合的方法，给出带不等式约束的多项式规划的全局最优性充分条件，并推广了已有文献中的一些结论；最后举例说明如何利用所给出的全局最优性充分条件来判定当前可行解就是全局最优解。
Globalization, Inequality, Say’s Law, and Fiscal Globalism
Gerasimos T. Soldatos
2017-01-01
.... The mentality under which this tax policy paradigm is put forward is that the competition of nation states in a fiscal globalism fashion crystallizes the optimal level of centralization under globalism...
New Hoopoe Heuristic Optimization
El-Dosuky, Mohammed; EL-Bassiouny, Ahmed; Hamza, Taher; Rashad, Magdy
2012-01-01
Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it against some test functions. Investigations show that it is very promising and could be seen as an optimi...
Optimal Output Regulation for Heterogeneous Multiagent Systems via Adaptive Dynamic Programming.
Zhang, Huaguang; Liang, Hongjing; Wang, Zhanshan; Feng, Tao
2017-01-01
In this paper, the optimal output regulation problem for partially model-free heterogeneous linear multiagent systems with disturbance generated by an exosystem is addressed by using adaptive dynamic programming and double compensator method. The topology graph for the information exchange of the agents has a spanning tree. The dynamic of individual agent is assumed to be nonidentical and of different dimensions. One distributed compensator is designed to deal with the nonidentical agents, and the other compensator is used to handle the optimal performance index. By constructing the double compensator, the distributed feedback control laws are designed to make the output of each agent synchronize with the reference output and minimize the energy of the output error simultaneously. To overcome the lack of the dynamics knowledge of each agent, a novel online policy iteration algorithm is developed to obtain the optimal feedback gain matrix. Finally, two examples are presented to illustrate the effectiveness of our results.
Yaping Hu
2015-01-01
the nonsmooth convex optimization problem. First, by using Moreau-Yosida regularization, we convert the original objective function to a continuously differentiable function; then we use approximate function and gradient values of the Moreau-Yosida regularization to substitute the corresponding exact values in the algorithm. The global convergence is proved under suitable assumptions. Numerical experiments are presented to show the effectiveness of this algorithm.
张国平; 王正欧; 袁国林
2001-01-01
混沌神经网络具有全局搜索能力，但其运用至今主要局限于组合优化．通过对普通Hopfield优化网络引入混沌噪声退火过程，提出了一种用于约束非线性全局优化的混沌退火神经网络，它易于实现，原理简明，应用广泛．对很复杂的测试函数的数字试验表明，该模型能够高效、可靠地搜索到全局最优，其性能超过遗传算法GAMAS．%Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield network,this paper proposes a new chaotic annealing neural network (CANN) for global optimization of continuous constrained non-linear programming.It is easy to implement,conceptually simple,and generally applicable.Numerical experiments on severe test functions manifest that CANN is efficient and reliable to search for global optimum and outperforms the existing genetic algorithm GAMAS for the same purpose.
National Oceanic and Atmospheric Administration, Department of Commerce — This accession contains the daily 25km global Optimally Interpolated Sea Surface Temperature (OISST) in situ and AVHRR analysis, supplemented with AVHRR Pathfinder...
周雪刚
2015-01-01
In this paper, suﬃcient global optimality conditions are presented for nonconvex quadratic programming problems with quadratic constraints as well as hyperrectangle constr-aints. The new conditions are obtained by making use of quadratic underestimators of quadratic function. We first introduce how to construct quadratic underestimators of quadratic function. Then, by using convex quadratic underestimators of the Lagrangian function at the Karush-Kuhn-Tucker point, we establish suﬃcient global optimality conditions for nonconvex quadratic programming problems. And we propose suﬃcient global optimality conditions by utilizing the minimum eigenvalue and quadratic underestimators. Finally, by using quadratic underestima-tors, we establish the suﬃcient condition for nonconvex quadratic programming problems with quadratic constraints.%本文讨论具有二次约束与超矩形约束的非凸二次规划问题的新型全局最优性充分条件，这些新的全局最优性充分条件是利用二次函数的二次下估计函数获得的。我们首先介绍如何构造二次函数的下估计函数。然后利用在KKT点处的拉格朗日函数的凸二次下估计函数建立非凸二次规划问题的全局最优性充分条件，再利用最小特征根与二次下估计函数获得它的全局最优性充分条件。最后利用二次下估计函数建立具有二次约束的非凸二次规划问题的全局最优性充分条件。
Identification of multiple inputs single output errors-in-variables system using cumulant
Haihui Long; Jiankang Zhao
2014-01-01
A higher-order cumulant-based weighted least square (HOCWLS) and a higher-order cumulant-based iterative least square (HOCILS) are derived for multiple inputs single output (MISO) errors-in-variables (EIV) systems from noisy input/output data. Whether the noises of the input/output of the system are white or colored, the proposed algorithms can be insensitive to these noises and yield unbiased estimates. To realize adaptive pa-rameter estimates, a higher-order cumulant-based recursive least square (HOCRLS) method is also studied. Convergence analy-sis of the HOCRLS is conducted by using the stochastic process theory and the stochastic martingale theory. It indicates that the parameter estimation error of HOCRLS consistently converges to zero under a generalized persistent excitation condition. The use-fulness of the proposed algorithms is assessed through numerical simulations.
康国胜; 刘建勋; 唐明董; 徐宇
2011-01-01
QoS全局最优动态Web服务选择是服务组合中的一个难题.基于差异演化算法,设计一种用于解决该问题的DE-GODSS算法.算法的主要思想是将问题表示为一个带QoS约束的多目标服务组合优化问题,通过理想点的方法将多目标向单目标转化,然后利用差异演化算法的智能优化原理进行算法设计及求解,最终产生一组满足约束条件的优化服务组合流程集.理论分析证明DE-GODSS算法的时间复杂度优于已有的多目标遗传算法,且实验结果表明该算法的收敛速度优于已有的多目标遗传算法.%Dynamic Web service selection with global QoS optimization is a critical issue in Web service composition. In order to solve the problem; based on the algorithm of differential evolution (DE); this paper proposes the DE-GODSS (global optimal of dynamic Web service selection based on DE) algorithm. The basic idea of the algorithm is to transform the original Web service selection problem into a multi-objective service composition optimization with global QoS constraints; which is further transformed into a single-object by using the method of ideal point. Then; the theory of intelligent optimization of DE is exploited to produce a set of optimal services composition process with QoS constraints. Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm; and the time complexity and convergence rate of our algorithm are much better than that of the multi-objective genetic algorithm used in prior work.
Melby, Melissa K; Loh, Lawrence C; Evert, Jessica; Prater, Christopher; Lin, Henry; Khan, Omar A
2016-05-01
Increasing demand for global health education in medical training has driven the growth of educational programs predicated on a model of short-term medical service abroad. Almost two-thirds of matriculating medical students expect to participate in a global health experience during medical school, continuing into residency and early careers. Despite positive intent, such short-term experiences in global health (STEGHs) may exacerbate global health inequities and even cause harm. Growing out of the "medical missions" tradition, contemporary participation continues to evolve. Ethical concerns and other disciplinary approaches, such as public health and anthropology, can be incorpo rated to increase effectiveness and sustainability, and to shift the culture of STEGHs from focusing on trainees and their home institutions to also considering benefits in host communities and nurtur ing partnerships. The authors propose four core principles to guide ethical development of educational STEGHs: (1) skills building in cross-cultural effective ness and cultural humility, (2) bidirectional participatory relationships, (3) local capacity building, and (4) long-term sustainability. Application of these principles highlights the need for assessment of STEGHs: data collection that allows transparent compar isons, standards of quality, bidirectionality of agreements, defined curricula, and ethics that meet both host and sending countries' standards and needs. To capture the enormous potential of STEGHs, a paradigm shift in the culture of STEGHs is needed to ensure that these experiences balance training level, personal competencies, medical and cross-cultural ethics, and educational objectives to minimize harm and maximize benefits for all involved.
Wei, Xile; Lu, Meili; Wang, Jiang; Tsang, K. M.; Deng, Bin; Che, Yanqiu
2010-05-01
We consider the assumption of existence of the general nonlinear internal model that is introduced in the design of robust output regulators for a class of minimum-phase nonlinear systems with rth degree (r ≥ 2). The robust output regulation problem can be converted into a robust stabilisation problem of an augmented system consisting of the given plant and a high-gain nonlinear internal model, perfectly reproducing the bounded including not only periodic but also nonperiodic exogenous signal from a nonlinear system, which satisfies some general immersion assumption. The state feedback controller is designed to guarantee the asymptotic convergence of system errors to zero manifold. Furthermore, the proposed scheme makes use of output feedback dynamic controller that only processes information from the regulated output error by using high-gain observer to robustly estimate the derivatives of the regulated output error. The stabilisation analysis of the resulting closed-loop systems leads to regional as well as semi-global robust output regulation achieved for some appointed initial condition in the state space, for all possible values of the uncertain parameter vector and the exogenous signal, ranging over an arbitrary compact set.
Gorissen, B.L.; Ben-Tal, A.; Blanc, J.P.C.; den Hertog, D.
2012-01-01
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimization problem by using the theory of Beck and Ben-Tal [2] on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First, we obtain a new convex reformulation of the
Optimally segmented permanent magnet structures
Insinga, Andrea Roberto; Bjørk, Rasmus; Smith, Anders
2016-01-01
We present an optimization approach which can be employed to calculate the globally optimal segmentation of a two-dimensional magnetic system into uniformly magnetized pieces. For each segment the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector...
赵峰; 张承慧; 孙波; 魏大钧
2015-01-01
冷热电联供系统能否高效、经济、环境友好的运行，取决于系统的设备选型、设备容量及运行策略的整体优化。文中设计了一种冷热电联供系统的三级协同整体优化方法，第一级优化运用离散粒子群算法，以年一次能源利用率最高为目标，求解最优设备选型问题；第二级优化采用粒子群算法，以年CO2排放量最少为目标，求解最优设备容量问题；第三级优化采用粒子群算法，以年运行成本最低为目标，求解最优运行参数问题。以应用于医院的冷热电联供系统为例，验证该三级协同整体优化设计方法的有效性。结果表明，采用该方法设计的冷热电联供系统同分别基于“以电定热”和“以热定电”运行策略设计的2种冷热电联供系统相比，该系统更节能、更环保、更经济。%ABSTRACT:Combined cooling heating and power (CCHP) system can be efficient, economical and environmentally friendly, depending on the global optimization design of equipment selection, equipment capacity and operation strategy of CCHP system. This paper proposed a three-stage collaborative global optimization design method for CCHP system. On the first stage,discrete particle swarm optimization was applied to solve the optimal equipment type problem with maximum annual primary energy utilization rate. On the second stage, particle swarm optimization was utilized to solve the optimal equipment capacity with minimum annual carbon dioxide emissions. On the third stage, particle swarm optimization was utilized to solve the optimal operation strategy with minimum annual operation costs. A case of CCHP system used in a hospital building validated the effectiveness of this optimal method. Results show that this CCHP system designed by this optimal method is more energy saving, more environmental and more profitable than two CCHP systems separately designed by following the electric load (FEL) and
Souza, Adilson P.; Escobedo, Joao F. [Universidade Estadual Paulista (UNESP), Botucatu, SP (Brazil)], E-mail: pachecopgid@yahoo.com.br
2010-07-01
This study evaluated the monthly and annual total radiation global, direct and diffuse on horizontal surfaces and tilted surfaces to 12.85 deg (|L|-10 deg), 22.85 deg (|L|) and 32.85 deg (|L|+10 deg), with the north face, in Botucatu, SP. The measures occurred in the following dates: 04/1998 to 07/2001 at 22.85 deg; 08/2001 to 02/2003 at 12.85 deg, and 03/2003 to 12/2007 in 32.85. In all periods occurred concurrent measures in the horizontal plane (reference). The total annual global radiation equal to 6500.87; 7044.21; 7193.24 and 6854.99 MJ m{sup -2}, for horizontal surfaces, 12.85 deg, 22.85 deg e 32.85 deg. The change of the angles of inclination throughout the year enabled gains of 324.92 MJ m{sup -2} (4.74%) in global radiation in relation to 22,85 deg, distributed as follows: I) horizontal: December, January and February; II) of 12.85: March and October; III) of 22.85: April, May, September and November, IV) of 32.85: June-August. In 22.85 were recorded the annual radiation directly (4367.40 MJ m{sup -2}), exceeding 12.85 deg, 32.85 deg and horizontal, 72.40, 284.67 and 718.03 MJ m{sup -2}, however, were achieved gains 16.82% compared to 22.85 deg. For diffuse radiation, annual earnings totaled 226.57 MJ m{sup -2} (compared with 22.85 deg), with differences of less than 103.00 MJ m{sup -2} between 12.85 deg, 22.85 deg and 32.85 deg. (author)
花新乐; 张骞; 付文杰; 张旭东; 王石; 王清亮; 栗军
2012-01-01
针对电力电缆优化分割下料问题，提出了全局启发一两层求解算法。算法定义了电缆的全局启发价值并先进行计算，然后基于剪枝回溯算法递归求解O-N背包问题，从而获得既有较高局部价值又有较高全局价值的分割方式，最后通过多级贪心策略逐步启发得到问题的全局优化分割方案。基于该算法研制的电缆优化分割计算平台，求解结果既可精确满足电缆需求，又达到余料高度集中的效果，不需调整就可直接应用。实践应用和算例比较表明，该算法计算速度快，求解质量高，通常情况下能够获得理想最优解。%A global heuristic two-tier algorithm to the problem of power cable optimal cutting is solve proposed. Firstly the global heuristic values of power cables are defin by this algorithm and calculates them; then based on pruned backtracking algorithm the O-N knapsack problem recursively solved to attain a power cable cutting mode with both higher local and global values; finally, through step-by-step elicitation of multipole greedy strategy the global solution of the problem is achieved. Based on the proposed algorithm a calculation platform for power cable optimal cutting is developed and the solutions of this platform can meet the requirement of cable cutting accurately and original scattered short oddments of power cables after traditional cutting can be highly centralized to a single cable as a whole, besides, needing not adjustment, the developed calculation platform can be directly applied. Both results from actual application and calculation examples show that the proposed algorithm possesses the advantages of high computing speed, satisfied solution quality, and under normal circumstances ideal optimal solution can be obtained, thus the proposed algorithm is significant to the saving of the usage amount of power cable and the reduction of the cost of power construction. The proposed algorithm is
易盟; 郭宝龙; 严春满
2012-01-01
A global aerial-video registration algorithm using the optimal derivative filters is proposed.Firstly,the Hession detector based on optimal derivative filters is presented to determine the location of feature points.To ensure that the same feature points are detected in images with different focus from camera,we choose feature points that constant across the three resolutions.Then the minimum spanning tree（MST） is used to find initial matching.Once matching feature points have been found,the transformation parameters which have global minimum error are then estimated using non-linear least squares（NLLS）and random sample consensus（RANSAC）method.Finally the image registration is finished.Experimental results show that by accurately registering frames at the background using optimal derivative filter and globally-optimal transformation model,the technique is robust under different dynamic scenes and illuminations.%提出一种基于优化梯度滤波的全局航拍视频配准算法。该方法首先提出一种基于优化梯度滤波的Hession检测器,以实现特征点的精确定位,同时,为了保证在不同摄像机焦距下获得相同的特征点,保留3个分辨率下具有恒定不变的特征点。然后利用最小生成树方法对待配准点进行初始匹配。一致特征点建立后,通过利用非线性最小二乘（NLLS）和随机采样一致性（RANSAC）算法选取具有全局最小误差的变换参数对视频帧间实现配准。实验结果表明,通过利用优化梯度滤波和全局最优模型估计可实现帧间的精确配准,对不同动态场景和光照变换具有较强的适应性。
S. R. Singh
2013-01-01
Full Text Available An inventory system for deteriorating items, with ramp-type demand rate, under two-level trade credit policy taking account of preservation technology is considered. The objective of this study is to develop a deteriorating inventory policy when the supplier provides to the retailer a permissible delay in payments, and during this credit period, the retailer accumulates the revenue and earns interest on that revenue; also the retailer invests on the preservation technology to reduce the rate of product deterioration. Shortages are allowed and partially backlogged. Sufficient conditions of the existence and uniqueness of the optimal replenishment policy are provided, and an algorithm, for its determination, is proposed. Numerical examples draw attention to the obtained results, and the sensitivity analysis of the optimal solution with respect to leading parameters of the system is carried out.
Global Optimization for Generalized Geometric Programming with Free Variables%带自由变量的广义几何规划问题的全局优化
申培萍; 靳利
2012-01-01
对带自由变量的广义几何规划问题(FGGP)给出一全局优化算法.该算法先利用等价转换把(FGGP)中的自由变量转化为正变量,再通过凸化方案建立了(FGGP)的松弛凸规划(RCP).通过对(RCP)可行域的细分以及一系列(RCP)的求解过程,提出的算法收敛到(FGGP)的全局最优解,且数值例子表明了算法的可行性.%A global optimization algorithm is proposed for locating global minimum of generalized geometric programming (FGGP) with free variables. By utilizing equivalent transformation,free variables in (FGGP) are first transformed into positive variables,by con-vexication strategies the relaxation convex programming (RCP) about (FGGP) is then established. The proposed branch and bound algorithm is convergent to the global minimum of (FGGP) through the successive renement of the feasible region of (RCP) and the solutions of a series of (RCP). And finally the numerical examples are given to illustrate the feasibility of the present algorithm.
贾宏新; 叶云岳; 林瑞光
2001-01-01
This paper introduces a new algorthm for global optimization,theDomain Elimination Algorithm (DEA),into the optimum design of motor.The MULT-SUMT method is combined with improved POWELL method to conduct local optimization procedure.The optimization project is used in the design of linear induction motor.The result is satisfied and can be available in a short time.%把一种新型全局优化算法—区域消去法引进到电机的优化设计中，并针对电机优化问题的具体特点，提出将混合罚函数法和修正的鲍威尔法用于其中的局部寻优。应用该算法对直线电机进行优化设计，较大提高了直线电机的力能指标，且具有收敛速度快的特点。
Introduction to Continuous Optimization
Andreasson, Niclas; Evgrafov, Anton; Patriksson, Michael
optimal solutions for continuous optimization models. The main part of the mathematical material therefore concerns the analysis and linear algebra that underlie the workings of convexity and duality, and necessary/sufficient local/global optimality conditions for continuous optimization problems. Natural...... algorithms are then developed from these optimality conditions, and their most important convergence characteristics are analyzed. The book answers many more questions of the form “Why?” and “Why not?” than “How?”. We use only elementary mathematics in the development of the book, yet are rigorous throughout...
Ibinson, James W; Vogt, Keith M; Taylor, Kevin B; Dua, Shiv B; Becker, Christopher J; Loggia, Marco; Wasan, Ajay D
2015-12-01
The insula is uniquely located between the temporal and parietal cortices, making it anatomically well-positioned to act as an integrating center between the sensory and affective domains for the processing of painful stimulation. This can be studied through resting-state functional connectivity (fcMRI) imaging; however, the lack of a clear methodology for the analysis of fcMRI complicates the interpretation of these data during acute pain. Detected connectivity changes may reflect actual alterations in low-frequency synchronous neuronal activity related to pain, may be due to changes in global cerebral blood flow or the superimposed task-induced neuronal activity. The primary goal of this study was to investigate the effects of global signal regression (GSR) and task paradigm regression (TPR) on the changes in functional connectivity of the left (contralateral) insula in healthy subjects at rest and during acute painful electric nerve stimulation of the right hand. The use of GSR reduced the size and statistical significance of connectivity clusters and created negative correlation coefficients for some connectivity clusters. TPR with cyclic stimulation gave task versus rest connectivity differences similar to those with a constant task, suggesting that analysis which includes TPR is more accurately reflective of low-frequency neuronal activity. Both GSR and TPR have been inconsistently applied to fcMRI analysis. Based on these results, investigators need to consider the impact GSR and TPR have on connectivity during task performance when attempting to synthesize the literature.
顾纪超; 周钰亮; 李光耀; 董佐民; 干年妃
2011-01-01
Plug-in hybrid vehicle model was built to improve the energy conversion efficiency and several global optimization methods were used for the design optimization of the vehicle model in-the -loop. The electrical/mechanical energy conversion efficiency was maximized in a finite number of operating states by optimization. The optimization results were converted into a look-up table and implemented using the vehicle model. The results gained by the simulation show noticeable improvements of the energy efficiency. By comparison, HAM method strikes a good balance between search accuracy and efficiency, which gains similar results using far less computation time.%为提高混合动力车的能量转换效率,开发了可充电式混合动力车的模型,应用多种全局最优化方法对回路中的汽车模型进行了优化,在有限操作状态下,使动力/机械能量转化效率达到最高.将优化结果输入控制模型中并进行仿真运算,结果表明,采用该模型可使能量效率得到显著提高.经过比较,基于混合元模型的自适应全局最优化(HAM)方法能兼顾精度和效率,用远远少于其他方法的计算时间得到了相似精度的结果.
Topics in Nonconvex Optimization
Mishra, Shashi Kant
2011-01-01
Nonconvex Optimization is a multi-disciplinary research field that deals with the characterization and computation of local/global minima/maxima of nonlinear, nonconvex, nonsmooth, discrete and continuous functions. Nonconvex optimization problems are frequently encountered in modeling real world systems for a very broad range of applications including engineering, mathematical economics, management science, financial engineering, and social science. This contributed volume consists of selected contributions from the Advanced Training Programme on Nonconvex Optimization and Its Applications he
F. C. Sperna Weiland
2012-03-01
Full Text Available Potential evaporation (PET is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1 evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2 tested on their usability for modeling of global discharge cycles.
A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts, the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e
Jedidi, Abdesslem
2015-11-13
Vibrational fingerprints of small PtnP2n (n = 1–5) clusters were computed from their low-lying structures located from a global exploration of their DFT potential energy surfaces with the GSAM code. Five DFT methods were assessed from the CCSD(T) wavenumbers of PtP2 species and CCSD relative energies of Pt2P4 structures. The eight first PtnP2n isomers found are reported. The vibrational computations reveal (i) the absence of clear signatures made by overtone or combination bands due to very weak mechanical and electrical anharmonicities and (ii) some significant and recurrent vibrational fingerprints in correlation with the different PP bonding situations in the PtnP2n structures.
田朝薇; 宋海洲
2011-01-01
针对非凸二次约束二次规划(QCQP)问题,将问题中二次函数的凸函数部分保留,达到所得松弛规划的可行域更加紧致的目的,得到原问题更好的下界.利用正交变换的方法得到原问题的一个凸规划松弛模型,再利用分支定界算法求其全局最优解.根据问题的最优性和可行性原则,提出一种能整体删除或缩小算法迭代过程中产生的分割子区域的区域删减策略.数值算例表明,算法及区域删减策略均是有效的.%In this paper, we obtain a sharper low bound by reserving the part of the convex function of the quadratic function for a non-convex quadratic programming with non-convex quadratic constraints (QCQP). The QCQP problem is first transformed into a convex quadratic programming with linear constraints by employing the orthogonal transformation and then the latter is solved by the branch-bound method. In order to improve the convergence of the proposed algorithm, two region-prunning techniques are given to delete or contract the sub-regions in which does not contain the optimal solutions of QCQP according to the optimality and feasibility of the problem. The numerical results show that the proposed algorithm and the prunning techniques are effective.
In-Flight Pitot-Static Calibration
Foster, John V. (Inventor); Cunningham, Kevin (Inventor)
2016-01-01
A GPS-based pitot-static calibration system uses global output-error optimization. High data rate measurements of static and total pressure, ambient air conditions, and GPS-based ground speed measurements are used to compute pitot-static pressure errors over a range of airspeed. System identification methods rapidly compute optimal pressure error models with defined confidence intervals.
J. C. P. Hemmings
2015-03-01
Full Text Available Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to capture the dominant biogeochemical dynamics of a complex biological system. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established. The feasibility of establishing such a relationship is investigated for an intermediate complexity biogeochemistry model (MEDUSA coupled with a widely used global ocean model (NEMO. A site-based mechanistic emulator is constructed for surface chlorophyll output from this target model as a function of model parameters. The emulator comprises an array of 1-D simulators and a statistical quantification of the uncertainty in their predictions. The unknown parameter-dependent biogeochemical environment, in terms of initial tracer concentrations and lateral flux information required by the simulators, is a significant source of uncertainty. It is approximated by a mean environment derived from a small ensemble of 3-D simulations representing variability of the target model behaviour over the parameter space of interest. The performance of two alternative uncertainty quantification schemes is examined: a direct method based on comparisons between simulator output and a sample of known target model "truths" and an indirect method that is only partially reliant on knowledge of the target model output. In general, chlorophyll
Jian Zhao
2017-01-01
Full Text Available Partial shading (PS is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV system. With PS, the system usually exhibits multiple-peak output power characteristics, but single-peak is also possible under special PS conditions. In fact it is shown that the partial shading condition (PSC is the necessary but not sufficient condition for multiple-peak. Based on circuit analysis, this paper shows that the number of peak points can be determined by short-circuit currents and maximum-power point currents of all the arrays in series. Then the principle is established based on which the number of the peak points is to be determined. Furthermore, based on the dynamic characteristic of solar array, this paper establishes the rule for determination of the relative position of the global maximum power point (GMPP. In order to track the GMPP within an appropriate period, a reliable technique and the corresponding computer algorithm are developed for GMPP tracking (GMPPT control. It exploits a definable nonlinear relation has been found between variable environmental parameters and the output current of solar arrays at every maximum power point, obtained based on the dynamic performance corresponding to PSC. Finally, the proposed method is validated with MATLAB®/Simulink® simulations and actual experiments. It is shown that the GMPPT of a PV generation system is indeed realized efficiently in a realistic environment with partial shading conditions.
Akimoto, K.; Matsunaga, A.; Fujii, Y. [Yokohama National University, Yokohama (Japan); Yamaji, K. [The University of Tokyo, Tokyo (Japan)
1998-10-01
Carbon emissions which would cause global warming were agreed to be constrained at COP3 in Kyoto. In addition, carton emission permits trading was also approved to be introduced. The emission permits trading is expected to achieve efficient carbon emission reduction, equalizing the marginal costs of the emission reduction for the participating countries. In other words, the permits trading allows participants to reduce emissions where it is least expensive to do so. However, the inadequate introduction of the trading systems may impose unfairly greater burden on some countries, and therefore careful evaluation of the system would be indispensable for its implementation. In this paper, we attempt to analyze the emission permits trading. using the theory of cooperative games with a global energy model of optimization type. We assumed that seven world regions as players participate the permits trading system under the condition of the emission reduction target presented at COP3 and so on, and show the nucleolus of the grand coalition games, and the computational results of primary energy supplies and CO2 shadow prices. The insights of this research indicate that in order to stabilize the grand coalition, a noticeable amount of additional transfer of money would be needed besides the payments associated with the emission permits trading. 10 refs., 7 figs., 5 tabs.
Optimal Control with Complete Load-Decoupled Design for the Buck DC-DC Switching Converter
Fei-Hu; Hsieh; Yau-Tarng; Juang
2002-01-01
This paper describes a new controller design procedure and turning method for the buck PWM DCDCswitching converter taking into consideration the equivalent series resistance of components. First, the linearoptimal feedback is designed by the LQR approach. Then the designed control law is implemented by aphase-lag lead controller incorporated with a complete load-decoupled PD compensator. The phase-lag lead controlleris tuned to achieve the optimal design based on the output error voltage directly, instead of using an estimator.With the proposed PD compensator, the converter is robust with respect to the load changes and parameterperturbations. We also provide the conditions for the robust stability assurance of the closed-loop system.
k Globally Different Optimal Sequenced Route Query Based on Obstacles%基于障碍物群的k全局相异最优有序路径查询
孙冬璞; 郝忠孝
2011-01-01
The problem of k Obstructed Globally Different Optimal Sequenced Route(kOGDOSR) query is proposed. The approximate algorithm for resolving this query problem is put forward based on visibility graph. The introduction of effect set and obstructed angle point reduces the number of obstacles used in the configuration of visibility graph. It decreases the number of inner obstructed route calculations through analyzing the relationship between the line segment from query point to data point and the vertices and arcs of the visibility graph. The algorithm is optimized completely through the two aspects described above. Experimental results indicate that the algorithm presented has a better performance.%提出障碍k全局相异最优有序路径的查询问题,利用可视图的思想给出近似查询算法,通过作用集与障碍角度点的引入有效地减少构造可视图障碍对象的数量,分析查询点和数据点构成的线段与可视图的顶点和弧的关系,减少内部障碍路径的计算次数,实现算法的全面优化.实验结果表明,该算法具有较好的性能.
杜金玲; 刘大莲; 李奇会
2012-01-01
对于运筹学问题学中的函数优化问题,本文提出一种嵌入思维进化的新的进化算法,将思维进化计算(Mind Evolutionary Computation,MEC)的“趋同”和“异化”操作加入到进化算法中,充分利用其特有记忆机制、定向机制和探测与开采功能之间的协调机制的好性能,并加入K-meams聚类算法,保证群体多样性.最后,数值模拟验证了新算法的有效性.%A new evolutionary algorithm for global optimization embe dded in the mind evolutionary computation for optimal problem in operational reserch is offered in this paper. Operations of similartaxis and dissimilation of mind evolutionary computation join in with the EC to make the best of the good quality of the evolutionary directionality mechanism, memory mechanism and harmony mechanism between exploitation and exploration. Also, K-meams clustering algorithm is used to ensure the diversity of the population. At last, the numerical results also show that the new approach is efficient.
Cavarec, P.E.
2002-11-15
The aim of this thesis is the study and the conception of splitted structures of global coil synchronous machines for the maximization of specific torque or thrust. This concept of machine, called multi-air gap, is more precisely applied to the elaboration of a new linear multi-rods actuator. It is clearly connected to the context of direct drive solutions. First, a classification of different electromagnetic actuator families gives the particular place of multi-air gaps actuators. Then, a study, based on geometrical parameters optimizations, underlines the interest of that kind of topology for reaching very high specific forces and mechanical dynamics. A similitude law, governing those actuators, is then extracted. A study of mechanical behaviour, taking into account mechanic (tolerance) and normal forces (guidance), is carried out. Hence, methods for filtering the ripple force, and decreasing the parasitic forces without affecting the useful force are presented. This approach drives to the multi-rods structures. A prototype is then tested and validates the feasibility of that kind of devices, and the accuracy of the magnetic models. This motor, having only eight rods for an active volume of one litre, reaches an electromagnetic force of 1000 N in static conditions. A method for estimate optimal performances of multi-rods actuators under several mechanical stresses is presented. (author)
Rapallo, Arnaldo; Rossi, Giulia; Ferrando, Riccardo; Fortunelli, Alessandro; Curley, Benjamin C.; Lloyd, Lesley D.; Tarbuck, Gary M.; Johnston, Roy L.
2005-05-01
A genetic algorithm approach is applied to the optimization of the potential energy of a wide range of binary metallic nanoclusters, Ag-Cu, Ag-Ni, Au-Cu, Ag-Pd, Ag-Au, and Pd-Pt, modeled by a semiempirical potential. The aim of this work is to single out the driving forces that make different structural motifs the most favorable at different sizes and chemical compositions. Paper I is devoted to the analysis of size-mismatched systems, namely, Ag-Cu, Ag-Ni, and Au-Cu clusters. In Ag-Cu and Ag-Ni clusters, the large size mismatch and the tendency of Ag to segregate at the surface of Cu and Ni lead to the location of core-shell polyicosahedral minimum structures. Particularly stable polyicosahedral clusters are located at size N =34 (at the composition with 27 Ag atoms) and N =38 (at the composition with 32 and 30 Ag atoms). In Ag-Ni clusters, Ag32Ni13 is also shown to be a good energetic configuration. For Au-Cu clusters, these core-shell polyicosahedra are less common, because size mismatch is not reinforced by a strong tendency to segregation of Au at the surface of Cu, and Au atoms are not well accommodated upon the strained polyicosahedral surface.
Optimally Stopped Optimization
Vinci, Walter; Lidar, Daniel A.
2016-11-01
We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark simulated annealing on a class of maximum-2-satisfiability (MAX2SAT) problems. We also compare the performance of a D-Wave 2X quantum annealer to the Hamze-Freitas-Selby (HFS) solver, a specialized classical heuristic algorithm designed for low-tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N =1098 variables, the D-Wave device is 2 orders of magnitude faster than the HFS solver, and, modulo known caveats related to suboptimal annealing times, exhibits identical scaling with problem size.
Optimization and Optimal Control
Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider
2010-01-01
During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou
Neuroanatomy and Global Neuroscience.
DeFelipe, Javier
2017-07-05
Our brains are like a dense forest-a complex, seemingly impenetrable terrain of interacting cells mediating cognition and behavior. However, we should view the challenge of understanding the brain with optimism, provided that we choose appropriate strategies for the development of global neuroscience. Copyright © 2017 Elsevier Inc. All rights reserved.
Ethier, Wilfred J.
2002-01-01
This paper addresses a complex of globalization issues: the effect of globalization on the skill premium; the effect of globalization on unemployment; the relative importance of globalization and exogenous technical change; the effect of globalization on the ability of national governments to conduct independent social policies. Thinking about these topics has been dominated by a large empirical literature concluding that trade has played a relatively minor role in the rise of the skill premi...
Philipsen, Lotte; Baggesgaard, Mads Anders
2013-01-01
In order to understand globalization, we need to consider what globalization is not. That is, in order to understand the mechanisms and elements that work toward globalization, we must, in a sense, read against globalization, highlighting the limitations of the concept and its inherent conflicts....... Only by employing this as a critical practice will we be analytically able to gain a dynamic understanding of the forces of globalization as they unfold today and as they have developed historically....
Mechanical Design Optimization Using Advanced Optimization Techniques
Rao, R Venkata
2012-01-01
Mechanical design includes an optimization process in which designers always consider objectives such as strength, deflection, weight, wear, corrosion, etc. depending on the requirements. However, design optimization for a complete mechanical assembly leads to a complicated objective function with a large number of design variables. It is a good practice to apply optimization techniques for individual components or intermediate assemblies than a complete assembly. Analytical or numerical methods for calculating the extreme values of a function may perform well in many practical cases, but may fail in more complex design situations. In real design problems, the number of design parameters can be very large and their influence on the value to be optimized (the goal function) can be very complicated, having nonlinear character. In these complex cases, advanced optimization algorithms offer solutions to the problems, because they find a solution near to the global optimum within reasonable time and computational ...
Benchmarking optimization solvers for structural topology optimization
Rojas Labanda, Susana; Stolpe, Mathias
2015-01-01
The purpose of this article is to benchmark different optimization solvers when applied to various finite element based structural topology optimization problems. An extensive and representative library of minimum compliance, minimum volume, and mechanism design problem instances for different...... sizes is developed for this benchmarking. The problems are based on a material interpolation scheme combined with a density filter. Different optimization solvers including Optimality Criteria (OC), the Method of Moving Asymptotes (MMA) and its globally convergent version GCMMA, the interior point...... profiles conclude that general solvers are as efficient and reliable as classical structural topology optimization solvers. Moreover, the use of the exact Hessians in SAND formulations, generally produce designs with better objective function values. However, with the benchmarked implementations solving...
Li, Peter Ping
2013-01-01
Global strategy differs from domestic strategy in terms of content and process as well as context and structure. The content of global strategy can contain five key elements, while the process of global strategy can have six major stages. These are expounded below. Global strategy is influenced...... by rich and complementary local contexts with diverse resource pools and game rules at the national level to form a broad ecosystem at the global level. Further, global strategy dictates the interaction or balance between different entry strategies at the levels of internal and external networks....
彭松; 贾阳
2012-01-01
In the tele-operation system of lunar rover,the path planning contains three levels： mission-level path planning,global path planning and local path planning.Based on the requirements of the global path planning of the lunar rover,the Particle Swarm Optimization（PSO） algorithm is introduced in the global navigation point planning.Since the PSO algorithm may converge ill or not converge in path planning, the algorithm is modified.In the modified algorithm,the velocity inertial weight is deleted,but the cognitive and social coefficients are kept,with the aim at making the algorithm converge quickly in path planning.Also the variation coefficient in evolution algorithm is imported to enhance the global optimization ability.Simulation results show the improved algorithm is simple and has high ability to find the best path.Also simulation tests are done in several different simulated lunar terrain maps,and optimization methods are given to make the planning result better.%在月面巡视器遥操作系统中,路径规划分为任务级路径规划、全局路径规划和局部路径规划。根据巡视器全局路径规划的应用要求,引入粒子群优化算法应用于全局导航点的规划。针对粒子群算法在路径规划中容易造成不收敛或病态收敛的问题,对算法进行了修改,去掉了速度更新中的速度惯性因子,只保留自身认识因子和社会认识因子,使其在全局路径规划中能够快速收敛;同时引入经典遗传算法中的变异因子以增强算法的全局优化能力。仿真结果表明该算法具有计算简单、全局寻优能力强等特点,能够快速地找到优化的全局导航点。同时在不同的模拟月面地形上进行仿真试验,针对存在的问题提出了对应的二次优化方法,结果表明该方法较好地满足了巡视器全局路径规划的应用需求。
Global path planning for lunar rover based on fruit fly optimization algorithm%基于果蝇优化算法的月球车全局路径规划
毛正阳; 方群
2014-01-01
Based on the requirements of the global path planning of the lunar rover, the Fruit Fly Optimization Algorithm is introduced in the global navigation point planning. Since FOA may converge ill, the algorithm is modified. In the modified algorithm, the distance from fruit flies to the origin is directly put into the fitness function, which is not easy to fall into local optimum,improve the stability of the algorithm, and make the flies group fly to known food source. Simulation results show the improved algorithm is simple and has high ability to find the best path.%基于月球车全局路径规划的任务要求，采用果蝇优化算法应用于全局路径的规划。针对果蝇优化算法在路径规划中容易形成局部最优的问题，对算法进行了修改，将果蝇与原点的距离直接带入味道浓度判定函数，从而不易陷入局部最优，提高了算法的稳定性，并可使果蝇群体向已知食物源飞行。通过仿真表明该算法具有计算简单、全局寻优能力强等特点，能够快速地找到优化的全局路径。
Narula, Rajneesh
Technology and globalization are interdependent processes. Globalization has a fundamental influence on the creation and diffusion of technology, which, in turn, affects the interdependence of firms and locations. This volume examines the international aspect of this interdependence at two levels...
... globalhealth/what/default.htm) CDC protects Americans through global health. frame support disabled and/or not supported ... Public Health Emergencies More stories Infographic More infographics Global Health & You OUTBREAKS DISEASES & CONDITIONS TRAVEL CDC JOBS ...
基于Lingo的离散型桁架结构全局拓扑优化设计%Global Topological Optimal Design of Discrete-sizing Trusses Based on Lingo
孙浩; 刘扬; 韦芳芳
2009-01-01
利用Lingo对离散型桁架结构进行拓扑优化设计.首先,对Lingo软件包做了简介.然后,在不同的荷载工况下同时考虑应力、稳定与位移等约束条件,建立了优化数学模型.给出了结构内力重分析的方法 ,运用该重分析技术,使拓扑优化计算工作量减少.最后,为了确保模型和方法 的可行性,给出了一个具有应力和位移约束的桁架结构拓扑优化的算例,算例表明,运用Lingo解决桁架全局拓扑优化问题是一种有效的方法 .%Lingo is used to solve global topological optimal design of discrete-sizing trusses problem. A brief introduction about Lingo is first proposed. Then,a modified mathematical model is detailed regarding some constraints including stress,stability,and displacement,under multiple loading conditions. In addition, multiple analysis of a truss technology is assigned in order to reduce the calculation workload. Ultimately,in order to examine the practicability of the approach and the model,a well-studied truss structure example is used to demonstrate the feasibility and effectiveness of such method and approach, which successfully reflects that the usage of Lingo to optimize trusses is an effective method.
Vivi Triana
2008-03-01
Full Text Available Pemanasan global (global warming pada dasarnya merupakan fenomena peningkatan temperature global dari tahun ke tahun karena terjadinya efek rumah kaca (greenhouse effect yang disebabkan oleh meningkatnya emisi gas-gas seperti karbondioksida (CO2, metana (CH4, dinitrooksida (N2O dan CFC sehingga energy matahari terperangkap dalam atmosfer bumi. Berbagai literatur menunjukkan kenaikan temperatur global termasuk Indonesia yang terjadi pada kisaran 1,5 – 40 °C pada akhir abad 21.
Group Leaders Optimization Algorithm
Daskin, Anmer
2010-01-01
Complexity of global optimization algorithms makes implementation of the algorithms difficult and leads the algorithms to require more computer resources for the optimization process. The ability to explore the whole solution space without increasing the complexity of algorithms has a great importance to not only get reliable results but so also make the implementation of these algorithms more convenient for higher dimensional and complex-real world problems in science and engineering. In this paper, we present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique that is designed into a group architecture similar to the architecture of Cooperative Coevolutionary Algorithms. Therefore, we present the implementation method and the experimental results for the single and multidimensional optimization test problems and a scientific real world problem, the energies and the geometric structures of Lennard-Jones clusters.
Longstreet, Wilma S., Ed.
1988-01-01
This issue contains an introduction ("The Promise and Perplexity of Globalism," by W. Longstreet) and seven articles dedicated to exploring the meaning of global education for today's schools. "Global Education: An Overview" (J. Becker) develops possible definitions, identifies objectives and skills, and addresses questions and…
1997
This document contains four papers from a symposium on global human resource development (HRD). "Globalization of Human Resource Management (HRM) in Government: A Cross-Cultural Perspective" (Pan Suk Kim) relates HRM to national cultures and addresses its specific functional aspects with a unique dimension in a global organization.…
Douglas, Ian
2011-01-01
The concept of usability has become an increasingly important consideration in the design of all kinds of technology. As more products are aimed at global markets and developed through internationally distributed teams, usability design needs to be addressed in global terms. Interest in usability as a design issue and specialist area of research and education has developed steadily in North America and Europe since the 1980's. However, it is only over the last ten years that it has emerged as a global concern. Global Usability provides an introduction to the important issues in globalizing des
Rode, Carsten
2013-01-01
High ambitions are set for the building physics performance of buildings today. No single technology can achieve fulfilment of these ambitions alone. Integrated, multi-facetted solutions and optimization are necessary. A holistic, or ‘global’, technological perspective is needed, which includes all...... aspects of the building as defined in building engineering. We live in an international society and building solutions are developed across country borders. Building physics is a global theme. The International Association of Building Physics has global appeal. This brief article reports the keynote...
Rode, Carsten
2012-01-01
High ambitions are set for the building physics performance of buildings today. No single technology can achieve fulfilment of these ambitions alone. Integrated, multi-facetted solutions and optimization are necessary. A holistic, or “global”, technological perspective is needed, which includes all...... aspects of the building as defined in building engineering. We live in an international society and building solutions are developed across country borders. Building physics is a global theme. The International Association of Building Physics has global appeal. The keynote lecture and this brief paper...
Tajuddin, Wan Ahmad
1994-02-01
Ease in finding the configuration at the global energy minimum in a symmetric neural network is important for combinatorial optimization problems. We carry out a comprehensive survey of available strategies for seeking global minima by comparing their performances in the binary representation problem. We recall our previous comparison of steepest descent with analog dynamics, genetic hill-climbing, simulated diffusion, simulated annealing, threshold accepting and simulated tunneling. To this, we add comparisons to other strategies including taboo search and one with field-ordered updating.
Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
Constrained Efficient Global Optimization for Pultrusion Process
Tutum, Cem C.; Deb, Kalyanmoy; Baran, Ismet
2015-01-01
Composite materials, as the name indicates, are composed of different materials that yield superior performance as compared to individual components. Pultrusion is one of the most cost-effective manufacturing techniques for producing fiber-reinforced composites with constant cross-sectional profiles
Adaptive Information Processing and Global Optimization
1991-11-29
IEEE Transactions on Automatic Control , vol. 34, no...Control Schemes Using Least-Squares Parameter Estimates," IEEE Transactions on Automatic Control , vol. 35, no. 5, pp. 416-424, April 1990. 9. H-F...P. R. Kumar, "Distributed Scheduling Based on Due Dates and Buffer Priorities," IEEE Transactions on Automatic Control , pp. 1406-1416, December
An Extended Particle Swarm Optimizer
XU Jun-jie; XIN Zhan-hong
2005-01-01
An Extended Particle Swarm Optimizer (EPSO) is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle's velocity updating process. EPSO is an integration of Local Best paradigm (LBEST) and Global Best paradigm (GBEST) and it significantly enhances the performance of the conventional particle swarm optimizers. The experiment results have proved that EPSO deserves to be investigated.
丁德臣
2011-01-01
目前涉及遗传算法与支持向量机相结合的预测模型中,遗传算法基本上采用的是标准算法.但是在对全局函数的优化中,一般的遗传算法容易陷入局部最优,从而降低遗传算法收敛速度和搜索精度,进而影响财务风险预警模型的精度与速度.基于此,提出了基于混合全局优化正交遗传算法(HOGA)和支持向量机(SVM)的财务风险预警模型(HOGA-SVM),通过使用混合全局优化正交遗传算法连同支持向量机来改进支持向量机进行财务风险预警的效果.结果显示,提出的模型不仅提高了财务风险预警的准确率和速度,而且模型的两类分类错误率(尤其是第一类分类错误率)相对其他模型也有了明显下降.未来的工作可以把模型的应用扩大到多分类的财务风险预警问题中.%Financial risk premonition can exert a significant influence on a company's survival and growth. Many financial risk premonition models generally fall into two categories: the traditional statistics model and the Al model. A hybrid model that incorporates hybrid genetic algorithms and support vector machines ( SVM ) is becoming an important financial risk promotion model.The simple genetic algorithm is frequently used in the hybrid financial risk premonition model. However, convergence rate has a weak performance in the optimization of multi-objective functions. A financial risk premonition model based on the hybrid orthogonal genetic algorithm for global optimization and support vector machine (HOGA-SVM) is proposed to improve the effect of financial risk premonition.HOGA is used to optimize both subset features and SVM parameters simultaneously. The optimization process includes five steps" ( 1 ) encode subset features and SVM parameters. The chromosomes of SVM parameters are encoded as a 16-bit string that consists of 8 bits standing for C and the other 8 bits standing for. The chromosomes of subset features were further encoded as
Global continuation for distance geometry problems
More, J.J.; Wu, Zhijun
1995-03-01
Distance geometry problems arise in the interpretation of NMR data and in the determination of protein structure. The authors formulate the distance geometry problem as a global minimization problem with special structure, and show the global smoothing techniques and a continuation approach for global optimization can be used to determine solutions of distance geometry problems with a nearly 100% probability of success.
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
On the optimality of double-bracket flows
Anthony M. Bloch
2004-01-01
Full Text Available We analyze the optimality of the stable fixed point of the double-bracket equations. We introduce different types of optimality and prove local and global optimality results with respect to the Schatten p-norms.
邹林君; 吴义忠; 毛虎平
2011-01-01
为了解决高效全局优化算法(EGO)中迭代次数增多时构建Kriging模型速度过慢,以及对于某些响应值变化范围较大的目标函数出现过早收敛的问题,提出了增量Kriging方法和基于此方法的改进EGO算法.增量方法利用已经得到的关联矩阵的逆矩阵和新增的数据点忽略关联系数优化的过程,直接进行一系列矩阵运算,得到新关联矩阵的逆矩阵,进而得到更新后的预测模型.改进的EGO算法使用上述的增量方法和更加严谨的停止规则,包括改善期望、自变量和响应值的停止准则.最后使用标准函数分别对增量方法和EGO算法进行测试,结果表明,增量方法可在损失少量精度的情况下大大缩短模型更新的时间,改进的EGO算法具有更高的效率和稳定性.%In efficient global optimization (EGO) algorithm, the time of rebuilding the Kriging model increases rapidly with the increasing of samples' size, and premature convergence may exist when the range of the objective function is too large. To conquer these problems, an incremental Kriging method (IKM) and the improved EGO algorithm are proposed. The inversion of the correlation matrix and the new data points are manipulated to get the coefficients of the Kriging model in IKM, while coefficients of correlation function are optimized and the inversion of new correlation matrix is directly calculated.Stopping criteria on expected improvement, response value and argument are used in the improved EGO algorithm. The experimental results demonstrate that IKM greatly reduces the time of modelling with little loss of accuracy and the improved EGO method has higher efficiency and better stability.
Busck, Ole Gunni
2015-01-01
Øgede globale økologiske distributionskonflikter i kraft af neoliberal globalisering drevet af kravet om økonomisk vækst......Øgede globale økologiske distributionskonflikter i kraft af neoliberal globalisering drevet af kravet om økonomisk vækst...
Jensen, Niels Rosendal
Antologien handler om "demokratiproblemer i den globale sammenhæng" (del I) og "demokratiproblemer i uddannelse og for de offentligt ansatte" (del II), bundet sammen af et mellemstykke, der rækker ud mod begge poler både det globale og det lokale ved at knytte det til forholdet mellem marked...
2009-01-01
The current global financial situation bluntly and brutally brings home the fact that the global and local are closely connected in times of opportunity as well as crises. The articles in this issue of Asia Insights are about ontra-action between Asia, particularly China, and the Nordic countries...
Global Mindsets: Exploration and Perspectives seeks to tackle a topic that is relatively new in research and practice, and is considered by many to be critical for firms seeking to conduct global business. It argues that multiple mindsets exist (across and within organizations), that they operate...... it is important for future scholars and managers and how it could be conceptualized. Global Mindsets: Exploration and Perspectives is split into two major sections; the first examines where the literature currently is with respect to the knowledge in the field and what conceptual frameworks guide the thinking......, and should help researchers determine areas of high potential research needs in the future. Given the fluid sense of global mindsets in action, the authors of this book have adopted a flexible and wide-ranging approach. Global Mindsets: Exploration and Perspectives includes conceptual chapters that push...
Improving Global Development Using Agile
Avritzer, Alberto; Bronsard, Francois; Matos, Gilberto
Global development promises important productivity and capability advantages over centralized work by optimally allocating tasks according to locality, expertise or cost. All too often, global development also introduces a different set of communication and coordination challenges that can negate all the expected benefits and even cause project failures. Most common problems have to do with building trust or quick feedback loops between distributed teams, or with the integration of globally developed components. Agile processes tend to emphasize the intensity of communication, and would seem to be negatively impacted by team distribution. In our experience, these challenges can be overcome, and agile processes can address some of the pitfalls of global development more effectively than plan-driven development. This chapter discusses how to address the difficulties faced when adapting agile processes to global development and the improvements to global development that adopting agile can produce.