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Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.
Dash, Tirtharaj; Sahu, Prabhat K
2015-05-30
The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.
Global optimization of silicon nanowires for efficient parametric processes
DEFF Research Database (Denmark)
Vukovic, Dragana; Xu, Jing; Mørk, Jesper
2013-01-01
We present a global optimization of silicon nanowires for parametric single-pump mixing. For the first time, the effect of surface roughness-induced loss is included in the analysis, significantly influencing the optimum waveguide dimensions....
GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.
Energy Technology Data Exchange (ETDEWEB)
D' Helon, CD
2004-08-18
The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.
Efficient global optimization for black-box simulation via sequential intrinsic Kriging
Mehdad, Ehsan; Kleijnen, J.P.C.
2017-01-01
Efficient Global Optimization (EGO) is a popular method that searches sequentially for the global optimum of a simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic simulation, EGO uses ordinary Kriging (OK), which is a special
Efficient Global Optimization for Black-Box Simulation via Sequential Intrinsic Kriging
Mehdad, Ehsan; Kleijnen, J.P.C.
2015-01-01
Efficient Global Optimization (EGO) is a popular method that searches sequentially for the global optimum of a simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic simulation, EGO uses ordinary Kriging (OK), which is a special
A fuzzy global efficiency optimization of a photovoltaic water pumping system
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Benlarbi, K.; Nait-Said, M.S. [Batna Univ. (Algeria). Dept. of Electrical Engineering; Mokrani, L. [Laghouat Univ. (Algeria). Materials Lab.
2004-07-01
This paper presents an on-line fuzzy optimization of the global efficiency of a photovoltaic water pumping system driven by a separately excited DC motor (DCM), a permanent magnet synchronous motor (PMSM), or an induction motor (IM), coupled to a centrifugal pump. The fuzzy optimization procedure stated above, which aims to the maximization of the global efficiency, will lead consequently to maximize the drive speed and the water discharge rate of the coupled centrifugal pump. The proposed solution is based on a judicious fuzzy adjustment of a chopper ratio which adapts on-line the load impedance to the photovoltaic generator (PVG). Simulation results show the effectiveness of the drive system for both transient and steady state operations. Hence it is suitable to use this fuzzy logic procedure as a standard optimization algorithm for such photovoltaic water pumping drives. (author)
Directory of Open Access Journals (Sweden)
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.
Efficient searching of globally optimal and smooth multi-surfaces with shape priors
Xu, Lei; Stojkovic, Branislav; Ding, Hu; Song, Qi; Wu, Xiaodong; Sonka, Milan; Xu, Jinhui
2012-02-01
Despite extensive studies in the past, the problem of segmenting globally optimal multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method based on a volumetric graph representation of the 3D image that incorporates curvature and shape prior information. Compared with the graph-based method, applying the shape prior to construct the graph on a specific preferred shape model allows easy incorporation of a wide spectrum of shape prior information. Furthermore, the key insight that computation of the objective function can be done independently in the x and y directions makes local improvement possible. Thus, instead of using global optimization technique such as maximum flow algorithm, the iteration based method is much faster. Additionally, the utilization of the curvature in the objective function ensures the smoothness. To the best of our knowledge, this is the first paper to combine the shape-prior penalties with utilizing curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. To evaluate the performance of our method, we test it on a set of 14 3D OCT images. Comparing to the best existing approaches, our experiments suggest that the proposed method reduces the unsigned surface positioning errors form 5.44 +/- 1.07(μm) to 4.52 +/- 0.84(μm). Moreover, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. The proposed method is also suitable for parallel implementation on GPUs, which could potentially allow us to segment highly noisy volumetric images in real time.
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.
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.
Sergeyev, Ya D; Kvasov, D E; Mukhametzhanov, M S
2018-01-11
Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but almost completely disjoint communities (they have different journals, different conferences, different test functions, etc.) solving these problems: a broad community of practitioners using stochastic nature-inspired metaheuristics and people from academia studying deterministic mathematical programming methods. In order to bridge the gap between these communities we propose a visual technique for a systematic comparison of global optimization algorithms having different nature. Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations.
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Romero, Vicente J.
1999-05-18
Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed here (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.
Fühner, Tim; Popp, Stephan; Dürr, Christoph; Erdmann, Andreas
2006-03-01
This paper presents improvements and extensions that have been applied to our earlier presented approach of mutually optimizing lithographic illumination and mask settings. Our work aims at two aspects: (1) improvements of the optimization approach and (2) of the simulation scheme used for the optimization. As described earlier, the main problem of the proposed optimization approach is the high requirement of computation time. One solution is to extensively distribute calculations onto different computers. As an alternative to the former approach using MPI, a new improved technique is proposed, which makes use of the Python powered framework Twisted. This allows for a fail-safe and load-balanced distribution of calculations in a heterogeneous network environment. Another enhancement is the integration of local optimization routines into the proposed concept. For that, the state-of-the-art optimization toolkit of Matlab has been integrated into our approach. By combining our genetic algorithm with local search methods it is not only possible to increase the overall optimization performance, but also to evaluate local environments in the search space, which helps to assess the technical stability of solutions. As a first example the non-linear SQP (sequential quadratic programming) has been used, allowing for constrained problem specifications. Furthermore, the simulation itself was drastically improved in terms of efficiency. For example, instead of evaluating all solutions at the same numerical resolution, as a first step, a coarse-grained evaluation is performed, only if a solution's merit lies above a certain threshold, a detailed analysis at a higher (numerical) resolution is conducted. Various tests demonstrate not only the increase in efficiency obtained with the newly incorporated measures, but also show new results for a combined optimization of different mask features.
Directory of Open Access Journals (Sweden)
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.
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
Rose, Michael T.; Crossan, Angus N.; Kennedy, Ivan R.
2008-01-01
Consideration of the property of action is proposed to provide a more meaningful definition of efficient energy use and sustainable production in ecosystems. Action has physical dimensions similar to angular momentum, its magnitude varying with mass, spatial configuration and relative motion. In this article, the relationship of action to…
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
2014-01-01
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...
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;
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).
Shoemaker, C. A.; Pang, M.; Akhtar, T.; Bindel, D.
2016-12-01
New parallel surrogate global optimization algorithms are developed and applied to objective functions that are expensive simulations (possibly with multiple local minima). The algorithms can be applied to most geophysical simulations, including those with nonlinear partial differential equations. The optimization does not require simulations be parallelized. Asynchronous (and synchronous) parallel execution is available in the optimization toolbox "pySOT". The parallel algorithms are modified from serial to eliminate fine grained parallelism. The optimization is computed with open source software pySOT, a Surrogate Global Optimization Toolbox that allows user to pick the type of surrogate (or ensembles), the search procedure on surrogate, and the type of parallelism (synchronous or asynchronous). pySOT also allows the user to develop new algorithms by modifying parts of the code. In the applications here, the objective function takes up to 30 minutes for one simulation, and serial optimization can take over 200 hours. Results from Yellowstone (NSF) and NCSS (Singapore) supercomputers are given for groundwater contaminant hydrology simulations with applications to model parameter estimation and decontamination management. All results are compared with alternatives. The first results are for optimization of pumping at many wells to reduce cost for decontamination of groundwater at a superfund site. The optimization runs with up to 128 processors. Superlinear speed up is obtained for up to 16 processors, and efficiency with 64 processors is over 80%. Each evaluation of the objective function requires the solution of nonlinear partial differential equations to describe the impact of spatially distributed pumping and model parameters on model predictions for the spatial and temporal distribution of groundwater contaminants. The second application uses an asynchronous parallel global optimization for groundwater quality model calibration. The time for a single objective
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...
An Efficient Algorithm for Unconstrained Optimization
Directory of Open Access Journals (Sweden)
Sergio Gerardo de-los-Cobos-Silva
2015-01-01
Full Text Available This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1 stabilization, (2 breadth-first search, and (3 depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.
Homotopy optimization methods for global optimization.
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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.
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
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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.
LDRD Final Report: Global Optimization for Engineering Science Problems
Energy Technology Data Exchange (ETDEWEB)
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.
Optimization analysis of propulsion motor control efficiency
Directory of Open Access Journals (Sweden)
CAI Qingnan
2017-12-01
Full Text Available [Objectives] This paper aims to strengthen the control effect of propulsion motors and decrease the energy used during actual control procedures.[Methods] Based on the traditional propulsion motor equivalence circuit, we increase the iron loss current component, introduce the definition of power matching ratio, calculate the highest efficiency of a motor at a given speed and discuss the flux corresponding to the power matching ratio with the highest efficiency. In the original motor vector efficiency optimization control module, an efficiency optimization control module is added so as to achieve motor efficiency optimization and energy conservation.[Results] MATLAB/Simulink simulation data shows that the efficiency optimization control method is suitable for most conditions. The operation efficiency of the improved motor model is significantly higher than that of the original motor model, and its dynamic performance is good.[Conclusions] Our motor efficiency optimization control method can be applied in engineering to achieve energy conservation.
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
DEFF Research Database (Denmark)
Le, T.H.A.; Pham, D. T.; Canh, Nam Nguyen
2010-01-01
Both the efficient and weakly efficient sets of an affine fractional vector optimization problem, in general, are neither convex nor given explicitly. Optimization problems over one of these sets are thus nonconvex. We propose two methods for optimizing a real-valued function over the efficient...... and weakly efficient sets of an affine fractional vector optimization problem. The first method is a local one. By using a regularization function, we reformulate the problem into a standard smooth mathematical programming problem that allows applying available methods for smooth programming. In case...... the objective function is linear, we have investigated a global algorithm based upon a branch-and-bound procedure. The algorithm uses Lagrangian bound coupling with a simplicial bisection in the criteria space. Preliminary computational results show that the global algorithm is promising....
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
Shang, C.; Wales, D. J.
2014-08-01
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Modified Grey Wolf Optimizer for Global Engineering Optimization
Directory of Open Access Journals (Sweden)
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.
Asynchronous parallel search in global optimization problems
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Archetti, F.; Schoen, F.
1982-01-01
A class of asynchronous parallel search methods is proposed in order to solve the global optimization problem on a multiprocessor system, consisting of several processors which can communicate through a set of global variables contained in a memory shared by all processors. The speed-up ratio and memory contension effects are experimentally analyzed for some algorithms of this class. 6 references.
On benchmarking Stochastic Global Optimization Algorithms
Hendrix, E.M.T.; Lancinskas, A.
2015-01-01
A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which
GA BASED GLOBAL OPTIMAL DESIGN PARAMETERS FOR ...
African Journals Online (AJOL)
This article uses Genetic Algorithm (GA) for the global design optimization of consecutive reactions taking place in continuous stirred tank reactors (CSTRs) connected in series. GA based optimal design determines the optimum number of CSTRs in series to achieve the maximum conversion, fractional yield and selectivity ...
DEFF Research Database (Denmark)
Achtziger, Wolfgang; Stolpe, Mathias
2007-01-01
to global optimality. In addition, these convex problems can be further relaxed to quadratic programs for which very efficient numerical solution procedures exist. By exploiting this special problem structure, much larger problem instances can be solved to global optimality compared to similar mixed......This paper considers the problem of optimal truss topology design subject to multiple loading conditions. We minimize a weighted average of the compliances subject to a volume constraint. Based on the ground structure approach, the cross-sectional areas are chosen as the design variables. While.......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...
FPSO global strength and hull optimization
Ma, Junyuan; Xiao, Jianhua; Ma, Rui; Cao, Kai
2014-03-01
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.
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....
A Novel Hybrid Firefly Algorithm for Global Optimization.
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.
Directory of Open Access Journals (Sweden)
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.
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...
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...
Measuring Tax Efficiency: A Tax Optimality Index
Raimondos-Møller, Pascalis; Woodland, Alan D
2004-01-01
This paper introduces an index of tax optimality that measures the distance of some current tax structure from the optimal tax structure in the presence of public goods. In doing so, we derive a [0, 1] number that reveals immediately how far the current tax configuration is from the optimal one and, thereby, the degree of efficiency of a tax system. We call this number the Tax Optimality Index. We show how the basic method can be altered in order to derive a revenue equivale...
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'
Vinh, Nguyen Xuan; Chetty, Madhu; Coppel, Ross; Wangikar, Pramod P
2011-10-01
Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks including the gene regulatory network (GRN). Due to the NP-hard nature of learning static Bayesian network structure, most methods for learning DBN also employ either local search such as hill climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing. This article presents GlobalMIT, a toolbox for learning the globally optimal DBN structure from gene expression data. We propose using a recently introduced information theoretic-based scoring metric named mutual information test (MIT). With MIT, the task of learning the globally optimal DBN is efficiently achieved in polynomial time. The toolbox, implemented in Matlab and C++, is available at http://code.google.com/p/globalmit. vinh.nguyen@monash.edu; madhu.chetty@monash.edu Supplementary data is available at Bioinformatics online.
Global optimization of rational multivariate functions
D. Jibetean
2001-01-01
textabstractThe paper deals with unconstrained global minimization of rational functions. A necessary condition is given for the function to have a finite infimum. In case the condition is satisfied, the problem is shown to be equivalent to a specific constrained polynomial optimization problem. In
Efficiency-optimal power partitioning for improved partial load efficiency of electric drives
Béthoux, Olivier; Laboure, Eric; Remy, Ghislain; Berthelot, Éric
2017-01-01
International audience; In this paper, the global power conversion efficiency is improved for a fault-tolerant drive architecture.The fault-tolerant architecture is obtained by combination of a 3-phase open-end winding machine anda 3H-bridge inverter. This combination offers more degrees of freedom for the control strategy than aclassical 3-leg inverter. This paper demonstrates that the additional degree of freedom of this powersystem can be exploited for efficiency optimization purposes in n...
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
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.
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.
Global Optimization Ensemble Model for Classification Methods
Directory of Open Access Journals (Sweden)
Hina Anwar
2014-01-01
Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.
Efficient Reanalysis Procedures in Structural Topology Optimization
DEFF Research Database (Denmark)
Amir, Oded
This thesis examines efficient solution procedures for the structural analysis problem within topology optimization. The research is motivated by the observation that when the nested approach to structural optimization is applied, most of the computational effort is invested in repeated solutions...... effort invested in the solution of the nested problem is even more dominant since nonlinear equation systems are to be solved repeatedly. Efficient procedures for nonlinear structural analysis are proposed, based on transferring solutions and factorized tangent stiffnesses from one design cycle...... is on the utilization of various approximations to the solution of the analysis problem, where the underlying model corresponds to linear elasticity. For computational environments that enable the direct solution of large linear equation systems using matrix factorization, we propose efficient procedures based...
Efficient Iris Localization via Optimization Model
Directory of Open Access Journals (Sweden)
Qi Wang
2017-01-01
Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.
Declining spatial efficiency of global cropland nitrogen allocation
Mueller, Nathaniel D.; Lassaletta, Luis; Runck, Bryan C.; Billen, Gilles; Garnier, Josette; Gerber, James S.
2017-02-01
Efficiently allocating nitrogen (N) across space maximizes crop productivity for a given amount of N input and reduces N losses to the environment. Here we quantify changes in the global spatial efficiency of cropland N use by calculating historical trade-off frontiers relating N inputs to possible N yield assuming efficient allocation. Time series cropland N budgets from 1961 to 2009 characterize the evolution of N input-yield response functions across 12 regions and are the basis for constructing trade-off frontiers. Improvements in agronomic technology have substantially increased cropping system yield potentials and expanded N-driven crop production possibilities. However, we find that these gains are compromised by the declining spatial efficiency of N use across regions. Since the start of the Green Revolution, N inputs and yields have moved farther from the optimal frontier over time; in recent years (1994-2009), global N surplus has grown to a value that is 69% greater than what is possible with efficient N allocation between regions. To reflect regional pollution and agricultural development goals, we construct scenarios that restrict reallocation, finding that these changes only slightly decrease potential gains in nitrogen use efficiency. Our results are inherently conservative due to the regional unit of analysis, meaning a larger potential exists than is quantified here for cross-scale policies to promote spatially efficient N use.
Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis
Directory of Open Access Journals (Sweden)
J. J. Jamian
2014-01-01
Full Text Available The Particle Swarm Optimization (PSO Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.
An Algorithm for Global Optimization Inspired by Collective Animal Behavior
Directory of Open Access Journals (Sweden)
Erik Cuevas
2012-01-01
Full Text Available A metaheuristic algorithm for global optimization called the collective animal behavior (CAB is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
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
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
Globally consistent registration of terrestrial laser scans via graph optimization
Theiler, Pascal Willy; Wegner, Jan Dirk; Schindler, Konrad
2015-11-01
In this paper we present a framework for the automatic registration of multiple terrestrial laser scans. The proposed method can handle arbitrary point clouds with reasonable pairwise overlap, without knowledge about their initial orientation and without the need for artificial markers or other specific objects. The framework is divided into a coarse and a fine registration part, which each start with pairwise registration and then enforce consistent global alignment across all scans. While we put forward a complete, functional registration system, the novel contribution of the paper lies in the coarse global alignment step. Merging multiple scans into a consistent network creates loops along which the relative transformations must add up. We pose the task of finding a global alignment as picking the best candidates from a set of putative pairwise registrations, such that they satisfy the loop constraints. This yields a discrete optimization problem that can be solved efficiently with modern combinatorial methods. Having found a coarse global alignment in this way, the framework proceeds by pairwise refinement with standard ICP, followed by global refinement to evenly spread the residual errors. The framework was tested on six challenging, real-world datasets. The discrete global alignment step effectively detects, removes and corrects failures of the pairwise registration procedure, finally producing a globally consistent coarse scan network which can be used as initial guess for the highly non-convex refinement. Our overall system reaches success rates close to 100% at acceptable runtimes < 1 h, even in challenging conditions such as scanning in the forest.
Optimizing Temporal Queries: Efficient Handling of Duplicates
DEFF Research Database (Denmark)
Toman, David; Bowman, Ivan Thomas
2001-01-01
, these query languages are implemented by translating temporal queries into standard relational queries. However, the compiled queries are often quite cumbersome and expensive to execute even using state-of-the- art relational products. This paper presents an optimization technique that produces more efficient...... translated SQL queries by taking into account the properties of the encoding used for temporal attributes. For concreteness, this translation technique is presented in the context of SQL/TP; however, these techniques are also applicable to other temporal query languages....
Optimal channel efficiency in a sensory network
Mosqueiro, Thiago S.; Maia, Leonardo P.
2013-07-01
Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the
Energy efficiency improvement by gear shifting optimization
Directory of Open Access Journals (Sweden)
Blagojevic Ivan A.
2013-01-01
Full Text Available Many studies have proved that elements of driver’s behavior related to gear selection have considerable influence on the fuel consumption. Optimal gear shifting is a complex task, especially for inexperienced drivers. This paper presents an implemented idea for gear shifting optimization with the aim of fuel consumption minimization with more efficient engine working regimes. Optimized gear shifting enables the best possible relation between vehicle motion regimes and engine working regimes. New theoretical-experimental approach has been developed using On-Board Diagnostic technology which so far has not been used for this purpose. The matrix of driving modes according to which tests were performed is obtained and special data acquisition system and analysis process have been developed. Functional relations between experimental test modes and adequate engine working parameters have been obtained and all necessary operations have been conducted to enable their use as inputs for the designed algorithm. The created Model has been tested in real exploitation conditions on passenger car with Otto fuel injection engine and On-Board Diagnostic connection without any changes on it. The conducted tests have shown that the presented Model has significantly positive effects on fuel consumption which is an important ecological aspect. Further development and testing of the Model allows implementation in wide range of motor vehicles with various types of internal combustion engines.
DEoptim: An R Package for Global Optimization by Differential Evolution
Directory of Open Access Journals (Sweden)
Katharine M. Mullen
2011-04-01
Full Text Available This article describes the R package DEoptim, which implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated by case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-switching generalized autoregressive conditional heteroskedasticity model for the returns of the Swiss Market Index.
Optimal Channel Efficiency in a Sensory Network
Mosqueiro, Thiago S
2012-01-01
We show that the entropy of the distribution of avalanche lifetimes in the Kinouchi-Copelli model always achieves a maximum jointly with the dynamic range. This is noteworthy and nontrivial because while the dynamic range is an equilibrium average measure of the sensibility of a sensory system to a stimulus, the entropy of relaxation times is a purely dynamical quantity, independent of the stimulus rate, that can be interpreted as the efficiency of the network seen as a communication channel. The newly found optimization occurs for all topologies we tested, even when the distribution of avalanche lifetimes itself is not a power-law and when the entropy of the size distribution of avalanches is not concomitantly maximized, strongly suggesting that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.
HRSG design method optimizes power plant efficiency
Energy Technology Data Exchange (ETDEWEB)
Ganapathy, V. (ABCO (US))
1991-05-01
Heat recovery steam generators (HRSGs) are widely used in cogeneration and combined-cycle power plants. simulating the performance of the HRSG system at design and off-design conditions helps the designer optimize the overall plant efficiency. It also helps in the selection of major auxiliary equipment. Conventional simulation of HRSG design and off-design performance is a tedious task, since there are several variables involved. However, with the simplified approach presented in this article, the engineer can acquire information on the performance of the HRSG without actually doing the mechanical design. The engineer does not need to size the tubes or determine the fin configuration. This paper reports that the method also can be used for heat balance studies and in the preparation of the HRSG specification.
Finding multiple reaction pathways via global optimization of action
Lee, Juyong; Lee, In-Ho; Joung, Insuk; Lee, Jooyoung; Brooks, Bernard R.
2017-05-01
Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes.
Global Optimization for Transport Network Expansion and Signal Setting
Directory of Open Access Journals (Sweden)
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.
STP: A Stochastic Tunneling Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
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.
Comprehensive effective and efficient global public health surveillance
Directory of Open Access Journals (Sweden)
McNabb Scott JN
2010-12-01
Full Text Available Abstract At a crossroads, global public health surveillance exists in a fragmented state. Slow to detect, register, confirm, and analyze cases of public health significance, provide feedback, and communicate timely and useful information to stakeholders, global surveillance is neither maximally effective nor optimally efficient. Stakeholders lack a globa surveillance consensus policy and strategy; officials face inadequate training and scarce resources. Three movements now set the stage for transformation of surveillance: 1 adoption by Member States of the World Health Organization (WHO of the revised International Health Regulations (IHR[2005]; 2 maturation of information sciences and the penetration of information technologies to distal parts of the globe; and 3 consensus that the security and public health communities have overlapping interests and a mutual benefit in supporting public health functions. For these to enhance surveillance competencies, eight prerequisites should be in place: politics, policies, priorities, perspectives, procedures, practices, preparation, and payers. To achieve comprehensive, global surveillance, disparities in technical, logistic, governance, and financial capacities must be addressed. Challenges to closing these gaps include the lack of trust and transparency; perceived benefit at various levels; global governance to address data power and control; and specified financial support from globa partners. We propose an end-state perspective for comprehensive, effective and efficient global, multiple-hazard public health surveillance and describe a way forward to achieve it. This end-state is universal, global access to interoperable public health information when it’s needed, where it’s needed. This vision mitigates the tension between two fundamental human rights: first, the right to privacy, confidentiality, and security of personal health information combined with the right of sovereign, national entities
A Collective Neurodynamic Approach to Constrained Global Optimization.
Yan, Zheng; Fan, Jianchao; Wang, Jun
2017-05-01
Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.
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.
MEMORY EFFICIENT SEMI-GLOBAL MATCHING
Directory of Open Access Journals (Sweden)
H. Hirschmüller
2012-07-01
Full Text Available Semi-GlobalMatching (SGM is a robust stereo method that has proven its usefulness in various applications ranging from aerial image matching to driver assistance systems. It supports pixelwise matching for maintaining sharp object boundaries and fine structures and can be implemented efficiently on different computation hardware. Furthermore, the method is not sensitive to the choice of parameters. The structure of the matching algorithm is well suited to be processed by highly paralleling hardware e.g. FPGAs and GPUs. The drawback of SGM is the temporary memory requirement that depends on the number of pixels and the disparity range. On the one hand this results in long idle times due to the bandwidth limitations of the external memory and on the other hand the capacity bounds are quickly reached. A full HD image with a size of 1920 × 1080 pixels and a disparity range of 512 pixels requires already 1 billion elements, which is at least several GB of RAM, depending on the element size, wich are not available at standard FPGA- and GPUboards. The novel memory efficient (eSGM method is an advancement in which the amount of temporary memory only depends on the number of pixels and not on the disparity range. This permits matching of huge images in one piece and reduces the requirements of the memory bandwidth for real-time mobile robotics. The feature comes at the cost of 50% more compute operations as compared to SGM. This overhead is compensated by the previously idle compute logic within the FPGA and the GPU and therefore results in an overall performance increase. We show that eSGM produces the same high quality disparity images as SGM and demonstrate its performance both on an aerial image pair with 142 MPixel and within a real-time mobile robotic application. We have implemented the new method on the CPU, GPU and FPGA.We conclude that eSGM is advantageous for a GPU implementation and essential for an implementation on our FPGA.
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.
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-...
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.
New Heuristics for global optimization of complex bioprocesses
Egea Larrosa, Jose Alberto
2008-01-01
[ENG] Optimization problems arising from the biotechnological and food industries are usually of non-convex nature and they often exhibit several local minima. Even though advances in global optimization research have been outstanding in recent years, the current state-of-the- art is not completely satisfactory, specially when one considers the global optimization of complex process models (typical of biotechnological and food industries). These models are complex due to their dynamic beha...
Global status report on energy efficiency 2008
Blok, K.; van Breevoort, P.; Roes, A.L.; Coenraads, R.; Müller, N.
2008-01-01
There is wide agreement that energy efficiency improvement is one of the key strategies to achieve greater sustainability of the energy system. In the past, the contribution of energy efficiency has already been considerable.Without the energy efficiency improvements achieved since the 1970s,
Global stability-based design optimization of truss structures using ...
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 38; Issue 1. Global stability-based design optimization of truss structures using multiple objectives. Tugrul Talaslioglu ... Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives.
Efficient reanalysis techniques for robust topology optimization
DEFF Research Database (Denmark)
Amir, Oded; Sigmund, Ole; Lazarov, Boyan Stefanov
2012-01-01
shown to yield optimized designs that are tolerant with respect to such manufacturing uncertainties. The main drawback of such procedures is the added computational cost associated with the need to evaluate a set of designs by performing multiple finite element analyses. In this article, we propose......The article focuses on the reduction of the computational effort involved in robust topology optimization procedures. The performance of structures designed by means of topology optimization may be seriously degraded due to fabrication errors. Robust formulations of the optimization problem were...
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.
Heat management methodology for enhanced global efficiency in hybrid electric vehicles
Directory of Open Access Journals (Sweden)
F. Claude
2017-09-01
Full Text Available The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC, real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.
Methods to Optimize for Energy Efficiency
2011-05-01
Prototype Representation & Design Exploration Methods 7 EXERGY -BASED METHODS Feature Presentation Historically: • Energy always an implicit...thermal components Exergy -Based Design Methods: Specify all vehicle design requirements as work potential ( exergy destruction, entropy...PS, ECS, and AFS-A Optimal Vehicles Predicted for Four Optimization Metrics Traditional: • Minimize Gross Takeoff Weight Exergy Methods
Optimizing the Efficiency of Batoid-Inspired Swimming
Quinn, Daniel B.
Traditional propellers lack the combination of efficiency, maneuverability, and stealth found among swimmers in nature. With this deficiency as motivation, two aspects of batoid-inspired swimming are investigated: flexibility and propulsor-boundary interactions. In the case of flexibility, direct force measurements on flexible panels reveal that operating in resonance can increase both thrust and efficiency. Gradient-based optimization is used to isolate the resonant modes of one panel, and Particle Image Velocimetry (PIV) is used to study the optimum and near-optimum conditions. Efficiency is globally optimized when (1) the Strouhal number is within an optimal range that varies weakly with amplitude and boundary conditions; (2) the panel is actuated at a resonant frequency of the fluid-panel system; (3) heave amplitude is tuned such that trailing edge amplitude is maximized while the flow along the body remains attached; and (4) the maximum pitch angle and phase lag are chosen so that the effective angle of attack is minimized. The multi-dimensionality and multi-modality of the efficiency response demonstrate that experimental optimization is well-suited for the design of flexible underwater propulsors. Linear beam theory combined with the Lighthill model offers a dimensionless parameter that can be used to tune propulsors to resonant modes. In self-propelled swimming trials, flexibility is found to increase the swimming economy, even at constant Strouhal number, challenging the traditional view that Strouhal number is a primary indicator of efficiency. Propulsor-boundary interactions are relevant to fish schooling, bodies with multiple fins, and fishes/vehicles that swim near the substrate. In the case of rigid foils operating near a rigid flat boundary, thrust is found to increase monotonically as the foil approaches the ground, and efficiency remains constant. A semi-empirical power law is offered to quantify this behavior, and the same power law is observed in
Finite-size effect on optimal efficiency of heat engines
Tajima, Hiroyasu; Hayashi, Masahito
2017-07-01
The optimal efficiency of quantum (or classical) heat engines whose heat baths are n -particle systems is given by the strong large deviation. We give the optimal work extraction process as a concrete energy-preserving unitary time evolution among the heat baths and the work storage. We show that our optimal work extraction turns the disordered energy of the heat baths to the ordered energy of the work storage, by evaluating the ratio of the entropy difference to the energy difference in the heat baths and the work storage, respectively. By comparing the statistical mechanical optimal efficiency with the macroscopic thermodynamic bound, we evaluate the accuracy of the macroscopic thermodynamics with finite-size heat baths from the statistical mechanical viewpoint. We also evaluate the quantum coherence effect on the optimal efficiency of the cycle processes without restricting their cycle time by comparing the classical and quantum optimal efficiencies.
Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka
2013-01-01
Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383
Measuring global logistics efficiency using PCA-DEA approach
Directory of Open Access Journals (Sweden)
Andrejić Milan M.
2016-01-01
Full Text Available In situation of increasing global trade, concentration of production and economic crisis cause the increasing importance of freight transport and logistics. In that manner, economic performances of a country are very affected by logistics performances. Measuring efficiency of logistics activities on global level is important for all participants in international trade. This paper proposes the new methodology for measuring global logistics efficiency that integrates international and domestic indicators into single measure. The Principal Component Analysis - Data Envelopment Analysis (PCA-DEA approach is used in this paper. Proposed approach is tested on a numerical example which consists of eight countries. According obtained efficiency scores observed countries are ranked. The paper also identifies the most important factors affecting the global efficiency. Several hypotheses are tested in this paper. The results show that the proposed approach can be used for evaluation of logistics activities at the global level.
Assessing global resource utilization efficiency in the industrial sector.
Rosen, Marc A
2013-09-01
Designing efficient energy systems, which also meet economic, environmental and other objectives and constraints, is a significant challenge. In a world with finite natural resources and large energy demands, it is important to understand not just actual efficiencies, but also limits to efficiency, as the latter identify margins for efficiency improvement. Energy analysis alone is inadequate, e.g., it yields energy efficiencies that do not provide limits to efficiency. To obtain meaningful and useful efficiencies for energy systems, and to clarify losses, exergy analysis is a beneficial and useful tool. Here, the global industrial sector and industries within it are assessed by using energy and exergy methods. The objective is to improve the understanding of the efficiency of global resource use in the industrial sector and, with this information, to facilitate the development, prioritization and ultimate implementation of rational improvement options. Global energy and exergy flow diagrams for the industrial sector are developed and overall efficiencies for the global industrial sector evaluated as 51% based on energy and 30% based on exergy. Consequently, exergy analysis indicates a less efficient picture of energy use in the global industrial sector than does energy analysis. A larger margin for improvement exists from an exergy perspective, compared to the overly optimistic margin indicated by energy. Copyright © 2012 Elsevier B.V. All rights reserved.
Gradient-Based Cuckoo Search for Global Optimization
Directory of Open Access Journals (Sweden)
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.
DEFF Research Database (Denmark)
Gregg, Jay Sterling; Balyk, Olexandr; Pérez, Cristian Hernán Cabrera
This study examines the three objectives of the UN Sustainable Energy for All (SE4ALL) initiative: 1. Ensure universal access to modern energy services by 2030. 2. Double the global rate of improvement in energy efficiency (from 1.3% to 2.6% annual reduction in energy intensity of GDP) by 2030. 3....... This analysis is conducted on a global and regional scale. The scenarios were constructed to analyze the effect of achieving the SE4ALL energy efficiency objective, the SE4ALL renewable energy objective, both together, and all three SE4ALL objectives. Synergies exist between renewable energy and energy...... efficiency. When the SE4ALL renewable energy objective is achieved, the economically optimal solution produced by ETSAP-TIAM also includes a reduction in energy intensity: globally, the compound annual reduction in energy intensity of GDP is 1.8% when the renewable energy objective is achieved. Likewise...
Globalized robust optimization for nonlinear uncertain inequalities
Ben-Tal, A.; Brekelmans, Ruud; den Hertog, Dick; Vial, J.P.
Robust optimization is a methodology that can be applied to problems that are affected by uncertainty in their parameters. The classical robust counterpart of a problem requires the solution to be feasible for all uncertain parameter values in a so-called uncertainty set and offers no guarantees for
Resource Efficient Scheduling, Optimization and Control.
van den Akker, Marjan|info:eu-repo/dai/nl/112066453; Derksen, Christian; Kowalczyk, Ryszard; Mönch, Lars; Valogianni, Konstantina; van Heck, Erik; Zhang, Minjie
2014-01-01
This report documents the program and the outcomes of Dagstuhl Seminar 14181 "Multi-agent systems and their role in future energy grids". A number of recent events (e.g. Fukushima, Japan, and the largest blackout in history, India) have once again increased global attention on climate change and
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use
On Global Optimization of Utility Plant Operations
Almakhaita, Mohammed Mansoor
2017-01-01
This work presents an optimization based formulation of a utility plant’s operations. The operational purpose of the utility plant is to meet three demands for a client facility: heat in the form of process steam demand, mechanical power, and electrical power. The optimization’s goal is to minimize the utility plant’s operating cost, which consists of a cost component related to fuel consumption, a cost (revenue) component related to electricity consumption (electricity generation), and a lab...
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.
Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval.
Zhang, P G; Yang, C L; Xu, Z H; Cao, Z L; Mu, Q Q; Xuan, L
2016-10-31
The core problem of phase diversity phase retrieval (PDPR) is to find suitable optimization algorithms for wave-front sensing of different scales, especially for large-scale wavefront sensing. When dealing with large-scale wave-front sensing, existing gradient-based local optimization algorithms used in PDPR are easily trapped in local minimums near initial positions, and available global optimization algorithms possess low convergence efficiency. We construct a practicable optimization algorithm used in PDPR for large-scale wave-front sensing. This algorithm, named EPSO-BFGS, is a two-step hybrid global optimization algorithm based on the combination of evolutionary particle swarm optimization (EPSO) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Firstly, EPSO provides global search and obtains a rough global minimum position in limited search steps. Then, BFGS initialized by the rough global minimum position approaches the global minimum with high accuracy and fast convergence speed. Numerical examples testify to the feasibility and reliability of EPSO-BFGS for wave-front sensing of different scales. Two numerical cases also validate the ability of EPSO-BFGS for large-scale wave-front sensing. The effectiveness of EPSO-BFGS is further affirmed by performing a verification experiment.
The optimal mechanical efficiency of laparoscopic forceps
Heijnsdijk, E. A. M.; Pasdeloup, A.; Dankelman, J.; Gouma, D. J.
2004-01-01
Background Laparoscopic forceps have a large amount of friction in the mechanism, leading to a limited mechanical efficiency, which impairs the pinch force feedback. The advantage of a small amount of friction is that it is easier to maintain a constant pinch force on the tissue. Therefore, to
Ringed Seal Search for Global Optimization via a Sensitive Search Model
Younes Saadi; Iwan Tri Riyadi Yanto; Tutut Herawan; Vimala Balakrishnan; Haruna Chiroma; Anhar Risnumawan
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to ...
Efficient architecture for global elimination algorithm for H. 264 ...
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 41; Issue 1. Efficient ... Fast block matching motion estimation; global elimination; matching complexity reduction; power reduction. ... The proposed architecture is based on Global Elimination (GE) Algorithm, which uses pixel averaging to reduce complexity of motion search while keeping ...
Joint optimization of spectrum and energy efficiency in cognitive radio networks
Directory of Open Access Journals (Sweden)
Shaowei Wang
2015-08-01
Full Text Available In this paper, we discuss the joint improvement of the energy efficiency (EE and the spectrum efficiency (SE in OFDM-based cognitive radio (CR networks. A multi-objective resource allocation task is formulated to optimize the EE and the SE of the CR system simultaneously with the consideration of the mutual interference and the spectrum sensing errors. We first exploit the EE–SE relations and demonstrate that the EE is a quasiconcave function of the SE, based on which the Pareto optimal set of the multi-objective optimization problem is characterized. To find a unique globally optimal solution, we propose a unified EE–SE tradeoff metric to transform the multi-objective optimization problem into a single-objective one which has a D.C. (difference of two convex functions/sets structure and yields a standard convex optimization problem. We derive a fast method to speed up the time-consuming computation by exploiting the structure of the convex problem. Simulation results validate the effectiveness and efficiency of the proposed algorithms, which can produce the unique globally optimal solution of the original multi-objective optimization problem.
Stability Constrained Efficiency Optimization for Droop Controlled DC-DC Conversion System
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
2013-01-01
implementing tertiary regulation. Moreover, system dynamic is affected when shifting VRs. Therefore, the stability is considered in optimization by constraining the eigenvalues arising from dynamic state space model of the system. Genetic algorithm is used in searching for global efficiency optimum while...
Optimal Energy Taxation for Environment and Efficiency
Energy Technology Data Exchange (ETDEWEB)
Pak, Y.D. [Korea Energy Economics Institute, Euiwang (Korea)
2001-11-01
Main purpose of this research is to investigate about how to use energy tax system to reconcile environmental protection and economic growth, and promote sustainable development with the emphasis of double dividend hypothesis. As preliminary work to attain this target, in this limited study I will investigate the specific conditions under which double dividend hypothesis can be valid, and set up the model for optimal energy taxation. The model will be used in the simulation process in the next project. As the beginning part in this research, I provide a brief review about energy taxation policies in Sweden, Netherlands, and the United States. From this review it can be asserted that European countries are more aggressive in the application of environmental taxes like energy taxes for a cleaner environment than the United States. In next part I examined the rationale for optimal environmental taxation in the first-best and the second-best setting. Then I investigated energy taxation how it can provoke various distortions in markets and be connected to the marginal environmental damages and environmental taxation. In the next chapter, I examined the environmentally motivated taxation in the point of optimal commodity taxation view. Also I identified the impacts of environmental taxation in various circumstances intensively to find out when the environment tax can yield double dividend after taking into account of even tax-interaction effects. Then it can be found that even though in general the environmental tax exacerbates the distortion in the market rather than alleviates, it can also improve the welfare and the employment under several specific circumstances which are classified as various inefficiencies in the existing tax system. (author). 30 refs.
Wolf Pack Algorithm for Unconstrained Global Optimization
Directory of Open Access Journals (Sweden)
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.
Optimal database locks for efficient integrity checking
DEFF Research Database (Denmark)
Martinenghi, Davide
2004-01-01
In concurrent database systems, correctness of update transactions refers to the equivalent effects of the execution schedule and some serial schedule over the same set of transactions. Integrity constraints add further semantic requirements to the correctness of the database states reached upon...... the execution of update transactions. Several methods for efficient integrity checking and enforcing exist. We show in this paper how to apply one such method to automatically extend update transactions with locks and simplified consistency tests on the locked entities. All schedules produced in this way...... are conflict serializable and preserve consistency. For certain classes of databases we also guarantee that the amount of locked database entities is minimal....
An optimal speech processor for efficient human speech ...
Indian Academy of Sciences (India)
Abstract. The transmitter and the receiver in a communication system have to be designed optimally with respect to one another to ensure reliable and efficient com- munication. Following this principle, we derive an optimal filterbank for processing speech signal in the listener's auditory system (receiver), so that maximum ...
A Method for Determining Optimal Residential Energy Efficiency Packages
Energy Technology Data Exchange (ETDEWEB)
Polly, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gestwick, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bianchi, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Horowitz, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Christensen, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Judkoff, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2011-04-01
This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location.
Meningococcal conjugate vaccines: optimizing global impact
Directory of Open Access Journals (Sweden)
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
Directory of Open Access Journals (Sweden)
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.
Closing the Gap GEF Experiences in Global Energy Efficiency
Yang, Ming
2013-01-01
Energy efficiency plays and will continue to play an important role in the world to save energy and mitigate greenhouse gas (GHG) emissions. However, little is known on how much additional capital should be invested to ensure using energy efficiently as it should be, and very little is known which sub-areas, technologies, and countries shall achieve maximum greenhouse gas emissions mitigation per dollar of investment in energy efficiency worldwide. Analyzing completed and slowly moving energy efficiency projects by the Global Environment Facility during 1991-2010, Closing the Gap: GEF Experiences in Global Energy Efficiency evaluates impacts of multi-billion-dollar investments in the world energy efficiency. It covers the following areas: 1. Reviewing the world energy efficiency investment and disclosing the global energy efficiency gap and market barriers that cause the gap; 2. Leveraging private funds with public funds and other resources in energy efficiency investments; using...
Online optimization of a multi-conversion-level DC home microgrid for system efficiency enhancement
DEFF Research Database (Denmark)
Boscaino, V.; Guerrero, J. M.; Ciornei, I.
2017-01-01
In this paper, an on-line management system for the optimal efficiency operation of a multi-bus DC home distribution system is proposed. The operation of the system is discussed with reference to a distribution system with two conversion stages and three voltage levels. In each of the conversion...... stages, three paralleled DC/DC converters are implemented. A Genetic Algorithm performs the on-line optimization of the DC network’s global efficiency, generating the optimal current sharing ratios of the concurrent power converters. The overall DC/DC conversion system including the optimization section...... for, such as: components loss parameters, components ageing, load currents, switching frequency and input voltage. Simulation results considering several case studies are presented and the benefits brought by the optimization algorithm in terms of power saving are widely discussed for each case study....
Exergetic efficiency optimization for an irreversible heat pump ...
Indian Academy of Sciences (India)
temperature heat reservoirs by taking exergetic efficiency as the optimization objective combining exergy concept with finite-time thermodynamics (FTT). Exergetic efficiency is defined as the ratio of rate of exergy output to rate of exergy input of the system ...
A Concept for Optimizing Behavioural Effectiveness & Efficiency
Barca, Jan Carlo; Rumantir, Grace; Li, Raymond
Both humans and machines exhibit strengths and weaknesses that can be enhanced by merging the two entities. This research aims to provide a broader understanding of how closer interactions between these two entities can facilitate more optimal goal-directed performance through the use of artificial extensions of the human body. Such extensions may assist us in adapting to and manipulating our environments in a more effective way than any system known today. To demonstrate this concept, we have developed a simulation where a semi interactive virtual spider can be navigated through an environment consisting of several obstacles and a virtual predator capable of killing the spider. The virtual spider can be navigated through the use of three different control systems that can be used to assist in optimising overall goal directed performance. The first two control systems use, an onscreen button interface and a touch sensor, respectively to facilitate human navigation of the spider. The third control system is an autonomous navigation system through the use of machine intelligence embedded in the spider. This system enables the spider to navigate and react to changes in its local environment. The results of this study indicate that machines should be allowed to override human control in order to maximise the benefits of collaboration between man and machine. This research further indicates that the development of strong machine intelligence, sensor systems that engage all human senses, extra sensory input systems, physical remote manipulators, multiple intelligent extensions of the human body, as well as a tighter symbiosis between man and machine, can support an upgrade of the human form.
Computational optimization of global gyrokinetic particle code GTS
Krishna Swamy, Aditya; Ethier, Stephane; Wang, Weixing; Startsev, Edward; Lee, Wei-Li; Ganesh, Rajaraman
2017-10-01
Electromagnetic microturbulence is an important source of anomalous ion and electron transport in tokamak plasmas. Gyrokinetic Tokamak Simulation (GTS), a global PIC code presents a first-principles based method to understand and predict such transport. Recently, the double-split-weight scheme that avoids the high-beta ``cancelation problem'' has been developed and implemented in GTS to study electromagnetic turbulence. Use of magnetic coordinates and a field-line following grid in GTS provides a highly efficient means to resolve a relatively larger set of modes in the same run. The misalignment of the field-line following grid with cylindrical grid, however, makes Fourier-filtering of single mode highly inefficient, and therefore makes benchmarking of linear modes with other codes time consuming. Recent algorithmic optimizations to align this subroutine with the 2-d domain have resulted in a significant performance improvement of 20x, with an overall code speedup of 3x. These and further improvements to the filtering capability, along with linear benchmarks of electromagnetic instabilities such as MTM and KBM will be discussed. SERB Indo-US Research Fellowship.
Maximizing the efficiency of multienzyme process by stoichiometry optimization.
Dvorak, Pavel; Kurumbang, Nagendra P; Bendl, Jaroslav; Brezovsky, Jan; Prokop, Zbynek; Damborsky, Jiri
2014-09-05
Multienzyme processes represent an important area of biocatalysis. Their efficiency can be enhanced by optimization of the stoichiometry of the biocatalysts. Here we present a workflow for maximizing the efficiency of a three-enzyme system catalyzing a five-step chemical conversion. Kinetic models of pathways with wild-type or engineered enzymes were built, and the enzyme stoichiometry of each pathway was optimized. Mathematical modeling and one-pot multienzyme experiments provided detailed insights into pathway dynamics, enabled the selection of a suitable engineered enzyme, and afforded high efficiency while minimizing biocatalyst loadings. Optimizing the stoichiometry in a pathway with an engineered enzyme reduced the total biocatalyst load by an impressive 56 %. Our new workflow represents a broadly applicable strategy for optimizing multienzyme processes. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
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.
Efficiency Optimization in Class-D Audio Amplifiers
DEFF Research Database (Denmark)
Yamauchi, Akira; Knott, Arnold; Jørgensen, Ivan Harald Holger
2015-01-01
This paper presents a new power efficiency optimization routine for designing Class-D audio amplifiers. The proposed optimization procedure finds design parameters for the power stage and the output filter, and the optimum switching frequency such that the weighted power losses are minimized under...... the given constraints. The optimization routine is applied to minimize the power losses in a 130 W class-D audio amplifier based on consumer behavior investigations, where the amplifier operates at idle and low power levels most of the time. Experimental results demonstrate that the optimization method can...
The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method
Directory of Open Access Journals (Sweden)
Liang Shen
2017-01-01
Full Text Available There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.
An efficient algorithm for global periodic orbits generation near irregular-shaped asteroids
Shang, Haibin; Wu, Xiaoyu; Ren, Yuan; Shan, Jinjun
2017-07-01
Periodic orbits (POs) play an important role in understanding dynamical behaviors around natural celestial bodies. In this study, an efficient algorithm was presented to generate the global POs around irregular-shaped uniformly rotating asteroids. The algorithm was performed in three steps, namely global search, local refinement, and model continuation. First, a mascon model with a low number of particles and optimized mass distribution was constructed to remodel the exterior gravitational potential of the asteroid. Using this model, a multi-start differential evolution enhanced with a deflection strategy with strong global exploration and bypassing abilities was adopted. This algorithm can be regarded as a search engine to find multiple globally optimal regions in which potential POs were located. This was followed by applying a differential correction to locally refine global search solutions and generate the accurate POs in the mascon model in which an analytical Jacobian matrix was derived to improve convergence. Finally, the concept of numerical model continuation was introduced and used to convert the POs from the mascon model into a high-fidelity polyhedron model by sequentially correcting the initial states. The efficiency of the proposed algorithm was substantiated by computing the global POs around an elongated shoe-shaped asteroid 433 Eros. Various global POs with different topological structures in the configuration space were successfully located. Specifically, the proposed algorithm was generic and could be conveniently extended to explore periodic motions in other gravitational systems.
Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
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 new discrete filled function algorithm for discrete global optimization
Yongjian, Yang; Yumei, Liang
2007-05-01
A definition of the discrete filled function is given in this paper. Based on the definition, a discrete filled function is proposed. Theoretical properties of the proposed discrete filled function are investigated, and an algorithm for discrete global optimization is developed from the new discrete filled function. The implementation of the algorithms on several test problems is reported with satisfactory numerical results.
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.
A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints.
Le, Xinyi; Chen, Sijie; Yan, Zheng; Xi, Juntong
2017-10-18
In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.
Language- and Machine-Independent Global Optimization on Intermediate Code
Bal, H.E.; Tanenbaum, A.S.
1986-01-01
Many retargetable production compilers use some form on intermediate code for applying global optimizations. The compilers of the Amsterdam Compiler Kit use a low-level, language- and machine-independent, intermediate code called EM. The choice of intermediate code has impact on both the
Directory of Open Access Journals (Sweden)
Dao-Wei Bi
2007-05-01
Full Text Available The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and DijkstraÃ¢Â€Â™s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.
Global Optimization of Nonlinear Blend-Scheduling Problems
Directory of Open Access Journals (Sweden)
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.
Global climate change: Mitigation opportunities high efficiency large chiller technology
Energy Technology Data Exchange (ETDEWEB)
Stanga, M.V.
1997-12-31
This paper, comprised of presentation viewgraphs, examines the impact of high efficiency large chiller technology on world electricity consumption and carbon dioxide emissions. Background data are summarized, and sample calculations are presented. Calculations show that presently available high energy efficiency chiller technology has the ability to substantially reduce energy consumption from large chillers. If this technology is widely implemented on a global basis, it could reduce carbon dioxide emissions by 65 million tons by 2010.
Method for Determining Optimal Residential Energy Efficiency Retrofit Packages
Energy Technology Data Exchange (ETDEWEB)
Polly, B.; Gestwick, M.; Bianchi, M.; Anderson, R.; Horowitz, S.; Christensen, C.; Judkoff, R.
2011-04-01
Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.
Efficiency optimized control of medium-size induction motor drives
DEFF Research Database (Denmark)
Abrahamsen, F.; Blaabjerg, Frede; Pedersen, John Kim
2000-01-01
The efficiency of a variable speed induction motor drive can be optimized by adaption of the motor flux level to the load torque. In small drives (small converter losses, but for medium-size drives (10-1000 kW) the losses can...... not be disregarded without further analysis. The importance of the converter losses on efficiency optimization in medium-size drives is analyzed in this paper. Based on the experiments with a 90 kW drive it is found that it is not critical if the converter losses are neglected in the control, except...... that the robustness towards load disturbances may unnecessarily be reduced. Both displacement power factor and model-based efficiency optimizing control methods perform well in medium-size drives. The last strategy is also tested on a 22 kW drive with good results....
Optimization of Flapping Airfoils for Maximum Thrust and Propulsive Efficiency
Directory of Open Access Journals (Sweden)
I. H. Tuncer
2004-01-01
Full Text Available A numerical optimization algorithm based on the steepest decent along the variation of the optimization function is implemented for maximizing the thrust and/or propulsive efficiency of a single flapping airfoil. Unsteady, low speed laminar flows are computed using a Navier-Stokes solver on moving overset grids. The flapping motion of the airfoil is described by a combined sinusoidal plunge and pitching motion. Optimization parameters are taken to be the amplitudes of the plunge and pitching motions, and the phase shift between them. Computations are performed in parallel in a work station cluster. The numerical simulations show that high thrust values may be obtained at the expense of reduced efficiency. For high efficiency in thrust generation, the induced angle of attack of the airfoil is reduced and large scale vortex formations at the leading edge are prevented.
An efficient approach for reliability-based topology optimization
Kanakasabai, Pugazhendhi; Dhingra, Anoop K.
2016-01-01
This article presents an efficient approach for reliability-based topology optimization (RBTO) in which the computational effort involved in solving the RBTO problem is equivalent to that of solving a deterministic topology optimization (DTO) problem. The methodology presented is built upon the bidirectional evolutionary structural optimization (BESO) method used for solving the deterministic optimization problem. The proposed method is suitable for linear elastic problems with independent and normally distributed loads, subjected to deflection and reliability constraints. The linear relationship between the deflection and stiffness matrices along with the principle of superposition are exploited to handle reliability constraints to develop an efficient algorithm for solving RBTO problems. Four example problems with various random variables and single or multiple applied loads are presented to demonstrate the applicability of the proposed approach in solving RBTO problems. The major contribution of this article comes from the improved efficiency of the proposed algorithm when measured in terms of the computational effort involved in the finite element analysis runs required to compute the optimum solution. For the examples presented with a single applied load, it is shown that the CPU time required in computing the optimum solution for the RBTO problem is 15-30% less than the time required to solve the DTO problems. The improved computational efficiency allows for incorporation of reliability considerations in topology optimization without an increase in the computational time needed to solve the DTO problem.
Efficiency optimized control of medium-size induction motor drives
DEFF Research Database (Denmark)
Abrahamsen, F.; Blaabjerg, Frede; Pedersen, John Kim
2000-01-01
The efficiency of a variable speed induction motor drive can be optimized by adaption of the motor flux level to the load torque. In small drives (<10 kW) this can be done without considering the relatively small converter losses, but for medium-size drives (10-1000 kW) the losses can not be disr......The efficiency of a variable speed induction motor drive can be optimized by adaption of the motor flux level to the load torque. In small drives (
Global energy efficiency governance in the context of climate politics
Gupta, J.; Ivanova, A.
2009-01-01
This paper argues that energy efficiency and conservation is a noncontroversial, critical, and equitable option for rich and poor alike. Although there is growing scientific and political consensus on its significance as an important option at global and national level, the political momentum for
Water use efficiency of net primary production in global terrestrial ...
Indian Academy of Sciences (India)
Water use efficiency; global terrestrial ecosystems; MODIS; net primary production; evapotranspiration;. Köppen–Geiger climate classification. ... Terrestrial plants fix or trap carbon dioxide via photosynthesis to produce the material ...... S W 2007 Evaluating water stress controls on primary production in biogeochemical and ...
Water use efficiency of net primary production in global terrestrial ...
Indian Academy of Sciences (India)
The carbon and water cycles of terrestrial ecosystems, which are strongly coupled via water use efficiency (WUE), are influenced by global climate change. To explore the relationship between the carbon and water cycles and predict the effect of climate change on terrestrial ecosystems, it is necessary to study the WUE in ...
Optimal discrimination index and discrimination efficiency for essay questions.
Chan, Wing-shing
2014-01-01
Recommended guidelines for discrimination index of multiple choice questions are often indiscriminately applied to essay type questions also. Optimal discrimination index under normality condition for essay question is independently derived. Satisfactory region for discrimination index of essay questions with passing mark at 50% of the total is between 0.12 and 0.31 instead of 0.40 or more in the case for multiple-choice questions. Optimal discrimination index for essay question is shown to increase proportional to the range of scores. Discrimination efficiency as the ratio of the observed discrimination index over the optimal discrimination index is defined. Recommended guidelines for discrimination index of essay questions are provided.
Optimal shaping and positioning of energy-efficient buildings
Directory of Open Access Journals (Sweden)
Barović Dušan D.
2017-01-01
Full Text Available Due to the number of variables and the complexity of objective functions, optimal design of an energy-efficient building is hard combinatorial problem of multi-objective optimisation. Therefore, it is necessary to describe structure and its position in surroundings precisely but by as few variables as possible. This paper presents methodology for finding adequate methodology for defining geometry and orientation of a given building, as well as its elements of importance for energy-efficiency analysis.
IFDR: An Efficient Iterative Optimization Algorithm for Standard Cell Placement
Feng Cheng; Junfa Mao
2004-01-01
In the automatic placement of integrated circuits, the force directed relaxation (FDR) method [Goto, S. (1981). An efficient algorithm for the two-dimensional placement problem in electrical circuit layout. IEEE Trans. on Circuits and Systems, CAS-28(1), 12-18] is a good iterative optimization algorithm. In this article, an improved force directed relaxation (IFDR) method for standard cell placement is presented, which provides a more flexible and efficient cell location adjustment scheme and...
Prioritized efficiency optimization for intensity modulated proton therapy.
Müller, Birgit S; Wilkens, Jan J
2016-12-07
A high dosimetric quality and short treatment time are major goals in radiotherapy planning. Intensity modulated proton therapy (IMPT) plans obtain dose distributions of great conformity but often result in long delivery times which are typically not incorporated into the optimization process. We present an algorithm to optimize delivery efficiency of IMPT plans while maintaining plan quality, and study the potential trade-offs of these interdependent objectives. The algorithm is based on prioritized optimization, a stepwise approach to implemented objectives. First the quality of the plan is optimized. The second step of the prioritized efficiency optimization (PrEfOpt) routine offers four alternatives for reducing delivery time: minimization of the total spot weight sum (A), maximization of the lowest spot intensity of each energy layer (B), elimination of low-weighted spots (C) or energy layers (D). The trade-off between dosimetric quality (step I) and treatment time (step II) is controlled during the optimization by option-dependent parameters. PrEfOpt was applied to a clinical patient case, and plans for different trade-offs were calculated. Delivery times were simulated for two virtual facilities with constant and variable proton current, i.e. independent and dependent on the optimized spot weight distributions. Delivery times decreased without major degradation of plan quality; absolute time reductions varied with the applied method and facility type. Minimizing the total spot weight sum (A) reduced times by 28% for a similar plan quality at a constant current (changes of minimum dose in the target optimization step into the optimization process can yield reduced delivery times with similar plan qualities. A potential clinical application of PrEfOpt is the generation of multiple plans with different trade-offs for a multicriteria optimization setting. Then, the planner can select the preferred compromise between treatment time and quality for each
Global structure search for molecules on surfaces: Efficient sampling with curvilinear coordinates
Krautgasser, Konstantin; Panosetti, Chiara; Palagin, Dennis; Reuter, Karsten; Maurer, Reinhard J.
2016-08-01
Efficient structure search is a major challenge in computational materials science. We present a modification of the basin hopping global geometry optimization approach that uses a curvilinear coordinate system to describe global trial moves. This approach has recently been shown to be efficient in structure determination of clusters [C. Panosetti et al., Nano Lett. 15, 8044-8048 (2015)] and is here extended for its application to covalent, complex molecules and large adsorbates on surfaces. The employed automatically constructed delocalized internal coordinates are similar to molecular vibrations, which enhances the generation of chemically meaningful trial structures. By introducing flexible constraints and local translation and rotation of independent geometrical subunits, we enable the use of this method for molecules adsorbed on surfaces and interfaces. For two test systems, trans-β-ionylideneacetic acid adsorbed on a Au(111) surface and methane adsorbed on a Ag(111) surface, we obtain superior performance of the method compared to standard optimization moves based on Cartesian coordinates.
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
Optimization and performance of bifacial solar modules: A global perspective
Energy Technology Data Exchange (ETDEWEB)
Sun, Xingshu; Khan, Mohammad Ryyan; Deline, Chris; Alam, Muhammad Ashraful
2018-02-01
With the rapidly growing interest in bifacial photovoltaics (PV), a worldwide map of their potential performance can help assess and accelerate the global deployment of this emerging technology. However, the existing literature only highlights optimized bifacial PV for a few geographic locations or develops worldwide performance maps for very specific configurations, such as the vertical installation. It is still difficult to translate these location- and configuration-specific conclusions to a general optimized performance of this technology. In this paper, we present a global study and optimization of bifacial solar modules using a rigorous and comprehensive modeling framework. Our results demonstrate that with a low albedo of 0.25, the bifacial gain of ground-mounted bifacial modules is less than 10% worldwide. However, increasing the albedo to 0.5 and elevating modules 1 m above the ground can boost the bifacial gain to 30%. Moreover, we derive a set of empirical design rules, which optimize bifacial solar modules across the world and provide the groundwork for rapid assessment of the location-specific performance. We find that ground-mounted, vertical, east-west-facing bifacial modules will outperform their south-north-facing, optimally tilted counterparts by up to 15% below the latitude of 30 degrees, for an albedo of 0.5. The relative energy output is reversed in latitudes above 30 degrees. A detailed and systematic comparison with data from Asia, Africa, Europe, and North America validates the model presented in this paper.
An Optimal Method for Developing Global Supply Chain Management System
Directory of Open Access Journals (Sweden)
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.
Optimization of aerodynamic efficiency for twist morphing MAV wing
Directory of Open Access Journals (Sweden)
N.I. Ismail
2014-06-01
Full Text Available Twist morphing (TM is a practical control technique in micro air vehicle (MAV flight. However, TM wing has a lower aerodynamic efficiency (CL/CD compared to membrane and rigid wing. This is due to massive drag penalty created on TM wing, which had overwhelmed the successive increase in its lift generation. Therefore, further CL/CDmax optimization on TM wing is needed to obtain the optimal condition for the morphing wing configuration. In this paper, two-way fluid–structure interaction (FSI simulation and wind tunnel testing method are used to solve and study the basic wing aerodynamic performance over (non-optimal TM, membrane and rigid wings. Then, a multifidelity data metamodel based design optimization (MBDO process is adopted based on the Ansys-DesignXplorer frameworks. In the adaptive MBDO process, Kriging metamodel is used to construct the final multifidelity CL/CD responses by utilizing 23 multi-fidelity sample points from the FSI simulation and experimental data. The optimization results show that the optimal TM wing configuration is able to produce better CL/CDmax magnitude by at least 2% than the non-optimal TM wings. The flow structure formation reveals that low TV strength on the optimal TM wing induces low CD generation which in turn improves its overall CL/CDmax performance.
Efficient topology optimization in MATLAB using 88 lines of code
DEFF Research Database (Denmark)
Andreassen, Erik; Clausen, Anders; Schevenels, Mattias
2011-01-01
The paper presents an efficient 88 line MATLAB code for topology optimization. It has been developed using the 99 line code presented by Sigmund (Struct Multidisc Optim 21(2):120–127, 2001) as a starting point. The original code has been extended by a density filter, and a considerable improvement...... in efficiency has been achieved, mainly by preallocating arrays and vectorizing loops. A speed improvement with a factor of 100 is obtained for a benchmark example with 7,500 elements. Moreover, the length of the code has been reduced to a mere 88 lines. These improvements have been accomplished without...... sacrificing the readability of the code. The 88 line code can therefore be considered as a valuable successor to the 99 line code, providing a practical instrument that may help to ease the learning curve for those entering the field of topology optimization. The paper also discusses simple extensions...
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
Directory of Open Access Journals (Sweden)
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.
Optimizing Global Force Management for Special Operations Forces
2016-12-01
FORCE MANAGEMENT FOR SPECIAL OPERATIONS FORCES by Emily A. LaCaille December 2016 Thesis Advisor: Paul L. Ewing Second Reader: Jeffrey...Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget...DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE OPTIMIZING GLOBAL FORCE MANAGEMENT FOR SPECIAL OPERATIONS FORCES 5. FUNDING NUMBERS 6
Energy Technology Data Exchange (ETDEWEB)
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.
Optimal efficiency of a noisy quantum heat engine.
Stefanatos, Dionisis
2014-07-01
In this article we use optimal control to maximize the efficiency of a quantum heat engine executing the Otto cycle in the presence of external noise. We optimize the engine performance for both amplitude and phase noise. In the case of phase damping we additionally show that the ideal performance of a noiseless engine can be retrieved in the adiabatic (long time) limit. The results obtained here are useful in the quest for absolute zero, the design of quantum refrigerators that can cool a physical system to the lowest possible temperature. They can also be applied to the optimal control of a collection of classical harmonic oscillators sharing the same time-dependent frequency and subjected to similar noise mechanisms. Finally, our methodology can be used for the optimization of other interesting thermodynamic processes.
Directory of Open Access Journals (Sweden)
Abdulbaset El Hadi Saad
2017-10-01
Full Text Available Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and “black-box” forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC, Firefly Algorithm (FFA, Cuckoo Search (CS, Bat Algorithm (BA, Flower Pollination Algorithm (FPA and Grey Wolf Optimizer (GWO. These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.
A Global Optimization Approach for Solving Generalized Nonlinear Multiplicative Programming Problem
Directory of Open Access Journals (Sweden)
Lin-Peng Yang
2014-01-01
Full Text Available This paper presents a global optimization algorithm for solving globally the generalized nonlinear multiplicative programming (MP with a nonconvex constraint set. The algorithm uses a branch and bound scheme based on an equivalently reverse convex programming problem. As a result, in the computation procedure the main work is solving a series of linear programs that do not grow in size from iterations to iterations. Further several key strategies are proposed to enhance solution production, and some of them can be used to solve a general reverse convex programming problem. Numerical results show that the computational efficiency is improved obviously by using these strategies.
Potential Global Benefits of Improved Ceiling Fan Energy Efficiency
Energy Technology Data Exchange (ETDEWEB)
Sathaye, Nakul [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Phadke, Amol [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Shah, Nihar [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Letschert, Virginie [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2012-10-31
Ceiling fans contribute significantly to residential electricity consumption, both in an absolute sense and as a proportion of household consumption in many locations, especially in developing countries in warm climates. However, there has been little detailed assessment of the costs and benefits of efficiency improvement options for ceiling fans and the potential resulting electricity consumption and greenhouse gas (GHG) emissions reductions. We analyze the costs and benefits of several options to improve the efficiency of ceiling fans and assess the global potential for electricity savings and GHG emission reductions with more detailed assessments for India, China, and the U.S. We find that ceiling fan efficiency can be cost-effectively improved by at least 50% using commercially available technology. If these efficiency improvements are implemented in all ceiling fans sold by 2020, 70 terrawatt hours per year (TWh/year) could be saved and 25 million metric tons of carbon dioxide (CO2) emissions per year could be avoided, globally. We assess how policies and programs such as standards, labels, and financial incentives can be used to accelerate the adoption of efficient ceiling fans in order to realize this savings potential.
Prioritized efficiency optimization for intensity modulated proton therapy
Müller, Birgit S.; Wilkens, Jan J.
2016-12-01
A high dosimetric quality and short treatment time are major goals in radiotherapy planning. Intensity modulated proton therapy (IMPT) plans obtain dose distributions of great conformity but often result in long delivery times which are typically not incorporated into the optimization process. We present an algorithm to optimize delivery efficiency of IMPT plans while maintaining plan quality, and study the potential trade-offs of these interdependent objectives. The algorithm is based on prioritized optimization, a stepwise approach to implemented objectives. First the quality of the plan is optimized. The second step of the prioritized efficiency optimization (PrEfOpt) routine offers four alternatives for reducing delivery time: minimization of the total spot weight sum (A), maximization of the lowest spot intensity of each energy layer (B), elimination of low-weighted spots (C) or energy layers (D). The trade-off between dosimetric quality (step I) and treatment time (step II) is controlled during the optimization by option-dependent parameters. PrEfOpt was applied to a clinical patient case, and plans for different trade-offs were calculated. Delivery times were simulated for two virtual facilities with constant and variable proton current, i.e. independent and dependent on the optimized spot weight distributions. Delivery times decreased without major degradation of plan quality; absolute time reductions varied with the applied method and facility type. Minimizing the total spot weight sum (A) reduced times by 28% for a similar plan quality at a constant current (changes of minimum dose in the target <1%). For a variable proton current, eliminating low-weighted spots (C) led to remarkably faster delivery (16%). The implementation of an efficiency-optimization step into the optimization process can yield reduced delivery times with similar plan qualities. A potential clinical application of PrEfOpt is the generation of multiple plans with different trade
Energy Technology Data Exchange (ETDEWEB)
Ravi, Kavita [US Department of Energy, Washington, DC (United States); Bennich, Peter [Swedish Energy Agency (Sweden); Cockburn, John [Natural Resources Canada, Ottawa (Canada); Doi, Naoko [Institute of Energy Economics (Japan); Garg, Sandeep [United Nations Development Programme, New York, NY (United States); Garnaik, S.P. [ICF International (India); Holt, Shane [Energy and Tourism, Canberra (Australia); Walker, Mike [Food and Rural Affairs (United Kingdom); Westbrook-Trenholm, Elizabeth [Natural Resources, Canada, Ottawa (Canada). Office of Energy Efficiency; Lising, Anna [Collaborative Labeling and Appliance Standards Program (United States); Pantano, Steve [Collaborative Labeling and Appliance Standards Program (United States); Khare, Amit [Collaborative Labeling and Appliance Standards Program (United States); Park, Won Young [Lawrence Berkeley National Lab., CA (United States)
2013-10-15
The Global Efficiency Medal competition, a cornerstone activity of the Super-efficient Equipment and Appliance Deployment (SEAD) Initiative, is an awards program that encourages the production and sale of super-efficient products. SEAD is a voluntary multinational government collaboration of the Clean Energy Ministerial (CEM). This winner-takes-all competition recognizes products with the best energy efficiency, guides early adopter purchasers towards the most efficient product choices and demonstrates the levels of energy efficiency achievable by commercially available and emerging technologies. The first Global Efficiency Medals were awarded to the most energy-efficient flat panel televisions; an iconic consumer purchase. SEAD Global Efficiency Medals were awarded to televisions that have proven to be substantially more energy efficient than comparable models available at the time of the competition (applications closed in the end of May 2012). The award-winning TVs consume between 33 to 44 percent less energy per 2 unit of screen area than comparable LED-backlit LCD televisions sold in each regional market and 50 to 60 percent less energy than CCFL-backlit LCD TVs. Prior to the launch of this competition, SEAD conducted an unprecedented international round-robin test (RRT) to qualify TV test laboratories to support verification testing for SEAD awards. The RRT resulted in increased test laboratory capacity and expertise around the world and ensured that the test results from participating regional test laboratories could be compared in a fair and transparent fashion. This paper highlights a range of benefits resulting from this first SEAD awards competition and encourages further investigation of the awards concept as a means to promote energy efficiency in other equipment types.
Directory of Open Access Journals (Sweden)
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 non-deterministic 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.
Long-term stability of the Tevatron by verified global optimization
Energy Technology Data Exchange (ETDEWEB)
Berz, Martin [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States)]. E-mail: berz@msu.edu; Makino, Kyoko [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States)]. E-mail: makino@msu.edu; Kim, Youn-Kyung [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States)]. E-mail: kimyounk@msu.edu
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.
Directory of Open Access Journals (Sweden)
Zong-Sheng Wu
2015-01-01
Full Text Available Teaching-learning-based optimization (TLBO algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
An Efficient PageRank Approach for Urban Traffic Optimization
Directory of Open Access Journals (Sweden)
Florin Pop
2012-01-01
to determine optimal decisions for each traffic light, based on the solution given by Larry Page for page ranking in Web environment (Page et al. (1999. Our approach is similar with work presented by Sheng-Chung et al. (2009 and Yousef et al. (2010. We consider that the traffic lights are controlled by servers and a score for each road is computed based on efficient PageRank approach and is used in cost function to determine optimal decisions. We demonstrate that the cumulative contribution of each car in the traffic respects the main constrain of PageRank approach, preserving all the properties of matrix consider in our model.
An efficient, optimized synthesis of fentanyl and related analogs.
Directory of Open Access Journals (Sweden)
Carlos A Valdez
Full Text Available The alternate and optimized syntheses of the parent opioid fentanyl and its analogs are described. The routes presented exhibit high-yielding transformations leading to these powerful analgesics after optimization studies were carried out for each synthetic step. The general three-step strategy produced a panel of four fentanyls in excellent yields (73-78% along with their more commonly encountered hydrochloride and citric acid salts. The following strategy offers the opportunity for the gram-scale, efficient production of this interesting class of opioid alkaloids.
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Directory of Open Access Journals (Sweden)
Younes Saadi
Full Text Available The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive and exploitation (intensive of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be
Ringed Seal Search for Global Optimization via a Sensitive Search Model
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Global structual optimizations of surface systems with a genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Chuang, Feng-Chuan [Iowa State Univ., Ames, IA (United States)
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
An efficient linear programming method for Optimal Transportation
Oberman, Adam M.; Ruan, Yuanlong
2015-01-01
An efficient method for computing solutions to the Optimal Transportation (OT) problem with a wide class of cost functions is presented. The standard linear programming (LP) discretization of the continuous problem becomes intractible for moderate grid sizes. A grid refinement method results in a linear cost algorithm. Weak convergence of solutions is stablished. Barycentric projection of transference plans is used to improve the accuracy of solutions. The method is applied to more general pr...
Optimal power and efficiency of quantum Stirling heat engines
Yin, Yong; Chen, Lingen; Wu, Feng
2017-01-01
A quantum Stirling heat engine model is established in this paper in which imperfect regeneration and heat leakage are considered. A single particle which contained in a one-dimensional infinite potential well is studied, and the system consists of countless replicas. Each particle is confined in its own potential well, whose occupation probabilities can be expressed by the thermal equilibrium Gibbs distributions. Based on the Schrödinger equation, the expressions of power output and efficiency for the engine are obtained. Effects of imperfect regeneration and heat leakage on the optimal performance are discussed. The optimal performance region and the optimal values of important parameters of the engine cycle are obtained. The results obtained can provide some guidelines for the design of a quantum Stirling heat engine.
A global optimization approach to multi-polarity sentiment analysis.
Directory of Open Access Journals (Sweden)
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
Resource efficient gadgets for compiling adiabatic quantum optimization problems
Babbush, Ryan; O'Gorman, Bryan; Aspuru-Guzik, Alán
2013-11-01
We develop a resource efficient method by which the ground-state of an arbitrary k-local, optimization Hamiltonian can be encoded as the ground-state of a (k-1)-local optimization Hamiltonian. This result is important because adiabatic quantum algorithms are often most easily formulated using many-body interactions but experimentally available interactions are generally 2-body. In this context, the efficiency of a reduction gadget is measured by the number of ancilla qubits required as well as the amount of control precision needed to implement the resulting Hamiltonian. First, we optimize methods of applying these gadgets to obtain 2-local Hamiltonians using the least possible number of ancilla qubits. Next, we show a novel reduction gadget which minimizes control precision and a heuristic which uses this gadget to compile 3-local problems with a significant reduction in control precision. Finally, we present numerics which indicate a substantial decrease in the resources required to implement randomly generated, 3-body optimization Hamiltonians when compared to other methods in the literature.
Efficient relaxations for joint chance constrained AC optimal power flow
Energy Technology Data Exchange (ETDEWEB)
Baker, Kyri; Toomey, Bridget
2017-07-01
Evolving power systems with increasing levels of stochasticity call for a need to solve optimal power flow problems with large quantities of random variables. Weather forecasts, electricity prices, and shifting load patterns introduce higher levels of uncertainty and can yield optimization problems that are difficult to solve in an efficient manner. Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality as an upper bound. In this paper, joint chance constraints are used to solve an AC optimal power flow problem while preventing overvoltages in distribution grids under high penetrations of photovoltaic systems. A tighter version of Boole's inequality is derived and used to provide a new upper bound on the joint chance constraint, and simulation results are shown demonstrating the benefit of the proposed upper bound. The new framework allows for a less conservative and more computationally efficient solution to considering joint chance constraints, specifically regarding preventing overvoltages.
Shape optimization for aerodynamic efficiency and low observability
Vinh, Hoang; Van Dam, C. P.; Dwyer, Harry A.
1993-01-01
Field methods based on the finite-difference approximations of the time-domain Maxwell's equations and the potential-flow equation have been developed to solve the multidisciplinary problem of airfoil shaping for aerodynamic efficiency and low radar cross section (RCS). A parametric study and an optimization study employing the two analysis methods are presented to illustrate their combined capabilities. The parametric study shows that for frontal radar illumination, the RCS of an airfoil is independent of the chordwise location of maximum thickness but depends strongly on the maximum thickness, leading-edge radius, and leadingedge shape. In addition, this study shows that the RCS of an airfoil can be reduced without significant effects on its transonic aerodynamic efficiency by reducing the leading-edge radius and/or modifying the shape of the leading edge. The optimization study involves the minimization of wave drag for a non-lifting, symmetrical airfoil with constraints on the airfoil maximum thickness and monostatic RCS. This optimization study shows that the two analysis methods can be used effectively to design aerodynamically efficient airfoils with certain desired RCS characteristics.
The computational optimization of heat exchange efficiency in stack chimneys
Energy Technology Data Exchange (ETDEWEB)
Van Goch, T.A.J.
2012-02-15
For many industrial processes, the chimney is the final step before hot fumes, with high thermal energy content, are discharged into the atmosphere. Tapping into this energy and utilizing it for heating or cooling applications, could improve sustainability, efficiency and/or reduce operational costs. Alternatively, an unused chimney, like the monumental chimney at the Eindhoven University of Technology, could serve as an 'energy channeler' once more; it can enhance free cooling by exploiting the stack effect. This study aims to identify design parameters that influence annual heat exchange in such stack chimney applications and optimize these parameters for specific scenarios to maximize the performance. Performance is defined by annual heat exchange, system efficiency and costs. The energy required for the water pump as compared to the energy exchanged, defines the system efficiency, which is expressed in an efficiency coefficient (EC). This study is an example of applying building performance simulation (BPS) tools for decision support in the early phase of the design process. In this study, BPS tools are used to provide design guidance, performance evaluation and optimization. A general method for optimization of simulation models will be studied, and applied in two case studies with different applications (heating/cooling), namely; (1) CERES case: 'Eindhoven University of Technology monumental stack chimney equipped with a heat exchanger, rejects heat to load the cold source of the aquifer system on the campus of the university and/or provides free cooling to the CERES building'; and (2) Industrial case: 'Heat exchanger in an industrial stack chimney, which recoups heat for use in e.g. absorption cooling'. The main research question, addressing the concerns of both cases, is expressed as follows: 'what is the optimal set of design parameters so heat exchange in stack chimneys is optimized annually for the cases in which a
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
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.
Optimized tapered dipole nanoantenna as efficient energy harvester.
El-Toukhy, Youssef M; Hussein, Mohamed; Hameed, Mohamed Farhat O; Heikal, A M; Abd-Elrazzak, M M; Obayya, S S A
2016-07-11
In this paper, a novel design of tapered dipole nanoantenna is introduced and numerically analyzed for energy harvesting applications. The proposed design consists of three steps tapered dipole nanoantenna with rectangular shape. Full systematic analysis is carried out where the antenna impedance, return loss, harvesting efficiency and field confinement are calculated using 3D finite element frequency domain method (3D-FEFD). The structure geometrical parameters are optimized using particle swarm algorithm (PSO) to improve the harvesting efficiency and reduce the return loss at wavelength of 500 nm. A harvesting efficiency of 55.3% is achieved which is higher than that of conventional dipole counterpart by 29%. This enhancement is attributed to the high field confinement in the dipole gap as a result of multiple tips created in the nanoantenna design. Furthermore, the antenna input impedance is tuned to match a wide range of fabricated diode based upon the multi-resonance characteristic of the proposed structure.
Simulated Annealing-Based Krill Herd Algorithm for Global Optimization
Directory of Open Access Journals (Sweden)
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.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Analyzing demand-side efficiency in global health: an application to maternal care in Vietnam.
Radin, Elizabeth; Ariana, Proochista; Broekel, Tom; Tran, Toan Khanh
2016-11-01
This article investigates demand-side efficiency in global health-or the efficiency with which health system users convert public health resources into health outcomes. We introduce and explain the concept of demand-side efficiency as well as quantitative methods to empirically estimate it. Using a robust nonparametric form of technical efficiency analysis, we estimate demand side efficiency and its social determinants. We pilot these methods looking at how efficiently pregnant women in Northern Vietnam convert public health resources into appropriate maternal care as defined by national policy. We find that women who live in non-mountainous geographies, who are formally employed, who are pregnant with a boy and who are ethnic minorities are all more likely to be efficient at achieving appropriate care. We find no significant association between wealth or education and efficiency. Our results suggest that, in the Vietnamese context, women who are the most likely to achieve appropriate maternal care, are not necessarily the most likely to do so efficiently. Women who live in non-mountainous geographies and who are formally employed are both more likely to achieve appropriate care and to do so efficiently. Yet ethnic minority women, who do not systematically achieve better care, are more likely to be efficient or to achieve better care when compared with those with the same endowment of public health resources. On the methodological level, the pilot highlights that this approach can provide useful information for policy by identifying which groups of people are more and less likely to be efficient. By understanding which groups are more likely to be efficient-and in turn how and why-it may be possible to devise policies to promote the drivers of, or conversely address the constraints to, optimizing demand-side efficiency. © The Author 2016. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights
Zhang, Yong-Feng; Chiang, Hsiao-Dong
2017-09-01
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.
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.
Carbonic Anhydrase: An Efficient Enzyme with Possible Global Implications
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Christopher D. Boone
2013-01-01
Full Text Available As the global atmospheric emissions of carbon dioxide (CO2 and other greenhouse gases continue to grow to record-setting levels, so do the demands for an efficient and inexpensive carbon sequestration system. Concurrently, the first-world dependence on crude oil and natural gas provokes concerns for long-term availability and emphasizes the need for alternative fuel sources. At the forefront of both of these research areas are a family of enzymes known as the carbonic anhydrases (CAs, which reversibly catalyze the hydration of CO2 into bicarbonate. CAs are among the fastest enzymes known, which have a maximum catalytic efficiency approaching the diffusion limit of 108 M−1s−1. As such, CAs are being utilized in various industrial and research settings to help lower CO2 atmospheric emissions and promote biofuel production. This review will highlight some of the recent accomplishments in these areas along with a discussion on their current limitations.
Global efficiency of local immunization on complex networks
Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.
2013-07-01
Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.
Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization.
Ciornei, Irina; Kyriakides, Elias
2012-02-01
Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimization algorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions.
Identification of metabolic system parameters using global optimization methods
Directory of Open Access Journals (Sweden)
Gatzke Edward P
2006-01-01
Full Text Available Abstract Background The problem of estimating the parameters of dynamic models of complex biological systems from time series data is becoming increasingly important. Methods and results Particular consideration is given to metabolic systems that are formulated as Generalized Mass Action (GMA models. The estimation problem is posed as a global optimization task, for which novel techniques can be applied to determine the best set of parameter values given the measured responses of the biological system. The challenge is that this task is nonconvex. Nonetheless, deterministic optimization techniques can be used to find a global solution that best reconciles the model parameters and measurements. Specifically, the paper employs branch-and-bound principles to identify the best set of model parameters from observed time course data and illustrates this method with an existing model of the fermentation pathway in Saccharomyces cerevisiae. This is a relatively simple yet representative system with five dependent states and a total of 19 unknown parameters of which the values are to be determined. Conclusion The efficacy of the branch-and-reduce algorithm is illustrated by the S. cerevisiae example. The method described in this paper is likely to be widely applicable in the dynamic modeling of metabolic networks.
New Algorithms for Global Optimization and Reaction Path Determination.
Weber, D; Bellinger, D; Engels, B
2016-01-01
We present new schemes to improve the convergence of an important global optimization problem and to determine reaction pathways (RPs) between identified minima. Those methods have been implemented into the CAST program (Conformational Analysis and Search Tool). The first part of this chapter shows how to improve convergence of the Monte Carlo with minimization (MCM, also known as Basin Hopping) method when applied to optimize water clusters or aqueous solvation shells using a simple model. Since the random movement on the potential energy surface (PES) is an integral part of MCM, we propose to employ a hydrogen bonding-based algorithm for its improvement. We show comparisons of the results obtained for random dihedral and for the proposed random, rigid-body water molecule movement, giving evidence that a specific adaption of the distortion process greatly improves the convergence of the method. The second part is about the determination of RPs in clusters between conformational arrangements and for reactions. Besides standard approaches like the nudged elastic band method, we want to focus on a new algorithm developed especially for global reaction path search called Pathopt. We started with argon clusters, a typical benchmark system, which possess a flat PES, then stepwise increase the magnitude and directionality of interactions. Therefore, we calculated pathways for a water cluster and characterize them by frequency calculations. Within our calculations, we were able to show that beneath local pathways also additional pathways can be found which possess additional features. © 2016 Elsevier Inc. All rights reserved.
Global Potential of Energy Efficiency Standards and Labeling Programs
Energy Technology Data Exchange (ETDEWEB)
McNeil, Michael A; McNeil, Michael A.; Letschert, Virginie; de la Rue du Can, Stephane
2008-06-15
This report estimates the global potential reductions in greenhouse gas emissions by 2030 for energy efficiency improvements associated with equipment (appliances, lighting, and HVAC) in buildings by means of energy efficiency standards and labels (EES&L). A consensus has emerged among the world's scientists and many corporate and political leaders regarding the need to address the threat of climate change through emissions mitigation and adaptation. A further consensus has emerged that a central component of these strategies must be focused around energy, which is the primary generator of greenhouse gas emissions. Two important questions result from this consensus: 'what kinds of policies encourage the appropriate transformation to energy efficiency' and 'how much impact can these policies have'? This report aims to contribute to the dialogue surrounding these issues by considering the potential impacts of a single policy type, applied on a global scale. The policy addressed in this report is Energy Efficient Standards and Labeling (EES&L) for energy-consuming equipment, which has now been implemented in over 60 countries. Mandatory energy performance standards are important because they contribute positively to a nation's economy and provide relative certainty about the outcome (both timing and magnitudes). Labels also contribute positively to a nation's economy and importantly increase the awareness of the energy-consuming public. Other policies not analyzed here (utility incentives, tax credits) are complimentary to standards and labels and also contribute in significant ways to reducing greenhouse gas emissions. We believe the analysis reported here to be the first systematic attempt to evaluate the potential of savings from EES&L for all countries and for such a large set of products. The goal of the analysis is to provide an assessment that is sufficiently well-quantified and accurate to allow comparison and integration
Optimizing Cubature for Efficient Integration of Subspace Deformations.
An, Steven S; Kim, Theodore; James, Doug L
2009-12-01
We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional quadrature) optimized for efficient integration of force densities associated with particular subspace deformations, particular materials, and particular geometric domains. We support generic subspace deformation kinematics, and nonlinear hyperelastic materials. For an r-dimensional deformation subspace with O(r) cubature points, our method is able to evaluate subspace forces at O(r(2)) cost. We also describe composite cubature rules for runtime error estimation. Results are provided for various subspace deformation models, several hyperelastic materials (St.Venant-Kirchhoff, Mooney-Rivlin, Arruda-Boyce), and multimodal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration. CR CATEGORIES: I.6.8 [Simulation and Modeling]: Types of Simulation-Animation, I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Physically based modeling G.1.4 [Mathematics of Computing]: Numerical Analysis-Quadrature and Numerical Differentiation.
Optimizing the efficiency of femtosecond-laser-written holograms
DEFF Research Database (Denmark)
Wædegaard, Kristian Juncher; Hansen, Henrik Dueholm; Balling, Peter
2013-01-01
Computer-generated binary holograms are written on a polished copper surface using single 800-nm, 120-fs pulses from a 1-kHz-repetition-rate laser system. The hologram efficiency (i.e. the power in the holographic reconstructed image relative to the incoming laser power) is investigated for diffe......Computer-generated binary holograms are written on a polished copper surface using single 800-nm, 120-fs pulses from a 1-kHz-repetition-rate laser system. The hologram efficiency (i.e. the power in the holographic reconstructed image relative to the incoming laser power) is investigated...... for different laser-structuring parameters. Theoretical diffraction grating efficiencies for a binary amplitude grating show good agreement with the experimental measurements for diameters of the laser-formed holes below the pitch. Modelling based on straightforward geometrical arguments is used to find...... the optimal hole size. For a coverage (i.e. relative laser-structured area) of ∼43 %, the efficiency reaches ∼10 %, which corresponds to a relative power transferred to one reconstructed image of ∼20 %. The efficiency as a function of pitch (for fixed coverage) is fairly constant from 2 to 6 μm....
Locomotion Efficiency Optimization of Biologically Inspired Snake Robots
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Eleni Kelasidi
2018-01-01
Full Text Available Snake robots constitute bio-inspired solutions that have been studied due to their ability to move in challenging environments where other types of robots, such as wheeled or legged robots, usually fail. In this paper, we consider both land-based and swimming snake robots. One of the principal concerns of the bio-inspired snake robots is to increase the motion efficiency in terms of the forward speed by improving the locomotion methods. Furthermore, energy efficiency becomes a crucial challenge for this type of robots due to the importance of long-term autonomy of these systems. In this paper, we take into account both the minimization of the power consumption and the maximization of the achieved forward velocity in order to investigate the optimal gait parameters for bio-inspired snake robots using lateral undulation and eel-like motion patterns. We furthermore consider possible negative work effects in the calculation of average power consumption of underwater snake robots. To solve the multi-objective optimization problem, we propose transforming the two objective functions into a single one using a weighted-sum method. For different set of weight factors, Particle Swarm Optimization is applied and a set of optimal points is consequently obtained. Pareto fronts or trade-off curves are illustrated for both land-based and swimming snake robots with different numbers of links. Pareto fronts represent trade-offs between the objective functions. For example, how increasing the forward velocity results in increasing power consumption. Therefore, these curves are a very useful tool for the control and design of snake robots. The trade-off curve thus constitutes a very useful tool for both the control and design of bio-inspired snake robots. In particular, the operators or designers of bio-inspired snake robots can choose a Pareto optimal point based on the trade-off curve, given the preferred number of links on the robot. The optimal gait parameters
Optimization of light use efficiency for biofuel production in algae.
Simionato, Diana; Basso, Stefania; Giacometti, Giorgio M; Morosinotto, Tomas
2013-12-01
A major challenge for next decades is development of competitive renewable energy sources, highly needed to compensate fossil fuels reserves and reduce greenhouse gas emissions. Among different possibilities, which are currently under investigation, there is the exploitation of unicellular algae for production of biofuels and biodiesel in particular. Some algae species have the ability of accumulating large amount of lipids within their cells which can be exploited as feedstock for the production of biodiesel. Strong research efforts are however still needed to fulfill this potential and optimize cultivation systems and biomass harvesting. Light provides the energy supporting algae growth and available radiation must be exploited with the highest possible efficiency to optimize productivity and make microalgae large scale cultivation energetically and economically sustainable. Investigation of the molecular bases influencing light use efficiency is thus seminal for the success of this biotechnology. In this work factors influencing light use efficiency in algal biomass production are reviewed, focusing on how algae genetic engineering and control of light environment within photobioreactors can improve the productivity of large scale cultivation systems. © 2013.
Utilization of Flexible Airspace Structure in Flight Efficiency Optimization
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Tomislav Mihetec
2013-04-01
Full Text Available With increasing air traffic demand in the Pan-European airspace there is a need for optimizing the use of the airspace structure (civilian and military in a manner that would satisfy the requirements of civil and military users. In the area of Europe with the highest levels of air traffic (Core area 32% of the volume of airspace above FL 195 is shared by both civil and military users. Until the introduction of the concept of flexible use of airspace, flexible airspace structures were 24 hours per day unavailable for commercial air transport. Flexible use of airspace concept provides a substantial level of dynamic airspace management by the usage of conditional routes. This paper analyses underutilization of resources, flexible airspace structures in the Pan-European airspace, especially in the south-eastern part of the traffic flows (East South Axis, reducing the efficiency of flight operations, as result of delegating the flexible structures to military users. Based on previous analysis, utilization model for flexible use of airspace is developed (scenarios with defined airspace structure. The model is based on the temporal, vertical, and modular airspace sectorisation parameters in order to optimize flight efficiency. The presented model brings significant improvement in flight efficiency (in terms of reduced flight distance for air carriers that planned to fly through the selected flexible airspace structure (LI_RST-49.
Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments
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Yen-Sheng Chen
2015-01-01
Full Text Available This paper presents an extremely efficient method for antenna design and optimization. Traditionally, antenna optimization relies on nature-inspired heuristic algorithms, which are time-consuming due to their blind-search nature. In contrast, design of experiments (DOE uses a completely different framework from heuristic algorithms, reducing the design cycle by formulating the surrogates of a design problem. However, the number of required simulations grows exponentially if a full factorial design is used. In this paper, a much more efficient technique is presented to achieve substantial time savings. By using orthogonal fractional experiments, only a small subset of the full factorial design is required, yet the resultant response surface models are still effective. The capability of orthogonal fractional experiments is demonstrated through three examples, including two tag antennas for radio-frequency identification (RFID applications and one internal antenna for long-term-evolution (LTE handheld devices. In these examples, orthogonal fractional experiments greatly improve the efficiency of DOE, thereby facilitating the antenna design with less simulation runs.
Efficient freeform lens optimization for computational caustic displays.
Damberg, Gerwin; Heidrich, Wolfgang
2015-04-20
Phase-only light modulation shows great promise for many imaging applications, including future projection displays. While images can be formed efficiently by avoiding per-pixel attenuation of light most projection efforts utilizing phase-only modulators are based on holographic principles which rely on interference of coherent laser light and a Fourier lens. Limitations of this type of an approach include scaling to higher power as well as visible artifacts such as speckle and image noise. We propose an alternative approach: operating the spatial phase modulator with broadband illumination by treating it as a programmable freeform lens. We describe a simple optimization approach for generating phase modulation patterns or freeform lenses that, when illuminated by a collimated, broadband light source, will project a pre-defined caustic image on a designated image plane. The optimization procedure is based on a simple geometric optics image formation model and can be implemented computationally efficient. We perform simulations and show early experimental results that suggest that the implementation on a phase-only modulator can create structured light fields suitable, for example, for efficient illumination of a spatial light modulator (SLM) within a traditional projector. In an alternative application, the algorithm provides a fast way to compute geometries for static, freeform lens manufacturing.
Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm
Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel
2011-09-01
The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
Spatiotemporal radiotherapy planning using a global optimization approach.
Adibi, Ali; Salari, Ehsan
2018-01-12
This paper aims at quantifying the extent of potential therapeutic gain, measured using biologically effective dose (BED), that can be achieved by altering the radiation dose distribution over treatment sessions in fractionated radiotherapy. To that end, a spatiotemporally integrated planning approach is developed, where the spatial and temporal dose modulations are optimized simultaneously. The concept of equivalent uniform BED (EUBED) is used to quantify and compare the clinical quality of spatiotemporally heterogeneous dose distributions in target and critical structures. This gives rise to a large-scale non-convex treatment-plan optimization problem, which is solved using global optimization techniques. The proposed spatiotemporal planning approach is tested on two stylized cancer cases resembling two different tumor sites and sensitivity analysis is performed for radio-biological and EUBED parameters. Numerical results validate that spatiotemporal plans are capable of delivering a larger BED to the target volume without increasing the BED in critical structures compared to conventional time-invariant plans. In particular, this additional gain is attributed to the irradiation of different regions of the target volume at different treatment sessions. Additionally, the trade-off between the potential therapeutic gain and the number of distinct dose distributions is quantified, which suggests a diminishing marginal gain as the number of dose distributions increases. © 2018 Institute of Physics and Engineering in Medicine.
Optimal resource allocation for efficient transport on complex networks
Gong, Xiaofeng; Kun, Li; Lai, C.-H.
2008-07-01
The problem of efficient transport on a complex network is studied in this paper. We find that there exists an optimal way to allocate resources for information processing on each node to achieve the best transport capacity of the network, or the largest input information rate which does not cause jamming in network traffic, provided that the network structure and routing strategy are given. More interestingly, this achievable network capacity limit is closely related to the topological structure of the network, and is actually inversely proportional to the average distance of the network, measured according to the same routing rule.
Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry
2015-12-22
AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology, Biochemistry Warren Powell TRUSTEES OF PRINCETON... Biochemistry 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-12-1-0200 5c. PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S) Warren Powell 5d. PROJECT NUMBER 5e...scientists. 15. SUBJECT TERMS Biochemistry 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF 19a. NAME OF RESPONSIBLE PERSON Warren
A new efficient mixture screening design for optimization of media.
Rispoli, Fred; Shah, Vishal
2009-01-01
Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett-Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high-order interaction among ingredients. (c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.
Opportunities of Optimization in Administrative Structures for Efficient Management
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Venelin Terziev
2017-12-01
Full Text Available Current paper presents studies on the administrative structures in order to optimize the activities and the overall management through the example of the Bulgarian Commission for Protection against Discrimination. It aims at establishing duplicate functions in the organization under study. The main tasks in the analysis are related to the display of the basic findings and conclusions for the strongest sides and the fields for improvement regarding the relevance, the effectiveness and the efficiency of the administration of the Commission for Protection against Discrimination in Bulgaria. The following areas are thoroughly and critically analyzed: relevance of the functions and efficiency of the activity. As a result of the study a Strategy for Organizational Development and a Training Plan have been drafted.
Global optimal hybrid geometric active contour for automated lung segmentation on CT images.
Zhang, Weihang; Wang, Xue; Zhang, Pengbo; Chen, Junfeng
2017-12-01
Lung segmentation on thoracic CT images plays an important role in early detection, diagnosis and 3D visualization of lung cancer. The segmentation accuracy, stability, and efficiency of serial CT scans have a significant impact on the performance of computer-aided detection. This paper proposes a global optimal hybrid geometric active contour model for automated lung segmentation on CT images. Firstly, the combination of global region and edge information leads to high segmentation accuracy in lung regions with weak boundaries or narrow bands. Secondly, due to the global optimality of energy functional, the proposed model is robust to the initial position of level set function and requires fewer iterations. Thus, the stability and efficiency of lung segmentation on serial CT slices can be greatly improved by taking advantage of the information between adjacent slices. In addition, to achieve the whole process of automated segmentation for lung cancer, two assistant algorithms based on prior shape and anatomical knowledge are proposed. The algorithms not only automatically separate the left and right lungs, but also include juxta-pleural tumors into the segmentation result. The proposed method was quantitatively validated on subjects from the publicly available LIDC-IDRI and our own data sets. Exhaustive experimental results demonstrate the superiority and competency of our method, especially compared with the typical edge-based geometric active contour model. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
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%.
An Efficient Optimization Method for Solving Unsupervised Data Classification Problems
Directory of Open Access Journals (Sweden)
Parvaneh Shabanzadeh
2015-01-01
Full Text Available Unsupervised data classification (or clustering analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
GLOBAL DESIGN OPTIMIZATION OF A REFRIGERATION SYSTEM USING A GENETIC ALGORITHM
Directory of Open Access Journals (Sweden)
L. Govindarajan
2012-01-01
Full Text Available
ENGLISH ABSTRACT: The optimal design of industrial three-stage refrigeration systems to minimize total production cost has been effectively implemented using an genetic algorithm (GA, which is an efficient alternative for conventional search algorithms. In this article the global optimum design parameters of a refrigeration system obtained by using GA is compared with the Nelder-Mead simplex search algorithm. The results prove that global design optimization using GA is more robust and simple, as it requires no initial guess values of design variables. Hence the proposed technique is well suited for designing a variety of industrially important systems.
AFRIKAANSE OPSOMMING: Die optimum ontwerp van 'n driestadium-vriessisteem om totale produksiekoste te beperk, word doeltreffend met behulp van 'n genetiese algoritme (GA as alternatief vir konvensionele soekalgoritmes in werking gestel. Die navorsing is daarop toegespits om die globale optimum ontwerpparameters van die GA met die Nelder-Mead Simpleks-soekalgoritme te vergelyk. Die resultate toon dat die GA se globale optimum ontwerp robuust en eenvoudig is aangesien geskatte aanvangswaardes vir ontwerpveranderlikes nie benodig word nie. Derhalwe is die GA-tegniek besonder geskik vir die ontwerp van nywerheidsisteme van uiteenlopende aard.
Directory of Open Access Journals (Sweden)
Pradeep Jangir
2017-04-01
Full Text Available Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid Particle Swarm Optimization (PSO-Multi verse Optimizer (MVO is exercised on some unconstraint benchmark test functions and the most common problem of the modern power system named Optimal Reactive Power Dispatch (ORPD is optimized using the novel hybrid meta-heuristic optimization algorithm Particle Swarm Optimization-Multi Verse Optimizer (HPSO-MVO method. Hybrid PSO-MVO is combination of PSO used for exploitation phase and MVO for exploration phase in uncertain environment. Position and Speed of particle is modernised according to location of universes in each iteration. The hybrid PSO-MVO method has a fast convergence rate due to use of roulette wheel selection method. For the ORPD solution, standard IEEE-30 bus test system is used. The hybrid PSO-MVO method is implemented to solve the proposed problem. The problems considered in the ORPD are fuel cost reduction, Voltage profile improvement, Voltage stability enhancement, Active power loss minimization and Reactive power loss minimization. The results obtained with hybrid PSO-MVO method is compared with other techniques such as Particle Swarm Optimization (PSO and Multi Verse Optimizer (MVO. Analysis of competitive results obtained from HPSO-MVO validates its effectiveness compare to standard PSO and MVO algorithm.
Energy Efficiency - Spectral Efficiency Trade-off: A Multiobjective Optimization Approach
Amin, Osama
2015-04-23
In this paper, we consider the resource allocation problem for energy efficiency (EE) - spectral efficiency (SE) trade-off. Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than the conventional approaches and more convenient to realistic communication applications and scenarios. We prove that the multiobjective optimization of the EE and SE is equivalent to a simple problem that maximizes the achievable rate/SE and minimizes the total power consumption. Then we apply the generalized framework of the resource allocation for the EE-SE trade-off to optimally allocate the subcarriers’ power for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.
A practical globalization of one-shot optimization for optimal design of tokamak divertors
Energy Technology Data Exchange (ETDEWEB)
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.
Global variation of carbon use efficiency in terrestrial ecosystems
Tang, Xiaolu; Carvalhais, Nuno; Moura, Catarina; Reichstein, Markus
2017-04-01
Carbon use efficiency (CUE), defined as the ratio between net primary production (NPP) and gross primary production (GPP), is an emergent property of vegetation that describes its effectiveness in storing carbon (C) and is of significance for understanding C biosphere-atmosphere exchange dynamics. A constant CUE value of 0.5 has been widely used in terrestrial C-cycle models, such as the Carnegie-Ames-Stanford-Approach model, or the Marine Biological Laboratory/Soil Plant-Atmosphere Canopy Model, for regional or global modeling purposes. However, increasing evidence argues that CUE is not constant, but varies with ecosystem types, site fertility, climate, site management and forest age. Hence, the assumption of a constant CUE of 0.5 can produce great uncertainty in estimating global carbon dynamics between terrestrial ecosystems and the atmosphere. Here, in order to analyze the global variations in CUE and understand how CUE varies with environmental variables, a global database was constructed based on published data for crops, forests, grasslands, wetlands and tundra ecosystems. In addition to CUE data, were also collected: GPP and NPP; site variables (e.g. climate zone, site management and plant function type); climate variables (e.g. temperature and precipitation); additional carbon fluxes (e.g. soil respiration, autotrophic respiration and heterotrophic respiration); and carbon pools (e.g. stem, leaf and root biomass). Different climate metrics were derived to diagnose seasonal temperature (mean annual temperature, MAT, and maximum temperature, Tmax) and water availability proxies (mean annual precipitation, MAP, and Palmer Drought Severity Index), in order to improve the local representation of environmental variables. Additionally were also included vegetation phenology dynamics as observed by different vegetation indices from the MODIS satellite. The mean CUE of all terrestrial ecosystems was 0.45, 10% lower than the previous assumed constant CUE of 0
Optimizing global liver function in radiation therapy treatment planning.
Wu, Victor W; Epelman, Marina A; Wang, Hesheng; Edwin Romeijn, H; Feng, Mary; Cao, Yue; Ten Haken, Randall K; Matuszak, Martha M
2016-09-07
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 ([Formula: see text]) (conventional '[Formula: see text] model'), the so-called perfusion-weighted [Formula: see text] ([Formula: see text]) (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 [Formula: see text], fEUD, and GLF plans delivering the same target [Formula: see text] 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 [Formula: see text] more liver function than the fEUD ([Formula: see text]) plan does in 2D cases, and up to [Formula: see text] in 3D cases. The GLF and fEUD plans worsen in [Formula: see text] 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
Urban energy efficiency: a breakthrough vs. the global crisis
Directory of Open Access Journals (Sweden)
Roberto Pagani
2011-04-01
Full Text Available The environmental design and its complex multi-disciplinary consequences are central in the thinking of the responsibilities, which the Institutes of Knowledge are based on. It is no longer a battle to the forefront, just for the explorers. It is an acquired and consolidated culture, but from an operational standpoint there are many areas of innovation, development, affirmation still to develop. The article focuses on four central aspects of the new culture of energy efficiency in urban systems: the new global/local decision making, the sustainability in urban processes, the new skills for research and development, the growing interest of industries in innovative urban technologies. It provides the main trends in Europe and a vision of a possible exit from the complex socio-economic situation of today: a culture of complexity and energy innovation as factors of development.
Game Theory-Inspired Evolutionary Algorithm for Global Optimization
Directory of Open Access Journals (Sweden)
Guanci Yang
2017-09-01
Full Text Available Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA. A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy.
Optimal efficiency of self-assembling light-harvesting arrays.
Kim, Ji-Hyun; Cao, Jianshu
2010-12-16
Using a classical master equation that describes energy transfer over a given lattice, we explore how energy transfer efficiency along with the photon capturing ability depends on network connectivity, on transfer rates, and on volume fractions-the numbers and relative ratio of fluorescence chromophore components, e.g., donor (D), acceptor (A), and bridge (B) chromophores. For a one-dimensional AD array, the exact analytical expression (derived in Appendix A) for efficiency shows a steep increase with a D-to-A transfer rate when a spontaneous decay is sufficiently slow. This result implies that the introduction of B chromophores can be a useful method for improving efficiency for a two-component AD system with inefficient D-to-A transfer and slow spontaneous decay. Analysis of this one-dimensional system can be extended to higher-dimensional systems with chromophores arranged in structures such as a helical or stacked-disk rod, which models the self-assembling monomers of the tobacco mosaic virus coat protein. For the stacked-disk rod, we observe the following: (1) With spacings between sites fixed, a staggered conformation is more efficient than an eclipsed conformation. (2) For a given ratio of A and D chromophores, the uniform distribution of acceptors that minimizes the mean first passage time to acceptors is a key point to designing the optimal network for a donor-acceptor system with a relatively small D-to-A transfer rate. (3) For a three-component ABD system with a large B-to-A transfer rate, a key design strategy is to increase the number of the pathways in accordance with the directional energy flow from D to B to A chromophores. These conclusions are consistent with the experimental findings reported by Francis, Fleming, and their co-workers and suggest that synthetic architectures of self-assembling supermolecules and the distributions of AD or ABD chromophore components can be optimized for efficient light-harvesting energy transfer.
Efficient Configuration Space Construction and Optimization for Motion Planning
Directory of Open Access Journals (Sweden)
Jia Pan
2015-03-01
Full Text Available The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces: how to efficiently compute an approximate representation of high-dimensional configuration spaces; and how to efficiently perform geometric proximity and motion planning queries in high-dimensional configuration spaces. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning.
Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review
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M. K. Sakharov
2015-01-01
memetic algorithms.The results show that, despite the successful application of various memetic algorithms in various applications, there are a large number of directions for their modification and study. These are, for example, a more detailed study of self-adaptation methods of memetic algorithms, development of methods to assess the meme ability to refine the solution at a particular stage of the working algorithm. Besides the problem of selecting memes, there are also problems associated with the duration of local search. The solution is a balance of computing time between the local and global search. For a fixed computing time it allows to allocate time between global and local search in the solution of the optimization problem, which will increase the efficiency of the algorithms.
Energy Technology Data Exchange (ETDEWEB)
Gerke, Brian F; McNeil, Michael A; Tu, Thomas; Xu, Feiyang
2017-09-06
A major barrier to effective appliance efficiency program design and evaluation is a lack of data for determination of market baselines and cost-effective energy savings potential. The data gap is particularly acute in developing countries, which may have the greatest savings potential per unit GDP. To address this need, we are developing the International Database of Efficient Appliances (IDEA), which automatically compiles data from a wide variety of online sources to create a unified repository of information on efficiency, price, and features for a wide range of energy-consuming products across global markets. This paper summarizes the database framework and demonstrates the power of IDEA as a resource for appliance efficiency research and policy development. Using IDEA data for refrigerators in China and India, we develop robust cost-effectiveness indicators that allow rapid determination of savings potential within each market, as well as comparison of that potential across markets and appliance types. We discuss implications for future energy efficiency policy development.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2016-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Tsai, Ko-Fan; Chu, Shu-Chun
2016-09-19
The one-time ray-tracing optimization method is a fast way to design LED illumination systems [Opt. Express22, 5357 (2014)10.1364/OE.22.005357]. The method optimizes the performance of LED illumination systems by modifying the LEDs' luminous intensity distribution curve (LIDC) with a freeform lens, instead of modifying the illumination system structure. In finding the LEDs' LIDC for optimizing the illumination system's performance, the LEDs' LIDC found by means of a general gradient descent method can be trapped in a local solution. This study develops a matrix operation method to directly find the global solution of the LEDs' LIDC for the optimization of the illumination system's performance for any initial design of an illumination system structure. As compared with the gradient descent method, using the proposed characteristic matrix operation method to find the best LEDs' LIDC reduces the cost in time by several orders of magnitude. The proposed characteristic matrix operation method ensures that the one-time ray-tracing optimization method is an efficient and reliable method for designing LED illumination systems.
Assisted closed-loop optimization of SSVEP-BCI efficiency
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Jacobo eFernandez-Vargas
2013-02-01
Full Text Available We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain computer interfaces (BCI based on steady state visually evoked potentials (SSVEP. In traditional paradigms, the control over the BCI-performance completely depends on the subjects’ ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (i a closed-loop search for the best set of SSVEP flicker frequencies and (ii feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects’ state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g. under the new protocol, baseline resting state EEG measures predict subjects’ BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g. as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.
PID Controller Design Based on Global Optimization Technique with Additional Constraints
Ozana, Stepan; Docekal, Tomas
2016-05-01
This paper deals with design of PID controller with the use of methods of global optimization implemented in Matlab environment and Optimization Toolbox. It is based on minimization of a chosen integral criterion with respect to additional requirements on control quality such as overshoot, phase margin and limits for manipulated value. The objective function also respects user-defined weigh coefficients for its particular terms for a different penalization of individual requirements that often clash each other such as for example overshoot and phase margin. The described solution is designated for continuous linear time-invariant static systems up to 4th order and thus efficient for the most of real control processes in practice.
Eukaryotic transcriptomics in silico: Optimizing cDNA-AFLP efficiency
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Wüst Christian
2009-11-01
Full Text Available Abstract Background Complementary-DNA based amplified fragment length polymorphism (cDNA-AFLP is a commonly used tool for assessing the genetic regulation of traits through the correlation of trait expression with cDNA expression profiles. In spite of the frequent application of this method, studies on the optimization of the cDNA-AFLP assay design are rare and have typically been taxonomically restricted. Here, we model cDNA-AFLPs on all 92 eukaryotic species for which cDNA pools are currently available, using all combinations of eight restriction enzymes standard in cDNA-AFLP screens. Results In silco simulations reveal that cDNA pool coverage is largely determined by the choice of individual restriction enzymes and that, through the choice of optimal enzyme combinations, coverage can be increased from Conclusion The insights gained from in silico screening of cDNA-AFLPs from a broad sampling of eukaryotes provide a set of guidelines that should help to substantially increase the efficiency of future cDNA-AFLP experiments in eukaryotes. In silico simulations also suggest a novel use of cDNA-AFLP screens to determine the number of transcripts expressed in a target tissue, an application that should be invaluable as next-generation sequencing technologies are adapted for differential display.
Video coding using arbitrarily shaped block partitions in globally optimal perspective
National Research Council Canada - National Science Library
Paul, Manoranjan; Murshed, Manzur
2011-01-01
.... But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther...
Abdelhady, Amr, M.
2016-01-06
Multi-teir hetrogeneous networks have become an essential constituent for next generation cellular networks. Meanwhile, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for the recently proposed two-teir architecture known as Phantom cellular networks. The optimization framework includes both EE and SE, where we propose an algorithm that computes the SE and EE resource allocation for Phantom cellular networks. Then, we compare the performance of both design strategies versus the number of users, and the ration of Phantom cellresource blocks to the total number or resource blocks. We aim to investigate the effect of some system parameters to acheive improved SE or EE performance at a non-significant loss in EE or SE performance, respectively. It was found that the system parameters can be tuned so that the EE solution does not yield a significant loss in the SE performance.
Automatic Construction and Global Optimization of a Multisentiment Lexicon
Directory of Open Access Journals (Sweden)
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.
Toward an optimal global stem cell donor recruitment strategy.
Schmidt, Alexander H; Sauter, Jürgen; Pingel, Julia; Ehninger, Gerhard
2014-01-01
Population-specific matching probabilities (MP) are a key parameter to assess the benefits of unrelated stem cell donor registries and the need for further donor recruitment efforts. In this study, we describe a general framework for MP estimations of specific and mixed patient populations under consideration of international stem cell donor exchange. Calculations were based on population-specific 4-locus (HLA-A, -B, -C, -DRB1) high-resolution haplotype frequencies (HF) of up to 21 populations. In various scenarios, we calculated several quantities of high practical relevance, including the maximal MP that can be reached by recruiting a fixed number of donors, the corresponding optimal composition by population of new registrants, and the minimal number of donors who need to be recruited to reach a defined MP. Starting at current donor numbers, the largest MP increases due to n = 500,000 additional same-population donors were observed for patients from Bosnia-Herzegovina (+0.25), Greece (+0.21) and Romania (+0.20). Especially small MP increases occurred for European Americans (+0.004), Germans (+0.01) and Hispanic Americans (+0.01). Due to the large Chinese population, the optimal distribution of n = 5,000,000 new donors worldwide included 3.9 million Chinese donors. As a general result of our calculations, we observed a need for same-population donor recruitment in order to increase population-specific MP efficiently. This result was robust despite limitations of our input data, including the use of HF derived from relatively small samples ranging from n = 1028 (Bosnia-Herzegovina) to n = 33,083 (Turkey) individuals. National strategies that neglect domestic donor recruitment should therefore be critically re-assessed, especially if only few donors have been recruited so far.
Toward an optimal global stem cell donor recruitment strategy.
Directory of Open Access Journals (Sweden)
Alexander H Schmidt
Full Text Available Population-specific matching probabilities (MP are a key parameter to assess the benefits of unrelated stem cell donor registries and the need for further donor recruitment efforts. In this study, we describe a general framework for MP estimations of specific and mixed patient populations under consideration of international stem cell donor exchange. Calculations were based on population-specific 4-locus (HLA-A, -B, -C, -DRB1 high-resolution haplotype frequencies (HF of up to 21 populations. In various scenarios, we calculated several quantities of high practical relevance, including the maximal MP that can be reached by recruiting a fixed number of donors, the corresponding optimal composition by population of new registrants, and the minimal number of donors who need to be recruited to reach a defined MP. Starting at current donor numbers, the largest MP increases due to n = 500,000 additional same-population donors were observed for patients from Bosnia-Herzegovina (+0.25, Greece (+0.21 and Romania (+0.20. Especially small MP increases occurred for European Americans (+0.004, Germans (+0.01 and Hispanic Americans (+0.01. Due to the large Chinese population, the optimal distribution of n = 5,000,000 new donors worldwide included 3.9 million Chinese donors. As a general result of our calculations, we observed a need for same-population donor recruitment in order to increase population-specific MP efficiently. This result was robust despite limitations of our input data, including the use of HF derived from relatively small samples ranging from n = 1028 (Bosnia-Herzegovina to n = 33,083 (Turkey individuals. National strategies that neglect domestic donor recruitment should therefore be critically re-assessed, especially if only few donors have been recruited so far.
SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments.
Elsts, Atis; Pentjuss, Agris; Stalidzans, Egils
2017-09-15
Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions. https://github.com/atiselsts/spacescanner . egils.stalidzans@lu.lv. Supplementary data are available at Bioinformatics online.
Chemically optimizing operational efficiency of molecular rotary motors.
Conyard, Jamie; Cnossen, Arjen; Browne, Wesley R; Feringa, Ben L; Meech, Stephen R
2014-07-09
Unidirectional molecular rotary motors that harness photoinduced cis-trans (E-Z) isomerization are promising tools for the conversion of light energy to mechanical motion in nanoscale molecular machines. Considerable progress has been made in optimizing the frequency of ground-state rotation, but less attention has been focused on excited-state processes. Here the excited-state dynamics of a molecular motor with electron donor and acceptor substituents located to modify the excited-state reaction coordinate, without altering its stereochemistry, are studied. The substituents are shown to modify the photochemical yield of the isomerization without altering the motor frequency. By combining 50 fs resolution time-resolved fluorescence with ultrafast transient absorption spectroscopy the underlying excited-state dynamics are characterized. The Franck-Condon excited state relaxes in a few hundred femtoseconds to populate a lower energy dark state by a pathway that utilizes a volume conserving structural change. This is assigned to pyramidalization at a carbon atom of the isomerizing bridging double bond. The structure and energy of the dark state thus reached are a function of the substituent, with electron-withdrawing groups yielding a lower energy longer lived dark state. The dark state is coupled to the Franck-Condon state and decays on a picosecond time scale via a coordinate that is sensitive to solvent friction, such as rotation about the bridging bond. Neither subpicosecond nor picosecond dynamics are sensitive to solvent polarity, suggesting that intramolecular charge transfer and solvation are not key driving forces for the rate of the reaction. Instead steric factors and medium friction determine the reaction pathway, with the sterically remote substitution primarily influencing the energetics. Thus, these data indicate a chemical method of optimizing the efficiency of operation of these molecular motors without modifying their overall rotational frequency.
3D prostate TRUS segmentation using globally optimized volume-preserving prior.
Qiu, Wu; Rajchl, Martin; Guo, Fumin; Sun, Yue; Ukwatta, Eranga; Fenster, Aaron; Yuan, Jing
2014-01-01
An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5% +/- 2.4%, a MAD of 1.4 +/- 0.6 mm, a MAXD of 5.2 +/- 3.2 mm, and a VD of 7.5% +/- 6.2% in - 1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.
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
Directory of Open Access Journals (Sweden)
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
Non-linear Global Optimization using Interval Arithmetic and Constraint Propagation
DEFF Research Database (Denmark)
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...
Drechsler, Martin
2017-02-01
Auctions have been proposed as alternatives to payments for environmental services when spatial interactions and costs are better known to landowners than to the conservation agency (asymmetric information). Recently, an auction scheme was proposed that delivers optimal conservation in the sense that social welfare is maximized. I examined the social welfare and the budget efficiency delivered by this scheme, where social welfare represents the difference between the monetized ecological benefit and the conservation cost incurred to the landowners and budget efficiency is defined as maximizing the ecological benefit for a given conservation budget. For the analysis, I considered a stylized landscape with land patches that can be used for agriculture or conservation. The ecological benefit was measured by an objective function that increases with increasing number and spatial aggregation of conserved land patches. I compared the social welfare and the budget efficiency of the auction scheme with an agglomeration payment, a policy scheme that considers spatial interactions and that was proposed recently. The auction delivered a higher level of social welfare than the agglomeration payment. However, the agglomeration payment was more efficient budgetarily than the auction, so the comparative performances of the 2 schemes depended on the chosen policy criterion-social welfare or budget efficiency. Both policy criteria are relevant for conservation. Which one should be chosen depends on the problem at hand, for example, whether social preferences should be taken into account in the decision of how much money to invest in conservation or whether the available conservation budget is strictly limited. © 2016 Society for Conservation Biology.
Directory of Open Access Journals (Sweden)
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.
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.
Global optimization of fuel consumption in rendezvous scenarios by the method of interval analysis
Ma, Hongliang; Xu, Shijie
2015-03-01
To reduce the optimal but large Δv of the fixed-short-time two impulse Lambert rendezvous between two spacecrafts along two coplanar circular orbits, the three-impulse Lambert rendezvous optimized via the optimization algorithm-interval analysis (IA) is proposed in this paper. The purpose of optimization is to minimize the velocity increment of the fixed-short-time three-impulse Lambert rendezvous. The optimization algorithm IA is given for solving the rendezvous optimization problem with multiple uncertain variables, and strong nonlinearity and nonconvexity. Numerical examples of the time-open, coplanar-circular-orbit, multiple-revolution Lambert rendezvous with a parking time optimized via the optimization algorithm IA are firstly undertaken to validate the feasibility of the optimization algorithm IA by comparing the optimization results with those of the globally optimal Hohmann transfer. The results indicate that the globally optimal parameters of the time-open coplanar-circular-orbit multiple-revolution Lambert rendezvous can be obtained by the optimization algorithm IA, and the initial separation angle of two spacecrafts with different orbit radius can be adjusted to obtain the globally optimal and small Δv by distributing an optimal parking time. After that, for the fixed-short-time two-impulse Lambert rendezvous problem without sufficient time to adjust the separation angle by distributing a parking time like the open-time Lambert rendezvous problem, three-impulse Lambert rendezvous involving multiple optimization variables is given and the variables are optimized by the optimization algorithm IA to obtain an optimal and small Δv. Numerical simulation indicates that the optimal and small Δv of the fixed short time, three-impulse Lambert rendezvous can be obtained using the optimization algorithm IA.
Lun, Fei; Liu, Junguo; Ciais, Philippe; Nesme, Thomas; Chang, Jinfeng; Wang, Rong; Goll, Daniel; Sardans, Jordi; Peñuelas, Josep; Obersteiner, Michael
2018-01-01
The application of phosphorus (P) fertilizer to agricultural soils increased by 3.2 % annually from 2002 to 2010. We quantified in detail the P inputs and outputs of cropland and pasture and the P fluxes through human and livestock consumers of agricultural products on global, regional, and national scales from 2002 to 2010. Globally, half of the total P inputs into agricultural systems accumulated in agricultural soils during this period, with the rest lost to bodies of water through complex flows. Global P accumulation in agricultural soil increased from 2002 to 2010 despite decreases in 2008 and 2009, and the P accumulation occurred primarily in cropland. Despite the global increase in soil P, 32 % of the world's cropland and 43 % of the pasture had soil P deficits. Increasing soil P deficits were found for African cropland vs. increasing P accumulation in eastern Asia. European and North American pasture had a soil P deficit because the continuous removal of biomass P by grazing exceeded P inputs. International trade played a significant role in P redistribution among countries through the flows of P in fertilizer and food among countries. Based on country-scale budgets and trends we propose policy options to potentially mitigate regional P imbalances in agricultural soils, particularly by optimizing the use of phosphate fertilizer and the recycling of waste P. The trend of the increasing consumption of livestock products will require more P inputs to the agricultural system, implying a low P-use efficiency and aggravating P-stock scarcity in the future. The global and regional phosphorus budgets and their PUEs in agricultural systems are publicly available at https://doi.pangaea.de/10.1594/PANGAEA.875296" target="_blank">https://doi.pangaea.de/10.1594/PANGAEA.875296.
Directory of Open Access Journals (Sweden)
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.
Perceptual Zero-Tree Coding with Efficient Optimization for Embedded Platforms
Directory of Open Access Journals (Sweden)
B.F. Wu
2013-08-01
Full Text Available This study proposes a block-edge-based perceptual zero-tree coding (PZTC method, which is implemented with efficient optimization on the embedded platform. PZTC combines two novel compression concepts for coding efficiency and quality: block-edge detection (BED and the low-complexity and low-memory entropy coder (LLEC. The proposed PZTC was implemented as a fixed-point version and optimized on the DSP-based platform based on both the presented platform-independent and platform-dependent optimization technologies. For platform-dependent optimization, this study examines the fixed-point PZTC and analyzes the complexity to optimize PZTC toward achieving an optimal coding efficiency. Furthermore, hardware-based platform-dependent optimizations are presented to reduce the memory size. The performance, such as compression quality and efficiency, is validated by experimental results.
Efficient source polarization optimization for robust optical lithography
Ma, Xu; Gao, Jie; Han, Chunying; Li, Yanqiu; Dong, Lisong; Liu, Lihui
2014-03-01
Source optimization (SO) has become increasing important to improve the process window (PW) of optical lithography systems. Most of current SO approaches modify the source intensity distribution, but fix the polarization state thus limiting the degrees of optimization freedom. In addition, these SO methods simultaneously optimize the imaging performance on focal and defocal planes to extend the depth of focus (DOF) at the cost of increasing the computational complexity. To overcome these above limitations, this paper develops a pixelated gradient-based polarization optimization (PO) method to effectively extend the PW by seeking for the optimal polarization angle for each point source. In order to accelerate the optimization process, the proposed method tries to optimize a compact cost function incorporating the integral imaging performance over a certain defocus range, rather than taking into account the separate performance metrics on different imaging planes. A gradientbased algorithm is exploited to iteratively modulate the polarization angles to keep reducing the cost function. Finally, a post-processing method is applied to reduce the complexity of the optimized polarization angle pattern for improving its manufacturability. Simulations show that the proposed PO algorithm will achieve approximate two-fold speedup compared to the method using a traditional cost function. The proposed PO algorithm is potential to be applied independently or associated with source and mask optimizations to further improve the lithographic performance.
Efficient sensor placement optimization using gradient descent and probabilistic coverage.
Akbarzadeh, Vahab; Lévesque, Julien-Charles; Gagné, Christian; Parizeau, Marc
2014-08-21
We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.
Directory of Open Access Journals (Sweden)
Zhongbo Sun
2014-01-01
Full Text Available Two modified three-term type conjugate gradient algorithms which satisfy both the descent condition and the Dai-Liao type conjugacy condition are presented for unconstrained optimization. The first algorithm is a modification of the Hager and Zhang type algorithm in such a way that the search direction is descent and satisfies Dai-Liao’s type conjugacy condition. The second simple three-term type conjugate gradient method can generate sufficient decent directions at every iteration; moreover, this property is independent of the steplength line search. Also, the algorithms could be considered as a modification of the MBFGS method, but with different zk. Under some mild conditions, the given methods are global convergence, which is independent of the Wolfe line search for general functions. The numerical experiments show that the proposed methods are very robust and efficient.
Directory of Open Access Journals (Sweden)
Ricardo Soto
2016-01-01
Full Text Available The Machine-Part Cell Formation Problem (MPCFP is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.
Domestic energy management methodology for optimizing efficiency in Smart Grids
Molderink, Albert; Bakker, Vincent; Bosman, M.G.C.; Hurink, Johann L.; Smit, Gerardus Johannes Maria
2009-01-01
Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be
A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm
Directory of Open Access Journals (Sweden)
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
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
Directory of Open Access Journals (Sweden)
Sorribas Albert
2011-08-01
Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.
Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert
2011-08-25
Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.
Auditory-like filterbank: An optimal speech processor for efficient ...
Indian Academy of Sciences (India)
The transmitter and the receiver in a communication system have to be designed optimally with respect to one another to ensure reliable and efﬁcient communication. Following this principle, we derive an optimal ﬁlterbank for processing speech signal in the listener's auditory system (receiver), so that maximum information ...
Superefficient Refrigerators: Opportunities and Challenges for Efficiency Improvement Globally
Energy Technology Data Exchange (ETDEWEB)
Shah, Nihar; Park, Won Young; Bojda, Nicholas; McNeil, Michael A.
2014-08-01
As an energy-intensive mainstream product, residential refrigerators present a significant opportunity to reduce electricity consumption through energy efficiency improvements. Refrigerators expend a considerable amount of electricity during normal use, typically consuming between 100 to 1,000 kWh of electricity per annum. This paper presents the results of a technical analysis done for refrigerators in support of the Super-efficient Equipment and Appliance Deployment (SEAD) initiative. Beginning from a base case representative of the average unit sold in India, we analyze efficiency improvement options and their corresponding costs to build a cost-versus-efficiency relationship. We then consider design improvement options that are known to be the most cost effective and that can improve efficiency given current design configurations. We also analyze and present additional super-efficient options, such as vacuum-insulated panels. We estimate the cost of conserved electricity for the various options, allowing flexible program design for market transformation programs toward higher efficiency. We estimate ~;;160TWh/year of energy savings are cost effective in 2030, indicating significant potential for efficiency improvement in refrigerators in SEAD economies and China.
Improving the efficiency of solving discrete optimization problems: The case of VRP
Belov, A.; Slastnikov, S.
2016-02-01
Paper is devoted constructing efficient metaheuristics algorithms for discrete optimization problems. Particularly, we consider vehicle routing problem applying original ant colony optimization method to solve it. Besides, some parts of algorithm are separated for parallel computing. Some experimental results are performed to compare the efficiency of these methods.
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management Project
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage
Directory of Open Access Journals (Sweden)
Vahab Akbarzadeh
2014-08-01
Full Text Available We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.
Optimizing power efficiency in radio-over-fiber systems
DEFF Research Database (Denmark)
Koonen, A. M. J.; Popov, M.; Wessing, Henrik
2013-01-01
Fiber-fed radio pico-cells topologies can reduce the overall power consumption of wireless communication networks. Joint optimization of fiber and radio network parts yields an optimum number of pico-cells which minimizes power consumption....
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.
Li, Beibei; Li, Xiaojiang
2017-02-01
In accordance with the high impact of the uneven distribution of laser beam power on the photovoltaic efficiency of photovoltaic cell (PV) array, a method based on PV layout optimization is proposed to improve the photovoltaic efficiency. First of all, a mathematical model of series-parallel PV array is built, and by analyzing the influencing factors on photovoltaic efficiency, the idea and scheme to improve the photovoltaic efficiency based on PV layout optimization is provided; then, the MATLAB/Simulink simulation tool is used to simulate the effects of improving photoelectric efficiency. The simulation results show that compared to the traditional PV array, the optimized PV array can obtain higher photovoltaic efficiency, and compared to the situation with uneven temperature distribution, the array efficiency has higher efficiency under even temperature distribution.
Energy Technology Data Exchange (ETDEWEB)
Huang, Hong Zhong; Zhang, Xudong; Meng, De Biao; Wang, Zhonglai; Liu, Yu [University of Electronic Science and Technology of China, Chengdu (China)
2013-06-15
Reliability based design optimization (RBDO) has been widely implemented in engineering practices for high safety and reliability. It is an important challenge to improve computational efficiency. Sequential optimization and reliability assessment (SORA) has made great efforts to improve computational efficiency by decoupling a RBDO problem into sequential deterministic optimization and reliability analysis as a single-loop method. In this paper, in order to further improve computational efficiency and extend the application of the current SORA method, an enhanced SORA (ESORA) is proposed by considering constant and varying variances of random design variables while keeping the sequential framework. Some mathematical examples and an engineering case are given to illustrate the proposed method and validate the efficiency.
An efficient algorithm for function optimization: modified stem cells algorithm
Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad
2013-03-01
In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
Globally Optimal Segmentation of Permanent-Magnet Systems
DEFF Research Database (Denmark)
Insinga, Andrea Roberto; Bjørk, Rasmus; Smith, Anders
2016-01-01
functional that is linear in the magnetic field. This approach, however, yields a continuously varying remanent flux density, while in practical applications, magnetic assemblies are realized by combining uniformly magnetized segments. The problem of determining the optimal shape of each of these segments...... remains unsolved. We show that the problem of optimal segmentation of a two-dimensional permanent-magnet assembly with respect to a linear objective functional can be reduced to the problem of piecewise linear approximation of a plane curve by perimeter maximization. Once the problem has been cast...
Optimizing RDF Data Cubes for Efficient Processing of Analytical Queries
DEFF Research Database (Denmark)
Jakobsen, Kim Ahlstrøm; Andersen, Alex B.; Hose, Katja
2015-01-01
In today’s data-driven world, analytical querying, typically based on the data cube concept, is the cornerstone of answering important business questions and making data-driven decisions. Traditionally, the underlying analytical data was mostly internal to the organization and stored in relational...... data warehouses and data cubes. Today, external data sources are essential for analytics and, as the Semantic Web gains popularity, more and more external sources are available in native RDF. With the recent SPARQL 1.1 standard, performing analytical queries over RDF data sources has finally become...... feasible. However, unlike their relational counterparts, RDF data cubes stores lack optimizations that enable fast querying. In this paper, we present an approach to optimizing RDF data cubes that is based on three novel cube patterns that optimize RDF data cubes, as well as associated algorithms...
Are the global REIT markets efficient by a new approach?
Directory of Open Access Journals (Sweden)
Fang Hao
2013-01-01
Full Text Available This study uses a panel KSS test by Nuri Ucar and Tolga Omay (2009, with a Fourier function based on the sequential panel selection method (SPSM procedure proposed by Georgios Chortareas and George Kapetanios (2009 to test the efficiency of REIT markets in 16 countries from 28 March 2008 to 27 June 2011. A Fourier approximation often captures the behavior of an unknown break, and testing for a unit root increases its power to do so. Moreover, SPSM can determine the mix of I(0 and I(1 series in a panel setting to clarify how many and which are random walk processes. Our empirical results demonstrate that REIT markets are efficient in all sampled countries except the UK. Our results imply that investors in countries with efficient REIT markets can adopt more passive portfolio strategies.
Efficient protein production by yeast requires global tuning of metabolism
DEFF Research Database (Denmark)
Huang, Mingtao; Bao, Jichen; Hallstrom, Bjorn M.
2017-01-01
intracellular processes with many underlying mechanisms still remaining unclear. Here, we use RNA-seq to study the genome-wide transcriptional response to protein secretion in mutant yeast strains. We find that many cellular processes have to be attuned to support efficient protein secretion. In particular......, altered energy metabolism resulting in reduced respiration and increased fermentation, as well as balancing of amino-acid biosynthesis and reduced thiamine biosynthesis seem to be particularly important. We confirm our findings by inverse engineering and physiological characterization and show...... that by tuning metabolism cells are able to efficiently secrete recombinant proteins. Our findings provide increased understanding of which cellular regulations and pathways are associated with efficient protein secretion....
Globalization and bank efficiency nexus: Symbiosis or parasites?
Directory of Open Access Journals (Sweden)
Fadzlan Sufian
2012-07-01
Full Text Available The performance of the banking sector is a subject that has received academic and policymaker attention in recent years. The rapid pace of the financial sector liberalization further accentuates policymakers’ interest in the topic. To date, studies examining the performance of the Asian banking sectors are numerous. However, these studies have mainly concentrated on the impacts of banking sector restructuring and bank ownership issues, while empirical evidence on the impact of economic globalization is completely missing from the literature. In light of the knowledge gap, this study provides, for the first time, empirical evidence on the nexus between the level of globalization and the performance of the Indonesian banking sector during the period from 1999 to 2007.
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
Directory of Open Access Journals (Sweden)
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.
Improvement of Piezoelectric Energy Harvester Efficiency Through Optimal Patch Configuration
Gosliga, Julian S.; Ganilova, Olga A.
2016-01-01
The aim of this paper is to explore how to improve the efficiency of a vibrating hybrid energy harvester through changing the patch configuration. The results of this work identify the patch configuration that maximises output while using the same amount of piezoelectric material. Using 6 patches was found to be the most efficient when looking at the energy output from a single cycle. Stress distributions generated using ANSYS show that this was because the patches were all locate...
Efficient Optimization Methods for Communication Network Planning and Assessment
Kiese, Moritz
2010-01-01
In this work, we develop efficient mathematical planning methods to design communication networks. First, we examine future technologies for optical backbone networks. As new, more intelligent nodes cause higher dynamics in the transport networks, fast planning methods are required. To this end, we develop a heuristic planning algorithm. The evaluation of the cost-efficiency of new, adapative transmission techniques comprises the second topic of this section. In the second part of this work, ...
Efficient Data-Driven Rule for Obtaining an Optimal Predictive ...
African Journals Online (AJOL)
This paper proposes a rule for optimizing a predictive discriminant function (PDF) in discriminant analysis (DA). In this study, we carried out a sequential-stepwise analysis on the predictor variables and a percentage-N-fold cross-validation on the data set obtained from students' academic records in a university system.
On multigrid-CG for efficient topology optimization
DEFF Research Database (Denmark)
Amir, Oded; Aage, Niels; Lazarov, Boyan Stefanov
2014-01-01
reduction is obtained by exploiting specific characteristics of a multigrid preconditioned conjugate gradients (MGCG) solver. In particular, the number of MGCG iterations is reduced by relating it to the geometric parameters of the problem. At the same time, accurate outcome of the optimization process...
Chemically Optimizing Operational Efficiency of Molecular Rotary Motors
Conyard, Jamie; Cnossen, Arjen; Browne, Wesley R.; Feringa, Ben L.; Meech, Stephen R.
2014-01-01
Unidirectional molecular rotary motors that harness photoinduced cis-trans (E-Z) isomerization are promising tools for the conversion of light energy to mechanical motion in nanoscale molecular machines. Considerable progress has been made in optimizing the frequency of ground-state rotation, but
Efficient Approximation of Optimal Control for Markov Games
DEFF Research Database (Denmark)
Fearnley, John; Rabe, Markus; Schewe, Sven
2011-01-01
We study the time-bounded reachability problem for continuous-time Markov decision processes (CTMDPs) and games (CTMGs). Existing techniques for this problem use discretisation techniques to break time into discrete intervals, and optimal control is approximated for each interval separately...
Exergetic efficiency optimization for an irreversible heat pump ...
Indian Academy of Sciences (India)
This paper deals with the performance analysis and optimization for irreversible heat pumps working on reversed Brayton cycle with constant-temperature heat reservoirs ... Institute of Civil & Architectural Engineering, Beijing University of Technology, Beijing 100124, People's Republic of China; Postgraduate School, Naval ...
Efficient use of iterative solvers in nested topology optimization
DEFF Research Database (Denmark)
Amir, Oded; Stolpe, Mathias; Sigmund, Ole
2009-01-01
In the nested approach to structural optimization, most of the computational effort is invested in the solution of the finite element analysis equations. In this study, it is suggested to reduce this computational cost by using an approximation to the solution of the nested problem, generated...
Efficient use of iterative solvers in nested topology optimization
DEFF Research Database (Denmark)
Amir, Oded; Stolpe, Mathias; Sigmund, Ole
2010-01-01
In the nested approach to structural optimization, most of the computational effort is invested in the solution of the analysis equations. In this study, it is suggested to reduce this computational cost by using an approximation to the solution of the analysis problem, generated by a Krylov...
An Efficient Algorithm for Solving Single Veriable Optimization ...
African Journals Online (AJOL)
Many methods are available for finding x*E Rn which minimizes the real value function f(x), some of which are Fibonacci Search Algorithm, Quadratic Search Algorithm, Convergence Algorithm and Cubic Search Algorithm. In this research work, existing algorithms used in single variable optimization problems are critically ...
Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks.
Zhang, Jing; Zhou, Qingjie; Ng, Derrick Wing Kwan; Jo, Minho
2017-09-15
In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity.
Saborido, Rubén; Ruiz, Ana B; Luque, Mariano
2017-01-01
In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.
Optimization Case Study: ISR Allocation in the Global Force Management Process
2016-09-01
REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE OPTIMIZATION CASE STUDY: ISR ALLOCATION IN THE GLOBAL FORCE MANAGEMENT PROCESS 5...Force Management Initial Capabilities Document. Washington, DC: Department of Defense. Joint Chiefs of Staff. 2014a. Global Force Management Allocation...Defense. ———. 2012b. Capability Development Document for Global Force Management Data Initiative Increment 2 Next Steps: Manpower and Personnel
An Optimal Method for Developing Global Supply Chain Management System
Hao-Chun Lu; Yao-Huei Huang
2013-01-01
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 illus...
Directory of Open Access Journals (Sweden)
Imen Châari
2014-07-01
Full Text Available Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO and Genetic Algorithms (GA for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO, GA algorithms. It also outperforms the Dijkstra exact method in solving the path planning problem for large graph environments. It improves the solution quality up to 57% in comparison with CACO and reduces the execution time up to 83% as compared to Dijkstra for large and dense graphs. In addition, the experimental results on a real robot shows that smartPATH finds the optimal path with a probability up to 80% with a small gap not exceeding 1m in 98%.
Global efficiency of local immunization on complex networks
Hébert-Dufresne, Laurent; Young, Jean-Gabriel; Dubé, Louis J
2012-01-01
Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, information spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in networks of diverse nature. We propose a local measure of node influence based on the network's community structure which is easily estimated in real systems and frequently outperforms the usual measure of a node's importance. More importantly, through an extensive study spanning 17 empirical networks and 2 epidemic models, we formulate a readily applicable approach which proves efficient even though different networks and different diseases require different strategies.This research is expected to guide efforts regarding public health policies, computer network security and t...
Lin, Y. S.; Medlyn, B. E.; Duursma, R.; Prentice, I. C.; Wang, H.
2014-12-01
Stomatal conductance (gs) is a key land surface attribute as it links transpiration, the dominant component of global land evapotranspiration and a key element of the global water cycle, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycles, a global scale database and an associated globally applicable model of gs that allow predictions of stomatal behaviour are lacking. We present a unique database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. We employed a model of optimal stomatal conductance to assess differences in stomatal behaviour, and estimated the model slope coefficient, g1, which is directly related to the marginal carbon cost of water, for each dataset. We found that g1 varies considerably among PFTs, with evergreen savanna trees having the largest g1 (least conservative water use), followed by C3 grasses and crops, angiosperm trees, gymnosperm trees, and C4 grasses. Amongst angiosperm trees, species with higher wood density had a higher marginal carbon cost of water, as predicted by the theory underpinning the optimal stomatal model. There was an interactive effect between temperature and moisture availability on g1: for wet environments, g1 was largest in high temperature environments, indicated by high mean annual temperature during the period when temperature above 0oC (Tm), but it did not vary with Tm across dry environments. We examine whether these differences in leaf-scale behaviour are reflected in ecosystem-scale differences in water-use efficiency. These findings provide a robust theoretical framework for understanding and predicting the behaviour of stomatal conductance across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of productivity and ecohydrological processes in a future changing climate.
Optimization of distribution transformer efficiency characteristics. Final report, March 1979
Energy Technology Data Exchange (ETDEWEB)
1980-06-01
A method for distribution transformer loss evaluation was derived. The total levalized annual cost method was used and was extended to account properly for conditions of energy cost inflation, peak load growth, and transformer changeout during the evaluation period. The loss costs included were the no-load and load power losses, no-load and load reactive losses, and the energy cost of regulation. The demand and energy components of loss costs were treated separately to account correctly for the diversity of load losses and energy cost inflation. The complete distribution transformer loss evaluation equation is shown, with the nomenclature and definitions for the parameters provided. Tasks described are entitled: Establish Loss Evaluation Techniques; Compile System Cost Parameters; Compile Load Parameters and Loading Policies; Develop Transformer Cost/Performance Relationship; Define Characteristics of Multiple Efficiency Transformer Package; Minimize Life Cycle Cost Based on Single Efficiency Characteristic Transformer Design; Minimize Life Cycle Cost Based on Multiple Efficiency Characteristic Transformer Design; and Interpretation.
Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.
Directory of Open Access Journals (Sweden)
Saket Navlakha
2015-07-01
Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.
On parallel Branch and Bound frameworks for Global Optimization
Herrera, Juan F.R.; Salmerón, José M.G.; Hendrix, Eligius M.T.; Asenjo, Rafael; Casado, Leocadio G.
2017-01-01
Branch and Bound (B&B) algorithms are known to exhibit an irregularity of the search tree. Therefore, developing a parallel approach for this kind of algorithms is a challenge. The efficiency of a B&B algorithm depends on the chosen Branching, Bounding, Selection, Rejection, and Termination
Optimization of the Separation Parameters and Indicators of Separation Efficiency of Buckwheat Seeds
Directory of Open Access Journals (Sweden)
Stanisław Konopka
2017-11-01
Full Text Available The separation parameters and the indicators of separation efficiency for buckwheat seeds and impurities that are difficult to separate were optimized with the use of self-designed software based on genetic algorithms. The results of the calculations differed significantly from the suboptimal values determined in previous studies. The optimal values of the indicator of separation efficiency were higher; whereas the values of the indicator of buckwheat seed loss were significantly lower. The optimal working parameters for a seed separator in order to promote separation efficiency were determined.
Energy Technology Data Exchange (ETDEWEB)
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.
Optimization of Mass and Stiffness Distribution for Efficient Bipedal Walking
Duindam, V.; Stramigioli, Stefano
2005-01-01
Energy-efficient control of bipedal walking robots requires both minimization of mechanical energy losses (often mainly due to impacts) and the use of natural oscillations in a mechanism to minimize actuator torques (as shown by research on passive dynamic walking). In this paper, we discuss how
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.
Leveraging Complementary Distribution Channels for an Effective, Efficient Global Supply Chain
2007-01-01
their strengths , the channels become complementary elements of an effective, efficient global supply chain. The Department of Defense’s inventory...leverage their individual strengths , these channels become complementary elements of an effective, efficient global supply chain. - 3 - 2. Major...commercial premium air delivery carriers such as FedEx, DHL , and UPS through blanket contracts that are all part of the WWX program. “Surface-direct” is
A Global Optimization Algorithm for Sum of Linear Ratios Problem
Directory of Open Access Journals (Sweden)
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.
On the Efficiency of Local and Global Communication in Modular Robots
DEFF Research Database (Denmark)
Garcia, Ricardo Franco Mendoza; Schultz, Ulrik Pagh; Støy, Kasper
2009-01-01
As exchange of information is essential to modular robots, deciding between local or global communication is a common design choice. This choice, however, still lacks theoretical support. In this paper we analyse the efficiency of local and global communication in modular robots. To this end, we ...
On the Efficiency of Local and Global Communication in Modular Robots
DEFF Research Database (Denmark)
Garcia, Ricardo Franco Mendoza; Schultz, Ulrik Pagh; Støy, Kasper
2009-01-01
As exchange of information is essential to modular robots, deciding between local or global communication is a common design choice. This choice, however, still lacks theoretical support. In this paper we analyse the efficiency of local and global communication in modular robots. To this end, we...
Design of robust and efficient photonic switches using topology optimization
DEFF Research Database (Denmark)
Elesin, Yuriy; Lazarov, Boyan Stefanov; Jensen, Jakob Søndergaard
2012-01-01
with a performance which is very sensitive to geometric manufacturing errors (under- or over-etching). Such behavior is undesirable and robustness is achieved by optimizing for several design realizations. The possible geometric uncertainties are modeled by random variables. It is shown that the designs...... are insensitive with respect to variations of signal parameters, such as signal amplitudes and phase shifts. The obtained robust designs of a 1D photonic switch can substantially outperform simple bandgap designs, known from the literature, where switching takes place due to the bandgap shift produced by a strong...
Biological optimization systems for enhancing photosynthetic efficiency and methods of use
Hunt, Ryan W.; Chinnasamy, Senthil; Das, Keshav C.; de Mattos, Erico Rolim
2012-11-06
Biological optimization systems for enhancing photosynthetic efficiency and methods of use. Specifically, methods for enhancing photosynthetic efficiency including applying pulsed light to a photosynthetic organism, using a chlorophyll fluorescence feedback control system to determine one or more photosynthetic efficiency parameters, and adjusting one or more of the photosynthetic efficiency parameters to drive the photosynthesis by the delivery of an amount of light to optimize light absorption of the photosynthetic organism while providing enough dark time between light pulses to prevent oversaturation of the chlorophyll reaction centers are disclosed.
Towards optimizing the efficiency of electrical power generation
Energy Technology Data Exchange (ETDEWEB)
Al-Zubaidy, S. [Univ. Malaysia Sarawak (Malaysia). Faculty of Engineering; Bhinder, F.S. [Univ. of Hertfordshire, Hatfield (United Kingdom)
1996-12-31
The thermal efficiency of a gas turbine engine, which ranges between 28% to 33%, may be raised by recovering some of the low grade thermal energy from the exhaust gas to heat the high pressure air leaving the compressor. The overall thermal efficiency of a combined power and power (CCP) cogeneration plant can be raised to about 60%. This is twice the value that may be reached by a modern gas turbine and nearly one and a half times the value that may be reached by a modern steam turbine. The work presented in this paper is an initial and preliminary study of a sponsored project that examines the effect of design parameters on overall performance of the power cycle with the view of producing a code that enables researchers to produce a complete computer simulation of the CCP for the purpose of developing control strategies.
Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management
P. Radhakrishnan; W. M. Prasad; M. R. Gopalan
2009-01-01
Problem statement: Today, inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout the supply chain, service provided to the customer ultimately gets enhanced. Hence, to ensure minimal cost for the supply chain, the determination of the level of inventory to be held at various levels in a supply chain is unavoidable. Minimizing the total supply chain cost refers to the reduction o...
Optimizing design efficiency of free recall events for FMRI.
Oztekin, Ilke; Long, Nicole M; Badre, David
2010-10-01
Free recall is a fundamental paradigm for studying memory retrieval in the context of minimal cue support. Accordingly, free recall has been extensively studied using behavioral methods. However, the neural mechanisms that support free recall have not been fully investigated due to technical challenges associated with probing individual recall events with neuroimaging methods. Of particular concern is the extent to which the uncontrolled latencies associated with recall events can confer sufficient design efficiency to permit neural activation for individual conditions to be distinguished. The present study sought to rigorously assess the feasibility of testing individual free recall events with fMRI. We used both theoretically and empirically derived free recall latency distributions to generate simulated fMRI data sets and assessed design efficiency across a range of parameters that describe free recall performance and fMRI designs. In addition, two fMRI experiments empirically assessed whether differential neural activation in visual cortex at onsets determined by true free recall performance across different conditions can be resolved. Collectively, these results specify the design and performance parameters that can provide comparable efficiency between free recall designs and more traditional jittered event-related designs. These findings suggest that assessing BOLD response during free recall using fMRI is feasible, under certain conditions, and can serve as a powerful tool in understanding the neural bases of memory search and overt retrieval.
DEFF Research Database (Denmark)
Thummala, Prasanth; Schneider, Henrik; Zhang, Zhe
2015-01-01
.The energy efficiency is optimized using a proposed new automatic winding layout (AWL) technique and a comprehensive loss model.The AWL technique generates a large number of transformer winding layouts.The transformer parasitics such as dc resistance, leakage inductance and self-capacitance are calculated...... for each winding layout.An optimization technique is formulated to minimize the sum of energy losses during charge and discharge operations.The efficiency and energy loss distribution results from the optimization routine provide a deep insight into the high voltage transformer designand its impact...... on the total converter efficiency.The proposed efficiency optimization approach is experimentally verified on a25 W (average charging power) with100 W (peakpower) flyback dc-dc prototype....
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
Pasquier, B.; Holzer, M.; Frants, M.
2016-02-01
We construct a data-constrained mechanistic inverse model of the ocean's coupled phosphorus and iron cycles. The nutrient cycling is embedded in a data-assimilated steady global circulation. Biological nutrient uptake is parameterized in terms of nutrient, light, and temperature limitations on growth for two classes of phytoplankton that are not transported explicitly. A matrix formulation of the discretized nutrient tracer equations allows for efficient numerical solutions, which facilitates the objective optimization of the key biogeochemical parameters. The optimization minimizes the misfit between the modelled and observed nutrient fields of the current climate. We systematically assess the nonlinear response of the biological pump to changes in the aeolian iron supply for a variety of scenarios. Specifically, Green-function techniques are employed to quantify in detail the pathways and timescales with which those perturbations are propagated throughout the world oceans, determining the global teleconnections that mediate the response of the global ocean ecosystem. We confirm previous findings from idealized studies that increased iron fertilization decreases biological production in the subtropical gyres and we quantify the counterintuitive and asymmetric response of global productivity to increases and decreases in the aeolian iron supply.
Generating Multiple Alternative Clusterings Via Globally Optimal Subspaces
DEFF Research Database (Denmark)
Dang, Xuan-Hong; Bailey, James
2014-01-01
Clustering analysis is important for exploring complex datasets. Alternative clustering analysis is an emerging subfield involving techniques for the generation of multiple different clusterings, allowing the data to be viewed from different perspectives. We present two new algorithms...... for alternative clustering generation. A distinctive feature of our algorithms is their principled formulation of an objective function, facilitating the discovery of a subspace satisfying natural quality and orthogonality criteria. The first algorithm is a regularization of the Principal Components analysis...... method, whereas the second is a regularization of graph-based dimension reduction. In both cases, we demonstrate a globally optimum subspace solution can be computed. Experimental evaluation shows our techniques are able to equal or outperform a range of existing methods....
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.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
Directory of Open Access Journals (Sweden)
T. A. Bozhanova
2009-09-01
Full Text Available We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also proved.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
T. A. Bozhanova
2009-01-01
We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also...
Optimizing Sampling Efficiency for Biomass Estimation Across NEON Domains
Abercrombie, H. H.; Meier, C. L.; Spencer, J. J.
2013-12-01
Over the course of 30 years, the National Ecological Observatory Network (NEON) will measure plant biomass and productivity across the U.S. to enable an understanding of terrestrial carbon cycle responses to ecosystem change drivers. Over the next several years, prior to operational sampling at a site, NEON will complete construction and characterization phases during which a limited amount of sampling will be done at each site to inform sampling designs, and guide standardization of data collection across all sites. Sampling biomass in 60+ sites distributed among 20 different eco-climatic domains poses major logistical and budgetary challenges. Traditional biomass sampling methods such as clip harvesting and direct measurements of Leaf Area Index (LAI) involve collecting and processing plant samples, and are time and labor intensive. Possible alternatives include using indirect sampling methods for estimating LAI such as digital hemispherical photography (DHP) or using a LI-COR 2200 Plant Canopy Analyzer. These LAI estimations can then be used as a proxy for biomass. The biomass estimates calculated can then inform the clip harvest sampling design during NEON operations, optimizing both sample size and number so that standardized uncertainty limits can be achieved with a minimum amount of sampling effort. In 2011, LAI and clip harvest data were collected from co-located sampling points at the Central Plains Experimental Range located in northern Colorado, a short grass steppe ecosystem that is the NEON Domain 10 core site. LAI was measured with a LI-COR 2200 Plant Canopy Analyzer. The layout of the sampling design included four, 300 meter transects, with clip harvests plots spaced every 50m, and LAI sub-transects spaced every 10m. LAI was measured at four points along 6m sub-transects running perpendicular to the 300m transect. Clip harvest plots were co-located 4m from corresponding LAI transects, and had dimensions of 0.1m by 2m. We conducted regression analyses
Vaziri Yazdi Pin, Mohammad
practices. Single criterion optimization algorithms using mathematical programming for globally optimal solutions have been developed for three objectives of cost, reliability, and the social/environmental impacts. Additional algorithms for inclusions of upgrade and optimal load assignment possibilities have been developed. Algorithms have been developed to handle the expansion as a multiobjective decision process. Typical data from both major investor owned and major municipal utilities operating in California USA, have been utilized to implement and test the algorithms on practical test cases. Results of the case studies and associated analyses indicate that the developed algorithms also perform efficiently in solving the multistage and multiobjective expansion problem.
Optimal perturbations of non-parallel wakes and their stabilizing effect on the global instability
Del Guercio, Gerardo; Cossu, Carlo; Pujals, Gregory
2014-02-01
We compute the spatial optimal energy amplification of steady inflow perturbations in a non-parallel wake and analyse their stabilizing action on the global mode instability. The optimal inflow perturbations, which are assumed spanwise periodic and varicose, consist in streamwise vortices that induce the downstream spatial transient growth of streamwise streaks. The maximum energy amplification of the streaks increases with the spanwise wavelength of the perturbations, in accordance with previous results obtained for the temporal energy growth supported by parallel wakes. A family of increasingly streaky wakes is obtained by forcing optimal inflow perturbations of increasing amplitude and then solving the nonlinear Navier-Stokes equations. We show that the linear global instability of the wake can be completely suppressed by forcing optimal perturbations of sufficiently large amplitude. The attenuation and suppression of self-sustained oscillations in the wake by optimal 3D perturbations is confirmed by fully nonlinear numerical simulations. We also show that the amplitude of optimal spanwise periodic (3D) perturbations of the basic flow required to stabilize the global instability is much smaller than the one required by spanwise uniform (2D) perturbations despite the fact that the first order sensitivity of the global eigenvalue to basic flow modifications is zero for 3D spanwise periodic modifications and non-zero for 2D modifications. We therefore conclude that first-order sensitivity analyses can be misleading if used far from the instability threshold, where higher order terms are the most relevant.
Fast Generation of Near-Optimal Plans for Eco-Efficient Stowage of Large Container Vessels
DEFF Research Database (Denmark)
Pacino, Dario; Delgado, Alberto; Jensen, Rune Møller
2011-01-01
Eco-efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills...
Directory of Open Access Journals (Sweden)
Peng Wang
2013-01-01
Full Text Available This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO. The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.
Wang, Peng; Zhu, Zhouquan; Huang, Shuai
2013-01-01
This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.
Biyanto, T. R.; Matradji; Syamsi, M. N.; Fibrianto, H. Y.; Afdanny, N.; Rahman, A. H.; Gunawan, K. S.; Pratama, J. A. D.; Malwindasari, A.; Abdillah, A. I.; Bethiana, T. N.; Putra, Y. A.
2017-11-01
The development of green building has been growing in both design and quality. The development of green building was limited by the issue of expensive investment. Actually, green building can reduce the energy usage inside the building especially in utilization of cooling system. External load plays major role in reducing the usage of cooling system. External load is affected by type of wall sheathing, glass and roof. The proper selection of wall, type of glass and roof material are very important to reduce external load. Hence, the optimization of energy efficiency and conservation in green building design is required. Since this optimization consist of integer and non-linear equations, this problem falls into Mixed-Integer-Non-Linear-Programming (MINLP) that required global optimization technique such as stochastic optimization algorithms. In this paper the optimized variables i.e. type of glass and roof were chosen using Duelist, Killer-Whale and Rain-Water Algorithms to obtain the optimum energy and considering the minimal investment. The optimization results exhibited the single glass Planibel-G with the 3.2 mm thickness and glass wool insulation provided maximum ROI of 36.8486%, EUI reduction of 54 kWh/m2·year, CO2 emission reduction of 486.8971 tons/year and reduce investment of 4,078,905,465 IDR.
Miao, Zhidong; Liu, Dake; Gong, Chen
2017-10-01
Inductive wireless power transfer (IWPT) is a promising power technology for implantable biomedical devices, where the power consumption is low and the efficiency is the most important consideration. In this paper, we propose an optimization method of impedance matching networks (IMN) to maximize the IWPT efficiency. The IMN at the load side is designed to achieve the optimal load, and the IMN at the source side is designed to deliver the required amount of power (no-more-no-less) from the power source to the load. The theoretical analyses and design procedure are given. An IWPT system for an implantable glaucoma therapeutic prototype is designed as an example. Compared with the efficiency of the resonant IWPT system, the efficiency of our optimized system increases with a factor of 1.73. Besides, the efficiency of our optimized IWPT system is 1.97 times higher than that of the IWPT system optimized by the traditional maximum power transfer method. All the discussions indicate that the optimization method proposed in this paper could achieve a high efficiency and long working time when the system is powered by a battery.
Ait moussa, Abdellah; Jassemnejad, Bahaeddin
2014-05-01
Nanocomposites with high-aspect ratio fillers attract enormous attention because of the superior physical properties of the composite over the parent matrix. Nanocomposites with functionalized graphene as fillers did not produce the high thermal conductivity expected due to the high interfacial thermal resistance between the functional groups and graphene flakes. We report here a robust and efficient technique that identifies the configuration of the functionalities for improved thermal conductivity. The method combines linearization of the interatomic interactions, calculation, and optimization of the thermal conductivity using the globalized and bounded Nelder-Mead algorithm.
Optimizing Nanopore Surface Properties for High-Efficiency Water Desalination
Cohen-Tanugi, David; Grossman, Jeffrey
2011-03-01
As water resources worldwide become rapidly scarcer, it is becoming increasingly important to devise new techniques to obtain clean water from seawater. At present, water purification technologies are limited by costly energy requirements relative to the theoretical thermodynamic limit and by insufficient understanding of the physical processes underlying ion filtration and fluid transport at the molecular scale. New advances in computational materials science offer a promising way to deepen our understanding of these physical phenomena. In this presentation, we describe a new approach for high-efficiency water desalination based on surface-engineered porous materials. This approach is especially relevant for promising technologies such as nanofiltration and membrane distillation, which offers promising advantages over traditional desalination technologies using mesoporous membranes that are only permeable to pure water vapor. More accurate molecular modeling of mesoporous and nanoporous materials represents a key step towards efficient large-scale treatment of seawater. Results regarding the effect of pore properties (surface texture, morphology, density, tortuosity) on desired performance characteristics such as ion selectivity, maximal water flux and energy requirements will be presented.
Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.
Directory of Open Access Journals (Sweden)
S. Salcedo-Sanz
2014-01-01
Full Text Available This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design. The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.
Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860
Globally optimal, minimum stored energy, double-doughnut superconducting magnets.
Tieng, Quang M; Vegh, Viktor; Brereton, Ian M
2010-01-01
The use of the minimum stored energy current density map-based methodology of designing closed-bore symmetric superconducting magnets was described recently. The technique is further developed to cater for the design of interventional-type MRI systems, and in particular open symmetric magnets of the double-doughnut configuration. This extends the work to multiple magnet domain configurations. The use of double-doughnut magnets in MRI scanners has previously been hindered by the ability to deliver strong magnetic fields over a sufficiently large volume appropriate for imaging, essentially limiting spatial resolution, signal-to-noise ratio, and field of view. The requirement of dedicated interventional space restricts the manner in which the coils can be arranged and placed. The minimum stored energy optimal coil arrangement ensures that the field strength is maximized over a specific region of imaging. The design method yields open, dual-domain magnets capable of delivering greater field strengths than those used prior to this work, and at the same time it provides an increase in the field-of-view volume. Simulation results are provided for 1-T double-doughnut magnets with at least a 50-cm 1-ppm (parts per million) field of view and 0.7-m gap between the two doughnuts. Copyright (c) 2009 Wiley-Liss, Inc.
Optimization of photovoltaic energy production through an efficient switching matrix
Directory of Open Access Journals (Sweden)
Pietro Romano
2013-09-01
Full Text Available This work presents a preliminary study on the implementation of a new system for power output maximization of photovoltaic generators under non-homogeneous conditions. The study evaluates the performance of an efficient switching matrix and the relevant automatic reconfiguration control algorithms. The switching matrix is installed between the PV generator and the inverter, allowing a large number of possible module configurations. PV generator, switching matrix and the intelligent controller have been simulated in Simulink. The proposed reconfiguration system improved the energy extracted by the PV generator under non-uniform solar irradiation conditions. Short calculation times of the proposed control algorithms allow its use in real time applications even where a higher number of PV modules is required.
Optimizing link efficiency for gated DPCCH transmission on HSUPA
DEFF Research Database (Denmark)
Zarco, Carlos Ruben Delgado; Wigard, Jeroen; Kolding, T. E.
2007-01-01
consider the E-DCH performance degradation caused by gating on other radio procedures relying on the DPCCH, such as inner and outer loop power control. Our studies show that gating is beneficial for both for 2 and 10 ms transmission time intervals. The gains in terms of LE with a Vehicular A 30 kmph......To minimize the terminal's transmission power in bursty uplink traffic conditions, the evolved High-Speed Uplink Packet Access (HSUPA) concept in 3GPP WCDMA includes a feature known as Dedicated Physical Control Channel (DPCCH) gating. We present here a detailed link level study of gating from...... a link efficiency (LE) perspective; LE being expressed in bits per second per Watt. While the overall gain mechanisms of gating are well known, we show how special challenges related to discontinuous Enhanced Dedicated Channel (E-DCH) transmission can be addressed for high link and system performance. We...
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Directory of Open Access Journals (Sweden)
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.
Efficient distribution of toy products using ant colony optimization algorithm
Hidayat, S.; Nurpraja, C. A.
2017-12-01
CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.
Optimizing Eco-Efficiency Across the Procurement Portfolio.
Pelton, Rylie E O; Li, Mo; Smith, Timothy M; Lyon, Thomas P
2016-06-07
Manufacturing organizations' environmental impacts are often attributable to processes in the firm's upstream supply chain. Environmentally preferable procurement (EPP) and the establishment of environmental purchasing criteria can potentially reduce these indirect impacts. Life-cycle assessment (LCA) can help identify the purchasing criteria that are most effective in reducing environmental impacts. However, the high costs of LCA and the problems associated with the comparability of results have limited efforts to integrate procurement performance with quantitative organizational environmental performance targets. Moreover, environmental purchasing criteria, when implemented, are often established on a product-by-product basis without consideration of other products in the procurement portfolio. We develop an approach that utilizes streamlined LCA methods, together with linear programming, to determine optimal portfolios of product impact-reduction opportunities under budget constraints. The approach is illustrated through a simulated breakfast cereal manufacturing firm procuring grain, containerboard boxes, plastic packaging, electricity, and industrial cleaning solutions. Results suggest that extending EPP decisions and resources to the portfolio level, recently made feasible through the methods illustrated herein, can provide substantially greater CO2e and water-depletion reductions per dollar spend than a product-by-product approach, creating opportunities for procurement organizations to participate in firm-wide environmental impact reduction targets.
Efficient optimal joint channel estimation and data detection for massive MIMO systems
Alshamary, Haider Ali Jasim
2016-08-15
In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.
European project HOPE (Health Optimization Protocol for Energy-efficient Buildings)
Bluyssen, P.M.; Cox, C.W.J.; Maroni, M.; Boschi, N.; Raw, G.; Roulet, C.A.; Foradini, F.
2003-01-01
In January 2002, a new European project named HOPE (Health Optimization Protocol for Energy-efficient Buildings) started with 14 participants from nine European countries. The final goal of the project is to provide the means to increase the number of energy-efficient buildings, i.e. buildings that
Thermodynamic bounds and general properties of optimal efficiency and power in linear responses.
Jiang, Jian-Hua
2014-10-01
We study the optimal exergy efficiency and power for thermodynamic systems with an Onsager-type "current-force" relationship describing the linear response to external influences. We derive, in analytic forms, the maximum efficiency and optimal efficiency for maximum power for a thermodynamic machine described by a N×N symmetric Onsager matrix with arbitrary integer N. The figure of merit is expressed in terms of the largest eigenvalue of the "coupling matrix" which is solely determined by the Onsager matrix. Some simple but general relationships between the power and efficiency at the conditions for (i) maximum efficiency and (ii) optimal efficiency for maximum power are obtained. We show how the second law of thermodynamics bounds the optimal efficiency and the Onsager matrix and relate those bounds together. The maximum power theorem (Jacobi's Law) is generalized to all thermodynamic machines with a symmetric Onsager matrix in the linear-response regime. We also discuss systems with an asymmetric Onsager matrix (such as systems under magnetic field) for a particular situation and we show that the reversible limit of efficiency can be reached at finite output power. Cooperative effects are found to improve the figure of merit significantly in systems with multiply cross-correlated responses. Application to example systems demonstrates that the theory is helpful in guiding the search for high performance materials and structures in energy researches.
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
Global optimization of fuel consumption in J2 rendezvous using interval analysis
Ma, Hongliang; Xu, Shijie; Liang, Yuying
2017-03-01
This paper addresses an open-time Lambert problem under first-order gravitational perturbations with unfixed parking time and transfer time. The perturbations are compensated by introducing its analytical solutions derived from Lagrange's planetary equations into Lambert problem. A drift vector of aim position correction is defined to reduce the aim position bias caused by the perturbations. The first purpose of optimization is to find sufficiently small intervals involving the global optimal parking time, transfer time, drift vector and velocity increment. The second is to determine the global solution or the solution close to it in these intervals. Interval analysis and a double-deck gradient-based method with GA estimating the initial range of drift vector are utilized to obtain the sufficiently small intervals including the global minimum velocity increment and the global minimum solution or one sufficiently close to it in these intervals.
Energy Technology Data Exchange (ETDEWEB)
Lim, Jong Min; Lee, Byung Chai; Lee, Ik Jin [KAIST, Daejeon (Korea, Republic of)
2015-04-15
This study develops an efficient and accurate methodology for reliability-based design optimization (RBDO) by combining the most probable point (MPP)-based dimension reduction method (DRM) to enhance accuracy and the sequential optimization and reliability assessment (SORA) to enhance efficiency. In many researches, first-order reliability method (FORM) has been utilized for RBDO methods due to its efficiency and simplicity. However, it might not be accurate enough for highly nonlinear performance functions. Therefore, the MPP-based DRM is introduced for the accurate reliability assessment in this study. Even though the MPP-based DRM significantly improves the accuracy, additional computations for the moment-based integration are required. It is desirable to reduce the number of reliability analyses in the RBDO process. Since decoupled approaches such as SORA reduce necessary reliability analyses considerably, DRM-based SORA is proposed in this study for accurate and efficient RBDO. Furthermore, convex linearization is introduced to approximate inactive probabilistic constraints to additionally improve the efficiency. The efficiency and accuracy of the proposed method are verified through numerical examples.
Efficient Capacity Computation and Power Optimization for Relay Networks
Parvaresh, Farzad
2011-01-01
The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power o...
Optimization of global model composed of radial basis functions using the term-ranking approach
Energy Technology Data Exchange (ETDEWEB)
Cai, Peng; Tao, Chao, E-mail: taochao@nju.edu.cn; Liu, Xiao-Jun [Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093 (China)
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
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...
A Filter-Filled Function Method for Global Optimization with Box Constraints
Ji, Hongxia; Tian, Zhiyuan; Gu, Yunxia
2017-09-01
A single parameter filled function for solving global optimization problems with box constraints is proposed and its properties are investigated. The filter technique avoids the difficulty of selecting the penalty parameter and has a good numerical results in the local optimization algorithm. The filter is introduced and combined with the filled function to form the filter-filled function algorithm in this paper. The numerical results illustrate that the algorithm is reliable and effective.
Game-Theoretic Rate-Distortion-Complexity Optimization of High Efficiency Video Coding
DEFF Research Database (Denmark)
Ukhanova, Ann; Milani, Simone; Forchhammer, Søren
2013-01-01
This paper presents an algorithm for rate-distortioncomplexity optimization for the emerging High Efficiency Video Coding (HEVC) standard, whose high computational requirements urge the need for low-complexity optimization algorithms. Optimization approaches need to specify different complexity...... profiles in order to tailor the computational load to the different hardware and power-supply resources of devices. In this work, we focus on optimizing the quantization parameter and partition depth in HEVC via a game-theoretic approach. The proposed rate control strategy alone provides 0.2 dB improvement...
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization
Directory of Open Access Journals (Sweden)
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.
Cano, Emilio L; Moguerza, Javier M; Alonso-Ayuso, Antonio
2015-12-01
Optimization instances relate to the input and output data stemming from optimization problems in general. Typically, an optimization problem consists of an objective function to be optimized (either minimized or maximized) and a set of constraints. Thus, objective and constraints are jointly a set of equations in the optimization model. Such equations are a combination of decision variables and known parameters, which are usually related to a set domain. When this combination is a linear combination, we are facing a classical Linear Programming (LP) problem. An optimization instance is related to an optimization model. We refer to that model as the Symbolic Model Specification (SMS) containing all the sets, variables, and parameters symbols and relations. Thus, a whole instance is composed by the SMS, the elements in each set, the data values for all the parameters, and, eventually, the optimal decisions resulting from the optimization solution. This data article contains several optimization instances from a real-world optimization problem relating to investment planning on energy efficient technologies at the building level.
Kumar, Rajesh; Kaushik, S C; Kumar, Raj
2015-01-01
Efficient power optimization of Brayton heat engine with variable specific heat of the working fluid is analyzed from the view of finite time thermodynamics. The efficient power is defined as the multiplication of engine power and engine efficiency. Hence, the proposed method considers not only the power output but also the engine efficiency. Optimizing the efficient power gives a compromise between power and engine efficiency. Results obtained are compared with those obtained by using the ma...
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
Xiangzhu He; Jida Huang; Yunqing Rao; Liang Gao
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 L?vy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functio...
A global optimization algorithm inspired in the behavior of selfish herds.
Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián
2017-10-01
In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.
Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods
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.
Efficient hybrid evolutionary algorithm for optimization of a strip coiling process
Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik
2015-04-01
This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.
Cooperative Co-evolution with Formula-based Variable Grouping for Large-Scale Global Optimization.
Wang, Yuping; Liu, Haiyan; Wei, Fei; Zong, Tingting; Li, Xiaodong
2017-08-09
For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered as an effective strategy to decompose the problem into smaller subproblems, each of which can be then solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real world problems are white-box problems, i.e., the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can be then used to design an effective variable group method. In this paper, a formulabased grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations " + ", " - ", " × ", " ÷ " and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in non-separable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being inter-dependent. FBG can be applied to any white-box problem easily and can be integrated into a cooperative co-evolution framework. Based on FBG, a novel cooperative co-evolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this paper for decomposing a large-scale white-box problem into several smaller sub-problems and optimizing them
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
Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron
2014-04-01
We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.
DEFF Research Database (Denmark)
Rasmussen, Marie-Louise Højlund; Stolpe, Mathias
2008-01-01
The subject of this article is solving discrete truss topology optimization problems with local stress and displacement constraints to global optimum. We consider a formulation based on the Simultaneous ANalysis and Design (SAND) approach. This intrinsically non-convex problem is reformulated to ...
Directory of Open Access Journals (Sweden)
Jui-Yu Wu
2012-01-01
Full Text Available This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO algorithm for solving 13 constrained global optimization (CGO problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.
Yamada, Ryosuke; Wakita, Kazuki; Mitsui, Ryosuke; Ogino, Hiroyasu
2017-09-01
Utilization of renewable feedstocks for the production of bio-based chemicals such as d-lactic acid by engineering metabolic pathways in the yeast Saccharomyces cerevisiae has recently become an attractive option. In this study, to realize efficient d-lactic acid production by S. cerevisiae, the expression of 12 glycolysis-related genes and the Leuconostoc mesenteroides d-LDH gene was optimized using a previously developed global metabolic engineering strategy, and repeated batch fermentation was carried out using the resultant strain YPH499/dPdA3-34/DLDH/1-18. Stable d-lactic acid production through 10 repeated batch fermentations was achieved using YPH499/dPdA3-34/DLDH/1-18. The average d-lactic acid production, productivity, and yield with 10 repeated batch fermentations were 60.3 g/L, 2.80 g/L/h, and 0.646, respectively. The present study is the first report of the application of a global metabolic engineering strategy for bio-based chemical production, and it shows the potential for efficient production of such chemicals by global metabolic engineering of the yeast S. cerevisiae. Biotechnol. Bioeng. 2017;114: 2075-2084. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
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.
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
Directory of Open Access Journals (Sweden)
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.
Global optimization of truss topology with discrete bar areas—Part I: Theory of relaxed problems
DEFF Research Database (Denmark)
Achtziger, Wolfgang; Stolpe, Mathias
2008-01-01
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...... of the paper together with Part II present an algorithmic framework for the calculation of a global optimizer of the underlying large-scaled mixed integer design problem. This framework is given by a convergent branch-and-bound algorithm which is based on solving a sequence of nonconvex continuous relaxations....... 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...
Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes
Directory of Open Access Journals (Sweden)
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.
Optimal efficiency vector control of induction motor drive system for drum washing machine
Lee, Won Cheol; Yu, Jae Sung; Jang, Bong An; Won, Chung Yuen
2005-12-01
In home appliances, electric energy is optimally controlled by using power electronics technology, creating a comfortable environment in terms of energy saving, low sound generation, and reduced time consumption. Usually simplicity and robustness make the three phase induction motor attractive for use in domestic appliance, including washing machines. Two main types of domestic washing machine have evolved. We focus on efficiency of the front loading machine favored in Europe, which has a horizontal drum axis. This paper presents the control algorithm for optimal efficiency drives of an induction motor for drum washing machine. This system uses a simple model of the induction motor that include equations of the iron losses. The proposed optimal efficiency control algorithm calculates commands of the reference torque and flux currents for the flux oriented control of the induction motor. The proposed algorithm is verified through digital simulation.
Energy Technology Data Exchange (ETDEWEB)
Yang, Ying-Ying, E-mail: xclin@semi.ac.cn, E-mail: yangyy@semi.ac.cn; Zhao, Ya-Ping; Wang, Li-Rong; Zhang, Ling; Lin, Xue-Chun, E-mail: xclin@semi.ac.cn, E-mail: yangyy@semi.ac.cn [Laboratory of All Solid State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083 (China)
2015-03-14
A highly efficient nano-periodical grating is theoretically investigated for spectral beam combining (SBC) and is experimentally implemented for attaining high-brightness laser from a diode laser array. The rigorous coupled-wave analysis with the S matrix method is employed to optimize the parameters of the grating. According the optimized parameters, the grating is fabricated and plays a key role in SBC cavity. The diffraction efficiency of this grating is optimized to 95% for the output laser which is emitted from the diode laser array. The beam parameter product of 3.8 mm mrad of the diode laser array after SBC is achieved at the output power of 46.3 W. The optical-to-optical efficiency of SBC cavity is measured to be 93.5% at the maximum operating current in the experiment.
THE HUNDRED BILLION DOLLAR BONUS: Global Energy Efficiency Lessons from India
Energy Technology Data Exchange (ETDEWEB)
Paul, Seema; Sathaye, Jayant
2011-03-01
At a time when India and other nations are grappling with myriad energy-related challenges, including unstable, costly power sources and growing greenhouse gas emissions, energy efficiency offers an alternative at a fraction of the cost of other new sources of energy. A consortium of leading Indian regulators, nongovernmental organizations, and international experts has recognized this opportunity and is working to develop effective policies that will bring significant domestic benefits to India while accelerating the global transition to energy efficiency.
Carretero, Juan A; Nahon, Meyer A
2005-12-01
Determining the minimum distance between convex objects is a problem that has been solved using many different approaches. On the other hand, computing the minimum distance between combinations of convex and concave objects is known to be a more complicated problem. Most methods propose to partition the concave object into convex subobjects and then solve the convex problem between all possible subobject combinations. This can add a large computational expense to the solution of the minimum distance problem. In this paper, an optimization-based approach is used to solve the concave problem without the need for partitioning concave objects into convex pieces. Since the optimization problem is no longer unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing (SA) and Genetic Algorithms (GAs) are used to solve the concave minimum distance problem. In order to reduce the computational expense, it is proposed to replace the objects' geometry by a set of points on the surface of each body. This reduces the problem to an unconstrained combinatorial optimization problem, where the combination of points (one on the surface of each body) that minimizes the distance will be the solution. Additionally, if the surface points are set as the nodes of a surface mesh, it is possible to accelerate the convergence of the global optimization algorithm by using a hill-climbing local optimization algorithm. Some examples using these novel approaches are presented.
Effects of upper body parameters on biped walking efficiency studied by dynamic optimization
Directory of Open Access Journals (Sweden)
Kang An
2016-12-01
Full Text Available Walking efficiency is one of the considerations for designing biped robots. This article uses the dynamic optimization method to study the effects of upper body parameters, including upper body length and mass, on walking efficiency. Two minimal actuations, hip joint torque and push-off impulse, are used in the walking model, and minimal constraints are set in a free search using the dynamic optimization. Results show that there is an optimal solution of upper body length for the efficient walking within a range of walking speed and step length. For short step length, walking with a lighter upper body mass is found to be more efficient and vice versa. It is also found that for higher speed locomotion, the increase of the upper body length and mass can make the walking gait optimal rather than other kind of gaits. In addition, the typical strategy of an optimal walking gait is that just actuating the swing leg at the beginning of the step.
Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio
2014-05-10
Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
An Efficient Approach for Solving Mesh Optimization Problems Using Newton’s Method
Directory of Open Access Journals (Sweden)
Jibum Kim
2014-01-01
Full Text Available We present an efficient approach for solving various mesh optimization problems. Our approach is based on Newton’s method, which uses both first-order (gradient and second-order (Hessian derivatives of the nonlinear objective function. The volume and surface mesh optimization algorithms are developed such that mesh validity and surface constraints are satisfied. We also propose several Hessian modification methods when the Hessian matrix is not positive definite. We demonstrate our approach by comparing our method with nonlinear conjugate gradient and steepest descent methods in terms of both efficiency and mesh quality.
The effects of energy efficiency improvement in China with global interaction
Directory of Open Access Journals (Sweden)
Solveig Glomsrød
2016-01-01
Full Text Available China has pledged to reduce its carbon intensity defined as carbon dioxide emissions per unit of GDP by 40–45% by 2020 and by 60–65% by 2030 compared to the 2005 level. To fulfill the pledges, China’s government has made energy efficiency its de facto climate policy. This article raises the question to what extent energy efficiency will be an efficient mitigation measure for reaching the targets as pledged by China to the UNFCCC. In this context, two issues blur the picture. One is the potential rebound effect, generally causing one percent improvement in energy efficiency to generate less than one percent reduction in energy-related emissions since users adapt to the direct and indirect productivity gains and cost reductions in energy use. Further, there is the impact on energy use in China from interaction with global markets, in which China has emerged as a dominant player. In the present paper, we study the net implications of energy efficiency improvement in China within alternative global climate policy regimes. Our results show that a one percent energy efficiency improvement in China reduces energy use by 0.38–0.59 percent per year depending on alternative international contexts. Hence, policy makers should consider climate policies adopted by the other regions such as carbon trading system when assessing the implications of energy efficiency for energy consumption and climate mitigation. Policy makers should also consider overlapping effects of alternative energy policies, as energy efficiency improvement might have no effect on energy and emission reduction if there is global carbon trade. However, policy makers can expect more reduction in energy use and emissions due to energy efficiency improvement in the new mechanism announced in the Paris Agreement at the COP21.
Two-scale cost efficiency optimization of 5G wireless backhaul networks
Ge, Xiaohu; Tu, Song; Mao, Guoqiang; Lau, Vincent K. N.; Pan, Linghui
2016-01-01
To cater for the demands of future fifth generation (5G) ultra-dense small cell networks, the wireless backhaul network is an attractive solution for the urban deployment of 5G wireless networks. Optimization of 5G wireless backhaul networks is a key issue. In this paper we propose a two-scale optimization solution to maximize the cost efficiency of 5G wireless backhaul networks. Specifically, the number and positions of gateways are optimized in the long time scale of 5G wireless backhaul ne...
A least squares approach for efficient and reliable short-term versus long-term optimization
DEFF Research Database (Denmark)
Christiansen, Lasse Hjuler; Capolei, Andrea; Jørgensen, John Bagterp
2017-01-01
to the Pareto front of optimal short-term and long-term trade-offs. However, such methods rely on a large number of reservoir simulations and scale poorly with the number of objectives subject to optimization. Consequently, the large-scale nature of production optimization severely limits applications to real...... the balance between the objectives, leaving an unfulfilled potential to increase profits. To promote efficient and reliable short-term versus long-term optimization, this paper introduces a natural way to characterize desirable Pareto points and proposes a novel least squares (LS) method. Unlike hierarchical...... approaches, the method is guaranteed to converge to a Pareto optimal point. Also, the LS method is designed to properly balance multiple objectives, independently of Pareto front’s shape. As such, the method poses a practical alternative to a posteriori methods in situations where the frontier is intractable...
Global energy efficiency improvement in the log term: a demand- and supply-side perspective
Graus, W.H.J.; Blomen, E.; Worrell, E.
2011-01-01
This study assessed technical potentials for energy efficiency improvement in 2050 in a global context. The reference scenario is based on the World Energy Outlook of the International Energy Agency 2007 edition and assumptions regarding gross domestic product developments after 2030. In the
Zheng, Jingjing; Frisch, Michael J
2017-12-12
An efficient geometry optimization algorithm based on interpolated potential energy surfaces with iteratively updated Hessians is presented in this work. At each step of geometry optimization (including both minimization and transition structure search), an interpolated potential energy surface is properly constructed by using the previously calculated information (energies, gradients, and Hessians/updated Hessians), and Hessians of the two latest geometries are updated in an iterative manner. The optimized minimum or transition structure on the interpolated surface is used for the starting geometry of the next geometry optimization step. The cost of searching the minimum or transition structure on the interpolated surface and iteratively updating Hessians is usually negligible compared with most electronic structure single gradient calculations. These interpolated potential energy surfaces are often better representations of the true potential energy surface in a broader range than a local quadratic approximation that is usually used in most geometry optimization algorithms. Tests on a series of large and floppy molecules and transition structures both in gas phase and in solutions show that the new algorithm can significantly improve the optimization efficiency by using the iteratively updated Hessians and optimizations on interpolated surfaces.
Zhang, Chi; Liu, Li-wei; Wang, Long-Fei; Yue, Yuan; Yu, Lian-Chun
2015-01-01
Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this paper, we calculated the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculated the mutual information, energy cost, and energy efficiency of an array of these bistable units. We found that the optimal number of units c...
An optimized efficient dual junction InGaN/CIGS solar cell: A numerical simulation
Farhadi, Bita; Naseri, Mosayeb
2016-08-01
The photovoltaic performance of an efficient double junction InGaN/CIGS solar cell including a CdS antireflector top cover layer is studied using Silvaco ATLAS software. In this study, to gain a desired structure, the different design parameters, including the CIGS various band gaps, the doping concentration and the thickness of CdS layer are optimized. The simulation indicates that under current matching condition, an optimum efficiency of 40.42% is achieved.
How to assess the Efficiency and "Uncertainty" of Global Sensitivity Analysis?
Haghnegahdar, Amin; Razavi, Saman
2016-04-01
Sensitivity analysis (SA) is an important paradigm for understanding model behavior, characterizing uncertainty, improving model calibration, etc. Conventional "global" SA (GSA) approaches are rooted in different philosophies, resulting in different and sometime conflicting and/or counter-intuitive assessment of sensitivity. Moreover, most global sensitivity techniques are highly computationally demanding to be able to generate robust and stable sensitivity metrics over the entire model response surface. Accordingly, a novel sensitivity analysis method called Variogram Analysis of Response Surfaces (VARS) is introduced to overcome the aforementioned issues. VARS uses the Variogram concept to efficiently provide a comprehensive assessment of global sensitivity across a range of scales within the parameter space. Based on the VARS principles, in this study we present innovative ideas to assess (1) the efficiency of GSA algorithms and (2) the level of confidence we can assign to a sensitivity assessment. We use multiple hydrological models with different levels of complexity to explain the new ideas.
Zhang, Xulong; Gan, Chenquan
2018-01-01
This paper aims to study the combined impact of countermeasure and network topology on virus diffusion and optimal dynamic countermeasure. A novel heterogenous propagation model and its optimal control problem are proposed and analyzed. Qualitative analysis shows that the unique equilibrium of the proposed model is globally attractive and the optimal control problem has an optimal control. Some simulation experiments are also performed. Specifically, it is found that our obtained results are contrary to some previous results and countermeasure dissemination to higher-degree nodes is more effective than that to lower-degree nodes. The related explanations are also made. This indicates that countermeasures and network topology play an important role in suppressing viral spread.
Directory of Open Access Journals (Sweden)
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.
Energy Technology Data Exchange (ETDEWEB)
Rattá, G.A., E-mail: giuseppe.ratta@ciemat.es [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Vega, J. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Murari, A. [Consorzio RFX, Associazione EURATOM/ENEA per la Fusione, Padua (Italy); Dormido-Canto, S. [Dpto. de Informática y Automática, Universidad Nacional de Educación a Distancia, Madrid (Spain); Moreno, R. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain)
2016-11-15
Highlights: • A global optimization method based on genetic algorithms was developed. • It allowed improving the prediction of disruptions using APODIS architecture. • It also provides the potential opportunity to develop a spectrum of future predictors using different training datasets. • The future analysis of how their structures reassemble and evolve in each test may help to improve the development of disruption predictors for ITER. - Abstract: Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features, quantity of models, their characteristics and internal parameters. This global optimization targets the creation of the best possible system with a reduced amount of required training data. The results harbor no doubts about the reliability of the global optimization method, allowing to outperform the ones of previous versions: 91.77% of predictions (89.24% with an anticipation higher than 10 ms) with a 3.55% of false alarms. Beyond its effectiveness, it also provides the potential opportunity to develop a spectrum of future predictors using different training datasets.
Gao, David Yang
2009-01-01
Written by some of the leading experts in complementarity, duality, global optimization, and quantum computations, this collection reveals the beauty of these mathematical disciplines. It investigates the developments in global optimization, nonconvex and nonsmooth analysis, nonlinear programming, and theoretical and engineering mechanics
An efficient and practical approach to obtain a better optimum solution for structural optimization
Chen, Ting-Yu; Huang, Jyun-Hao
2013-08-01
For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.
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.
Optimization of Multi-layer Active Magnetic Regenerator towards Compact and Efficient Refrigeration
DEFF Research Database (Denmark)
Lei, Tian; Engelbrecht, Kurt; Nielsen, Kaspar Kirstein
2016-01-01
Magnetic refrigerators can theoretically be more efficient than current vapor compression systems and use no vapor refrigerants with global warming potential. The core component, the active magnetic regenerator (AMR) operates based on the magnetocaloric effect of magnetic materials and the heat...
Study on route division for ship energy efficiency optimization based on big environment data
Wang, K.; Yan, Xinping; Yuan, Yupeng; Jiang, X.; Lodewijks, G.; Negenborn, R.R.; Ma, Weiming
2017-01-01
In the case of the global energy crisis and the higher sound of energy saving and emission reduction, how to take effective management measures of ship energy efficiency to achieve the goal of energy saving and emission reduction, put forward a new challenge for the development of shipping
Optimization of Multi-layer Active Magnetic Regenerator towards Compact and Efficient Refrigeration
DEFF Research Database (Denmark)
Lei, Tian; Engelbrecht, Kurt; Nielsen, Kaspar Kirstein
2016-01-01
Magnetic refrigerators can theoretically be more efficient than current vapor compression systems and use no vapor refrigerants with global warming potential. The core component, the active magnetic regenerator (AMR) operates based on the magnetocaloric effect of magnetic materials and the heat r....... In addition, simulations are carried out to investigate the potential of applying nanofluid in future magnetic refrigerators....
An efficient simulation-optimization coupling for management of coastal aquifers
Kourakos, George; Mantoglou, Aristotelis
2015-09-01
Seawater intrusion in coastal aquifers is a major environmental problem and efficient tools are needed to assist decision making. Decision tools are often simulation models (which evaluate probable actions) combined with optimization algorithms (which search for optimum management decisions). A coupling between simulation models and optimization algorithms for management of coastal aquifers is presented. The simulation models are based on the sharp-interface approximation where the decision variables do not affect the discretized system matrix. For such problems, a transformation of the system matrix prior to optimization is proposed which supports rapid solution of the linear system of equations during the optimization stage, for different values of decision variables. The method is applied to a hypothetical simulation of a coastal aquifer on the Greek island of Santorini, where the proposed simulation-optimization coupling method is employed to maximize pumping rates subject to environmental constraints that protect the aquifer from seawater intrusion. Various packages were tested in order to investigate their efficiency in solving the linear system pertinent to the case study. The proposed method, based on coupling of equations, is found to be very efficient in terms of computational cost. In particular, for the problem examined, it is at least 50 times faster than standard methods, depending on the grid size.
Efficient Solutions and Cost-Optimal Analysis for Existing School Buildings
Directory of Open Access Journals (Sweden)
Paolo Maria Congedo
2016-10-01
Full Text Available The recast of the energy performance of buildings directive (EPBD describes a comparative methodological framework to promote energy efficiency and establish minimum energy performance requirements in buildings at the lowest costs. The aim of the cost-optimal methodology is to foster the achievement of nearly zero energy buildings (nZEBs, the new target for all new buildings by 2020, characterized by a high performance with a low energy requirement almost covered by renewable sources. The paper presents the results of the application of the cost-optimal methodology in two existing buildings located in the Mediterranean area. These buildings are a kindergarten and a nursery school that differ in construction period, materials and systems. Several combinations of measures have been applied to derive cost-effective efficient solutions for retrofitting. The cost-optimal level has been identified for each building and the best performing solutions have been selected considering both a financial and a macroeconomic analysis. The results illustrate the suitability of the methodology to assess cost-optimality and energy efficiency in school building refurbishment. The research shows the variants providing the most cost-effective balance between costs and energy saving. The cost-optimal solution reduces primary energy consumption by 85% and gas emissions by 82%–83% in each reference building.
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.
Optimization of the efficiency of a biomimetic marine propulsor using CFD
Directory of Open Access Journals (Sweden)
Carlos Gervasio Rodríguez Vidal
2014-03-01
Full Text Available The purpose of this paper is to employ a CFD (Computational Fluid Dynamics procedure to optimize a biomimetic marine propulsor. This propulsor is based on an undulating panel which emulates the movement of a fin fish. The numerical model has been employed to analyze the hydrodynamics and improve the efficiency. Particularly, the fin shape has been studied as measure to improve the efficiency. Three fin shapes have been analyzed, rectangular, elliptic and lunate. The results have indicated that, for the same area, the lunate shape is the most efficient.
Efficiency Optimization for Disassembly Tools via Using NN-GA Approach
Directory of Open Access Journals (Sweden)
Guangdong Tian
2013-01-01
Full Text Available Disassembly issues have been widely attracted in today’s sustainable development context. One of them is the selection of disassembly tools and their efficiency comparison. To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process. Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN and genetic algorithm (GA. Their efficiency comparison is discussed. Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.
Optimization of E-DCH channel power ratios to maximize link level efficiency
DEFF Research Database (Denmark)
Zarco, Carlos Ruben Delgado; Malone, Jaime Tito; Wigard, Jeroen
2006-01-01
For the WCDMA/HSUPA concept, a key to ensuring high spectral efficiency is to correctly adjust the transmission power ratios among the data and control channels. This paper provides optimal values for the power ratio between the Enhanced-Dedicated Physical Data Channel (E-DPDCH) and the Dedicated...
Optimization of the multi-turn injection efficiency for a medical synchrotron
Kim, J.; Yoon, M.; Yim, H.
2016-09-01
We present a method for optimizing the multi-turn injection efficiency for a medical synchrotron. We show that for a given injection energy, the injection efficiency can be greatly enhanced by choosing transverse tunes appropriately and by optimizing the injection bump and the number of turns required for beam injection. We verify our study by applying the method to the Korea Heavy Ion Medical Accelerator (KHIMA) synchrotron which is currently being built at the campus of Dongnam Institute of Radiological and Medical Sciences (DIRAMS) in Busan, Korea. First the frequency map analysis was performed with the help of the ELEGANT and the ACCSIM codes. The tunes that yielded good injection efficiency were then selected. With these tunes, the injection bump and the number of turns required for injection were then optimized by tracking a number of particles for up to one thousand turns after injection, beyond which no further beam loss occurred. Results for the optimization of the injection efficiency for proton ions are presented.
Application of an Efficient Gradient-Based Optimization Strategy for Aircraft Wing Structures
Directory of Open Access Journals (Sweden)
Odeh Dababneh
2018-01-01
Full Text Available In this paper, a practical optimization framework and enhanced strategy within an industrial setting are proposed for solving large-scale structural optimization problems in aerospace. The goal is to eliminate the difficulties associated with optimization problems, which are mostly nonlinear with numerous mixed continuous-discrete design variables. Particular emphasis is placed on generating good initial starting points for the search process and in finding a feasible optimum solution or improving the chances of finding a better optimum solution when traditional techniques and methods have failed. The efficiency and reliability of the proposed strategy were demonstrated through the weight optimization of different metallic and composite laminated wingbox structures. The results show the effectiveness of the proposed procedures in finding an optimized solution for high-dimensional search space cases with a given level of accuracy and reasonable computational resources and user efforts. Conclusions are also inferred with regards to the sensitivity of the optimization results obtained with respect to the choice of different starting values for the design variables, as well as different optimization algorithms in the optimization process.
Directory of Open Access Journals (Sweden)
Mitsuhiro eHayashibe
2014-02-01
Full Text Available A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works. Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system. In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics. We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques. We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions. Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is minimized down to 12% for no load at hand, 16% for a 0.5kg load condition. The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost
Optimal Strategy of Efficiency Power Plant with Battery Electric Vehicle in Distribution Network
Ma, Tao; Su, Su; Li, Shunxin; Wang, Wei; Yang, Tiantian; Li, Mengjuan; Ota, Yutaka
2017-05-01
With the popularity of electric vehicles (EVs), such as plug-in electric vehicles (PHEVs) and battery electric vehicles (BEVs), an optimal strategy for the coordination of BEVs charging is proposed in this paper. The proposed approach incorporates the random behaviours and regular behaviours of BEV drivers in urban environment. These behaviours lead to the stochastic nature of the charging demand. The optimal strategy is used to guide the coordinated charging at different time to maximize the efficiency of virtual power plant (VPP). An innovative peer-to-peer system is used with BEVs to achieve the goals. The actual behaviours of vehicles in a campus is used to validate the proposed approach, and the simulation results show that the optimal strategy can not only maximize the utilization ratio of efficiency power plant, but also do not need additional energies from distribution grid.
The efficient and economic design of PEM fuel cell systems by multi-objective optimization
Na, Woonki; Gou, Bei
Since the efficiency of fuel cells is the ratio of the electrical power output and the fuel input, it is a function of power density, system pressure, and stoichiometric ratios of hydrogen and oxygen. Typically, the fuel cell efficiency decreases as its power output increases. In order for the fuel cell system to obtain highly efficient operation with the same power generation, more cells and other auxiliaries such as a high-capacity compressor system, etc. are required. In other words, fuel cell efficiency is closely related to fuel cell economics. Therefore, an optimum efficiency should exist and should result in the definition of a cost-effective fuel cell system. Using a multi-objective optimization technique, the sequential quadratic programming (SQP) method, the efficiency and cost of a fuel cell system have been optimized under various operating conditions. This paper has obtained some analytical results that provide a useful suggestion for the design of a cost-effective fuel cell system with high operation efficiency.
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-12-21
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.
Directory of Open Access Journals (Sweden)
Z. Novacek
2009-04-01
Full Text Available This paper deals with a method of the radiation pattern determination of the directional antennas. The method combining both the functional minimization method and the Fourier iterative algorithm is based on the phaseless near-field measurement on two plane surfaces. The method is used for a reconstruction of the phase distribution on the aperture of the measured antenna, and for the determination of the antenna radiation pattern, consequently. The binary genetic algorithm (BGA, the realvalued genetic algorithm (RVGA, the particle swarm optimization (PSO, and differential evolutionary algorithm (DEA were chosen for the global functional minimization. The paper is aimed to analyze the performance of the global optimizations (GOs when solving the described problem, and to compare the GOs. GOs were examined through data achieved by measurement of a horn antenna and a parabola.
Deposition efficiency optimization in cold spraying of metal-ceramic powder mixtures
Klinkov, S. V.; Kosarev, V. F.
2017-10-01
In the present paper, results of optimization of the cold spray deposition process of a metal-ceramic powder mixture involving impacts of ceramic particles onto coating surface are reported. In the optimization study, a two-probability model was used to take into account the surface activation induced by the ceramic component of the mixture. The dependence of mixture deposition efficiency on the concentration and size of ceramic particles was analysed to identify the ranges of both parameters in which the effect due to ceramic particles on the mixture deposition efficiency was positive. The dependences of the optimum size and concentration of ceramic particles, and also the maximum gain in deposition efficiency, on the probability of adhesion of metal particles to non-activated coating surface were obtained.
Design of articulated mechanisms with a degree of freedom constraint using global optimization
DEFF Research Database (Denmark)
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 that...... 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....
Decoupled CFD-based optimization of efficiency and cavitation performance of a double-suction pump
Škerlavaj, A.; Morgut, M.; Jošt, D.; Nobile, E.
2017-04-01
In this study the impeller geometry of a double-suction pump ensuring the best performances in terms of hydraulic efficiency and reluctance of cavitation is determined using an optimization strategy, which was driven by means of the modeFRONTIER optimization platform. The different impeller shapes (designs) are modified according to the optimization parameters and tested with a computational fluid dynamics (CFD) software, namely ANSYS CFX. The simulations are performed using a decoupled approach, where only the impeller domain region is numerically investigated for computational convenience. The flow losses in the volute are estimated on the base of the velocity distribution at the impeller outlet. The best designs are then validated considering the computationally more expensive full geometry CFD model. The overall results show that the proposed approach is suitable for quick impeller shape optimization.
Efficient design of a truss beam by applying first order optimization method
Fedorik, Filip
2013-10-01
Applications of optimization procedures in structural designs are widely discussed problems, which are caused by currently still-increasing demands on structures. Using of optimization methods in efficient designs passes through great development, especially in duplicate production where even small savings might lead to considerable reduction of total costs. The presented paper deals with application and analysis of the First Order optimization technique, which is implemented in the Design Optimization module that uses the main features of multi-physical FEM program ANSYS, in steel truss-beam design. Constraints of the design are stated by EN 1993 Eurocode 3, for uniform compression forces in compression members and tensile resistance moments in tension members. Furthermore, a minimum frequency of the first natural modal shape of the structure is determined. The aim of the solution is minimizing the weight of the structure by changing members' cross-section properties.
Gradient-based optimization for efficient exposure planning in maskless lithography
Ghalehbeygi, Omid Tayefeh; Wills, Adrian G.; Routley, Ben S.; Fleming, Andrew J.
2017-07-01
Scanning laser lithography is a maskless method for exposing photoresist during semiconductor manufacturing. In this method, the energy of a focused beam is controlled while scanning the beam or substrate. With a positive photoresist material, areas that receive an exposure dosage over the threshold energy are dissolved during development. The surface dosage is related to the exposure profile by a convolution and nonlinear function, so the optimal exposure profile is nontrivial. A gradient-based optimization method for determining an optimal exposure profile, given the desired pattern and models of the beam profile and photochemistry, is described. This approach is more numerically efficient than optimal barrier-function-based methods but provides near-identical results. This is demonstrated through simulation and experimental lithography.
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.
Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing
2017-07-19
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.
Directory of Open Access Journals (Sweden)
Yuchao Chang
2017-07-01
Full Text Available Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs. Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH, hybrid energy-efficient distributed clustering (HEED, genetic protocol-based self-organizing network clustering (GASONeC, and double cost function-based routing (DCFR algorithms.
Directory of Open Access Journals (Sweden)
Ali Wagdy Mohamed
2017-01-01
Full Text Available This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.
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.
Xing, Qianhe; Li, Shuang; Fan, Xueliang; Bian, Anhua; Cao, Shi-Jie; Li, Cheng
2017-09-01
Graphene thermoacoustic loudspeakers, composed of a graphene film on a substrate, generate sound with heat. Improving thermoacoustic efficiency of graphene speakers is a goal for optimal design. In this work, we first modified the existing TA model with respect to small thermal wavelengths, and then built an acoustic platform for model validation. Additionally, sensitivity analyses for influential factors on thermoacoustic efficiency were performed, including the thickness of multilayered graphene films, the thermal effusivity of substrates, and the characteristics of inserted gases. The higher sensitivity coefficients result in the stronger effects on thermoacoustic efficiency. We find that the thickness (5 nm-15 nm) of graphene films plays a trivial role in efficiency, resulting in the sensitivity coefficient less than 0.02. The substrate thermal effusivity, however, has significant effects on efficiency, with the sensitivity coefficient around 1.7. Moreover, substrates with a lower thermal effusivity show better acoustic performances. For influences of ambient gases, the sensitivity coefficients of density ρg, thermal conductivity κg, and specific heat cp,g are 2.7, 0.98, and 0.8, respectively. Furthermore, large magnitudes of both ρg and κg lead to a higher efficiency and the sound pressure level generated by graphene films is approximately proportional to the inverse of cp,g. These findings can refer to the optimal design for graphene thermoacoustic speakers.
Xu, Gang; Li, Ming; Mourrain, Bernard; Rabczuk, Timon; Xu, Jinlan; Bordas, Stéphane P. A.
2018-01-01
In this paper, we propose a general framework for constructing IGA-suitable planar B-spline parameterizations from given complex CAD boundaries consisting of a set of B-spline curves. Instead of forming the computational domain by a simple boundary, planar domains with high genus and more complex boundary curves are considered. Firstly, some pre-processing operations including B\\'ezier extraction and subdivision are performed on each boundary curve in order to generate a high-quality planar parameterization; then a robust planar domain partition framework is proposed to construct high-quality patch-meshing results with few singularities from the discrete boundary formed by connecting the end points of the resulting boundary segments. After the topology information generation of quadrilateral decomposition, the optimal placement of interior B\\'ezier curves corresponding to the interior edges of the quadrangulation is constructed by a global optimization method to achieve a patch-partition with high quality. Finally, after the imposition of C1=G1-continuity constraints on the interface of neighboring B\\'ezier patches with respect to each quad in the quadrangulation, the high-quality B\\'ezier patch parameterization is obtained by a C1-constrained local optimization method to achieve uniform and orthogonal iso-parametric structures while keeping the continuity conditions between patches. The efficiency and robustness of the proposed method are demonstrated by several examples which are compared to results obtained by the skeleton-based parameterization approach.
Multiple tube structure for heating uniformity and efficiency optimization of microwave ovens
Zhou, Rong; Yang, Xiaoqing; Sun, Di; Jia, Guozhu
2015-02-01
Microwave heating is widely applied to microwave assisted chemical reactions in modified domestic microwave ovens, however, the potential issues (non-uniformity and low heating efficiency) still exist during the heating process. In this paper, a new heating model of multiple tube structure is proposed and the relevant simulations and experiments of heating water were performed based on the computational platform COMSOL Multi-physics software in order to achieve the better temperature uniformity and heating efficiency. Besides, the influence of the instability of microwave ovens on the heating performances of the optimal heating models was analyzed. The simulation results show that the heating uniformity and efficiency of water in optimal six tube structure increased by 7.1% and 68.5% (30 mL), 9.2% and 61% (60 mL) respectively compared with the optimal single tube structure. Moreover, the heating performances of the optimal heating models do not change obviously, while the working frequency and power change slightly. The simulation results are in good agreement with the experiment data.
Homann, Stefanie; Hofmann, Christian; Gorin, Aleksandr M.; Nguyen, Huy Cong Xuan; Huynh, Diana; Hamid, Phillip; Maithel, Neil; Yacoubian, Vahe; Mu, Wenli; Kossyvakis, Athanasios; Sen Roy, Shubhendu; Yang, Otto Orlean
2017-01-01
Transfection is one of the most frequently used techniques in molecular biology that is also applicable for gene therapy studies in humans. One of the biggest challenges to investigate the protein function and interaction in gene therapy studies is to have reliable monospecific detection reagents, particularly antibodies, for all human gene products. Thus, a reliable method that can optimize transfection efficiency based on not only expression of the target protein of interest but also the uptake of the nucleic acid plasmid, can be an important tool in molecular biology. Here, we present a simple, rapid and robust flow cytometric method that can be used as a tool to optimize transfection efficiency at the single cell level while overcoming limitations of prior established methods that quantify transfection efficiency. By using optimized ratios of transfection reagent and a nucleic acid (DNA or RNA) vector directly labeled with a fluorochrome, this method can be used as a tool to simultaneously quantify cellular toxicity of different transfection reagents, the amount of nucleic acid plasmid that cells have taken up during transfection as well as the amount of the encoded expressed protein. Finally, we demonstrate that this method is reproducible, can be standardized and can reliably and rapidly quantify transfection efficiency, reducing assay costs and increasing throughput while increasing data robustness. PMID:28863132
Directory of Open Access Journals (Sweden)
Lim, C. H.
2007-01-01
Full Text Available Production of Lactobacillus salivarius i 24, a probiotic strain for chicken, was studied in batch fermentation using 500 mL Erlenmeyer flask. Response surface method (RSM was used to optimize the medium for efficient cultivation of the bacterium. The factors investigated were yeast extract, glucose and initial culture pH. A polynomial regression model with cubic and quartic terms was used for the analysis of the experimental data. Estimated optimal conditions of the factors for growth of L. salivarius i 24 were; 3.32 % (w/v glucose, 4.31 % (w/v yeast extract and initial culture pH of 6.10.
Directory of Open Access Journals (Sweden)
Liang Tang
2010-01-01
Full Text Available A mathematical model for M/G/1-type queueing networks with multiple user applications and limited resources is established. The goal is to develop a dynamic distributed algorithm for this model, which supports all data traffic as efficiently as possible and makes optimally fair decisions about how to minimize the network performance cost. An online policy gradient optimization algorithm based on a single sample path is provided to avoid suffering from a “curse of dimensionality”. The asymptotic convergence properties of this algorithm are proved. Numerical examples provide valuable insights for bridging mathematical theory with engineering practice.
Fast Generation of Near-Optimal Plans for Eco-Efficient Stowage of Large Container Vessels
DEFF Research Database (Denmark)
Pacino, Dario; Jensen, Rune Møller; Delgado-Ortegon, Alberto
2011-01-01
Eco-Efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills...... industrial time and quality requirements. Our approach combines an integer programming model for assigning groups of containers to storage areas of the vessel over multiple ports, and a constraint programming and local search procedure for stowing individual containers....
Contrasting responses of water use efficiency to drought across global terrestrial ecosystems
Yang, Yuting; Guan, Huade; Batelaan, Okke; McVicar, Tim R.; Long, Di; Piao, Shilong; Liang, Wei; Liu, Bing; Jin, Zhao; Simmons, Craig T.
2016-01-01
Drought is an intermittent disturbance of the water cycle that profoundly affects the terrestrial carbon cycle. However, the response of the coupled water and carbon cycles to drought and the underlying mechanisms remain unclear. Here we provide the first global synthesis of the drought effect on ecosystem water use efficiency (WUE?=?gross primary production (GPP)/evapotranspiration (ET)). Using two observational WUE datasets (i.e., eddy-covariance measurements at 95 sites (526 site-years) an...
Ramrakhyani, A K; Mirabbasi, S; Mu Chiao
2011-02-01
Resonance-based wireless power delivery is an efficient technique to transfer power over a relatively long distance. This technique typically uses four coils as opposed to two coils used in conventional inductive links. In the four-coil system, the adverse effects of a low coupling coefficient between primary and secondary coils are compensated by using high-quality (Q) factor coils, and the efficiency of the system is improved. Unlike its two-coil counterpart, the efficiency profile of the power transfer is not a monotonically decreasing function of the operating distance and is less sensitive to changes in the distance between the primary and secondary coils. A four-coil energy transfer system can be optimized to provide maximum efficiency at a given operating distance. We have analyzed the four-coil energy transfer systems and outlined the effect of design parameters on power-transfer efficiency. Design steps to obtain the efficient power-transfer system are presented and a design example is provided. A proof-of-concept prototype system is implemented and confirms the validity of the proposed analysis and design techniques. In the prototype system, for a power-link frequency of 700 kHz and a coil distance range of 10 to 20 mm, using a 22-mm diameter implantable coil resonance-based system shows a power-transfer efficiency of more than 80% with an enhanced operating range compared to ~40% efficiency achieved by a conventional two-coil system.
Energy Technology Data Exchange (ETDEWEB)
Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Castro, Joseph Pete Jr. (; .); Giunta, Anthony Andrew
2006-01-01
Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and
Energy Efficiency Optimizing Based on Characteristics of Machine Learning in Cloud Computing
Directory of Open Access Journals (Sweden)
Cai Xiao-Bo
2017-01-01
Full Text Available Energy efficiency is one of the most important issues for large-scale server systems in current cloud computing. the main method about the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. However, there still exist several main challenges about Energy efficiency due to the complexities of real cloud computing application scene. The paper adopts machine learning theory to save energy consumption by decrease redundant computation for high energy-efficiency cloud computing environment. give the typical k-means and Page Rank applications, the Experiments show that the presented algorithm can save power consumption apparently. The research combines the machine learning theory and distributed technology, and presents a creative way to challenged problems in energy-efficiency cloud.
Gunnels, John
2010-06-01
We provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.
DEFF Research Database (Denmark)
Åkerström, Thorbjörn; Vedel, Kenneth; Needham Andersen, Josefine
2015-01-01
Transfection of rat skeletal muscle in vivo is a widely used research model. However, gene electrotransfer protocols have been developed for mice and yield variable results in rats. We investigated whether changes in hyaluronidase pre-treatment and plasmid DNA delivery can improve transfection...... efficiency in rat skeletal muscle. We found that pre-treating the muscle with a hyaluronidase dose suitable for rats (0.56. U/g b.w.) prior to plasmid DNA injection increased transfection efficiency by >200% whereas timing of the pre-treatment did not affect efficiency. Uniformly distributing plasmid DNA...... delivery across the muscle by increasing the number of plasmid DNA injections further enhanced transfection efficiency whereas increasing plasmid dose from 0.2 to 1.6. μg/g b.w. or vehicle volume had no effect. The optimized protocol resulted in ~80% (CI95%: 79-84%) transfected muscle fibers...
Energy efficiency as a unifying principle for human, environmental, and global health
Fontana, Luigi; Atella, Vincenzo; Kammen, Daniel M
2013-01-01
A strong analogy exists between over/under consumption of energy at the level of the human body and of the industrial metabolism of humanity. Both forms of energy consumption have profound implications for human, environmental, and global health. Globally, excessive fossil-fuel consumption, and individually, excessive food energy consumption are both responsible for a series of interrelated detrimental effects, including global warming, extreme weather conditions, damage to ecosystems, loss of biodiversity, widespread pollution, obesity, cancer, chronic respiratory disease, and other lethal chronic diseases. In contrast, data show that the efficient use of energy—in the form of food as well as fossil fuels and other resources—is vital for promoting human, environmental, and planetary health and sustainable economic development. While it is not new to highlight how efficient use of energy and food can address some of the key problems our world is facing, little research and no unifying framework exists to harmonize these concepts of sustainable system management across diverse scientific fields into a single theoretical body. Insights beyond reductionist views of efficiency are needed to encourage integrated changes in the use of the world’s natural resources, with the aim of achieving a wiser use of energy, better farming systems, and healthier dietary habits. This perspective highlights a range of scientific-based opportunities for cost-effective pro-growth and pro-health policies while using less energy and natural resources. PMID:24555053
Energy efficiency as a unifying principle for human, environmental, and global health.
Fontana, Luigi; Atella, Vincenzo; Kammen, Daniel M
2013-01-01
A strong analogy exists between over/under consumption of energy at the level of the human body and of the industrial metabolism of humanity. Both forms of energy consumption have profound implications for human, environmental, and global health. Globally, excessive fossil-fuel consumption, and individually, excessive food energy consumption are both responsible for a series of interrelated detrimental effects, including global warming, extreme weather conditions, damage to ecosystems, loss of biodiversity, widespread pollution, obesity, cancer, chronic respiratory disease, and other lethal chronic diseases. In contrast, data show that the efficient use of energy-in the form of food as well as fossil fuels and other resources-is vital for promoting human, environmental, and planetary health and sustainable economic development. While it is not new to highlight how efficient use of energy and food can address some of the key problems our world is facing, little research and no unifying framework exists to harmonize these concepts of sustainable system management across diverse scientific fields into a single theoretical body. Insights beyond reductionist views of efficiency are needed to encourage integrated changes in the use of the world's natural resources, with the aim of achieving a wiser use of energy, better farming systems, and healthier dietary habits. This perspective highlights a range of scientific-based opportunities for cost-effective pro-growth and pro-health policies while using less energy and natural resources.
Role and potential of renewable energy and energy efficiency for global energy supply
Energy Technology Data Exchange (ETDEWEB)
Krewitt, Wolfram; Nienhaus, Kristina [German Aerospace Center e.V. (DLR), Stuttgart (Germany); Klessmann, Corinna; Capone, Carolin; Stricker, Eva [Ecofys Germany GmbH, Berlin (Germany); Graus, Wina; Hoogwijk, Monique [Ecofys Netherlands BV, Utrecht (Netherlands); Supersberger, Nikolaus; Winterfeld, Uta von; Samadi, Sascha [Wuppertal Institute for Climate, Environment and Energy GmbH, Wuppertal (Germany)
2009-12-15
The analysis of different global energy scenarios in part I of the report confirms that the exploitation of energy efficiency potentials and the use of renewable energies play a key role in reaching global CO2 reduction targets. An assessment on the basis of a broad literature research in part II shows that the technical potentials of renewable energy technologies are a multiple of today's global final energy consumption. The analysis of cost estimates for renewable electricity generation technologies and even long term cost projections across the key studies in part III demonstrates that assumptions are in reasonable agreement. In part IV it is shown that by implementing technical potentials for energy efficiency improvements in demand and supply sectors by 2050 can be limited to 48% of primary energy supply in IEA's ''Energy Technology Perspectives'' baseline scenario. It was found that a large potential for cost-effective measures exists, equivalent to around 55-60% of energy savings of all included efficiency measures (part V). The results of the analysis on behavioural changes in part VI show that behavioural dimensions are not sufficiently included in energy scenarios. Accordingly major research challenges are revealed. (orig.)
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.
Dynamic water allocation policies improve the global efficiency of storage systems
Niayifar, Amin; Perona, Paolo
2017-06-01
Water impoundment by dams strongly affects the river natural flow regime, its attributes and the related ecosystem biodiversity. Fostering the sustainability of water uses e.g., hydropower systems thus implies searching for innovative operational policies able to generate Dynamic Environmental Flows (DEF) that mimic natural flow variability. The objective of this study is to propose a Direct Policy Search (DPS) framework based on defining dynamic flow release rules to improve the global efficiency of storage systems. The water allocation policies proposed for dammed systems are an extension of previously developed flow redistribution rules for small hydropower plants by Razurel et al. (2016).The mathematical form of the Fermi-Dirac statistical distribution applied to lake equations for the stored water in the dam is used to formulate non-proportional redistribution rules that partition the flow for energy production and environmental use. While energy production is computed from technical data, riverine ecological benefits associated with DEF are computed by integrating the Weighted Usable Area (WUA) for fishes with Richter's hydrological indicators. Then, multiobjective evolutionary algorithms (MOEAs) are applied to build ecological versus economic efficiency plot and locate its (Pareto) frontier. This study benchmarks two MOEAs (NSGA II and Borg MOEA) and compares their efficiency in terms of the quality of Pareto's frontier and computational cost. A detailed analysis of dam characteristics is performed to examine their impact on the global system efficiency and choice of the best redistribution rule. Finally, it is found that non-proportional flow releases can statistically improve the global efficiency, specifically the ecological one, of the hydropower system when compared to constant minimal flows.
Automatic efficiency optimization of an axial compressor with adjustable inlet guide vanes
Li, Jichao; Lin, Feng; Nie, Chaoqun; Chen, Jingyi
2012-04-01
The inlet attack angle of rotor blade reasonably can be adjusted with the change of the stagger angle of inlet guide vane (IGV); so the efficiency of each condition will be affected. For the purpose to improve the efficiency, the DSP (Digital Signal Processor) controller is designed to adjust the stagger angle of IGV automatically in order to optimize the efficiency at any operating condition. The A/D signal collection includes inlet static pressure, outlet static pressure, outlet total pressure, rotor speed and torque signal, the efficiency can be calculated in the DSP, and the angle signal for the stepping motor which control the IGV will be sent out from the D/A. Experimental investigations are performed in a three-stage, low-speed axial compressor with variable inlet guide vanes. It is demonstrated that the DSP designed can well adjust the stagger angle of IGV online, the efficiency under different conditions can be optimized. This establishment of DSP online adjustment scheme may provide a practical solution for improving performance of multi-stage axial flow compressor when its operating condition is varied.
Toward Improved Rotor-Only Axial Fans—Part II: Design Optimization for Maximum Efficiency
DEFF Research Database (Denmark)
Sørensen, Dan Nørtoft; Thompson, M. C.; Sørensen, Jens Nørkær
2000-01-01
Numerical design optimization of the aerodynamic performance of axial fans is carried out, maximizing the efficiency in a designinterval of flow rates. Tip radius, number of blades, and angular velocity of the rotor are fixed, whereas the hub radius andspanwise distributions of chord length......, stagger angle, and camber angle are varied to find the optimum rotor geometry.Constraints ensure a pressure rise above a specified target and an angle of attack on the blades below stall. The optimizationscheme is used to investigate the dependence of maximum efficiency on the width of the design interval...
Global Sea Surface Temperature and Sea Level Rise Estimation with Optimal Historical Time Lag Data
Directory of Open Access Journals (Sweden)
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.
Optimal laser wavelength for efficient laser power converter operation over temperature
Höhn, O.; Walker, A. W.; Bett, A. W.; Helmers, H.
2016-06-01
A temperature dependent modeling study is conducted on a GaAs laser power converter to identify the optimal incident laser wavelength for optical power transmission. Furthermore, the respective temperature dependent maximal conversion efficiencies in the radiative limit as well as in a practically achievable limit are presented. The model is based on the transfer matrix method coupled to a two-diode model, and is calibrated to experimental data of a GaAs photovoltaic device over laser irradiance and temperature. Since the laser wavelength does not strongly influence the open circuit voltage of the laser power converter, the optimal laser wavelength is determined to be in the range where the external quantum efficiency is maximal, but weighted by the photon flux of the laser.
Ling, Chen; Li, Baozheng; Ma, Wenqin; Srivastava, Arun
2016-08-01
We have described the development of capsid-modified next-generation AAV vectors for both AAV2 and AAV3 serotypes, in which specific surface-exposed tyrosine (Y), serine (S), threonine (T), and lysine (K) residues on viral capsids were modified to achieve high-efficiency transduction at lower doses. We have also described the development of genome-modified AAV vectors, in which the transcriptionally inactive, single-stranded AAV genome was modified to achieve improved transgene expression. Here, we describe that combination of capsid modifications and genome modifications leads to the generation of optimized AAV serotype vectors, which transduce cells and tissues more efficiently, both in vitro and in vivo, at ∼20-30-fold reduced doses. These studies have significant implications in the potential use of the optimized AAV serotype vectors in human gene therapy.
Directory of Open Access Journals (Sweden)
Syed Bilal Hussain Shah
2017-01-01
Full Text Available In Wireless Sensors Networks (WSNs, researcher’s main focus is on energy preservation and prolonging network lifetime. More energy resources are required in case of remote applications of WSNs, where some of the nodes die early that shorten the lifetime and decrease the stability of the network. It is mainly caused due to the non-optimal Cluster Heads (CHs selection based on single criterion and uneven distribution of energy. We propose a new clustering protocol for both homogeneous and heterogeneous environments, named as Optimized Path planning algorithm with Energy efficiency and Extending Network lifetime in WSN (OPEN. In the proposed protocol, timer value concept is used for CH selection based on multiple criteria. Simulation results prove that OPEN performs better than the existing protocols in terms of the network lifetime, throughput and stability. The results explicitly explain the cluster head selection of OPEN protocol and efficient solution of uneven energy distribution problem.
Directory of Open Access Journals (Sweden)
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.
Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach
Directory of Open Access Journals (Sweden)
Ramirez Mário
2009-05-01
Full Text Available Abstract Background Multilocus Sequence Typing (MLST is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST. The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Results Here, we present a globally optimized implementation of the eBURST algorithm – goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. Conclusion goeBURST is a globally optimized implementation of the eBURST algorithm, that
AN EFFICIENT NUMERICAL METHOD FOR THE SOLUTION OF THE L2 OPTIMAL MASS TRANSFER PROBLEM*
Haber, Eldad; Rehman, Tauseef; Tannenbaum, Allen
2010-01-01
In this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal L2 mass transport mapping. In contrast to the integration of a time dependent partial differential equation proposed in [S. Angenent, S. Haker, and A. Tannenbaum, SIAM J. Math. Anal., 35 (2003), pp. 61–97], we employ in the present work a direct variational method. The efficacy of the approach is demonstrated on both real and synthetic data. PMID:21278828
Asymptotic optimality and efficient computation of the leave-subject-out cross-validation
Xu, Ganggang
2012-12-01
Although the leave-subject-out cross-validation (CV) has been widely used in practice for tuning parameter selection for various nonparametric and semiparametric models of longitudinal data, its theoretical property is unknown and solving the associated optimization problem is computationally expensive, especially when there are multiple tuning parameters. In this paper, by focusing on the penalized spline method, we show that the leave-subject-out CV is optimal in the sense that it is asymptotically equivalent to the empirical squared error loss function minimization. An efficient Newton-type algorithm is developed to compute the penalty parameters that optimize the CV criterion. Simulated and real data are used to demonstrate the effectiveness of the leave-subject-out CV in selecting both the penalty parameters and the working correlation matrix. © 2012 Institute of Mathematical Statistics.
Optimized shielded-gate trench MOSFET technology for high-frequency, high-efficiency power supplies
Challa, Ashok; Sarkar, Tirthajyoti; Sapp, Steven
2012-10-01
Shielded-gate trench-MOSFETs yield superior performance compared to conventional gate trench devices by allowing higher doping density in the drift region and providing a `shielding effect' for the gate by placing an intermediate electrode between gate and drain. However, further design optimizations can be done for a shieldedgate trench-MOSFET to improve performance parameters particularly suited for next-generation high-frequency computing power supply applications and they have been outlined in this article. Channel optimization, substrate thinning and intrinsic gate resistance reduction (by layout enhancements) have been discussed along with their impact on cost-performance benefit on the device. Further, effects of these design optimizations on the power loss and efficiency of a high-frequency switching converter have been demonstrated by mixed device-circuit simulations.
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.
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.
A globally optimal k-anonymity method for the de-identification of health data.
El Emam, Khaled; Dankar, Fida Kamal; Issa, Romeo; Jonker, Elizabeth; Amyot, Daniel; Cogo, Elise; Corriveau, Jean-Pierre; Walker, Mark; Chowdhury, Sadrul; Vaillancourt, Regis; Roffey, Tyson; Bottomley, Jim
2009-01-01
Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.
Han, Wenhua; Xu, Jun; Wang, Ping; Tian, Guiyun
2014-06-12
In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%-30% than that of the SLPSO-based inversing technique.
Directory of Open Access Journals (Sweden)
Stella Kafetzoglou
2015-08-01
Full Text Available Among the key aspects of the Internet of Things (IoT is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.
Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon
2015-08-11
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.
Welker, A; Wolcke, B; Schleppers, A; Schmeck, S B; Focke, U; Gervais, H W; Schmeck, J
2010-10-01
The introduction of the diagnosis-related groups reimbursement system has increased cost pressures. Due to the interaction of many different professional groups, analysis and optimization of internal coordination and scheduling in the operating room (OR) is mandatory. The aim of this study was to analyze the processes at a university hospital in order to optimize strategies by identifying potential weak points. Over a period 6 weeks before and 4 weeks after intervention processes time intervals in the OR of a tertiary care hospital (university hospital) were documented in a structured data collection sheet. The main reason for lack of efficiency of labor was underused OR utilization. Multifactorial reasons, particularly in the management of perioperative interfaces, led to vacant ORs. A significant deficit was in the use of OR capacity at the end of the daily OR schedule. After harmonization of working hours of different staff groups and implementation of several other changes an increase in efficiency could be verified. These results indicate that optimization of perioperative processes considerably contribute to the success of OR organization. Additionally, the implementation of standard operating procedures and a generally accepted OR statute are mandatory. In this way an efficient OR management can contribute to the economic success of a hospital.
Development of an Optimal Controller and Validation Test Stand for Fuel Efficient Engine Operation
Rehn, Jack G., III
There are numerous motivations for improvements in automotive fuel efficiency. As concerns over the environment grow at a rate unmatched by hybrid and electric automotive technologies, the need for reductions in fuel consumed by current road vehicles has never been more present. Studies have shown that a major cause of poor fuel consumption in automobiles is improper driving behavior, which cannot be mitigated by purely technological means. The emergence of autonomous driving technologies has provided an opportunity to alleviate this inefficiency by removing the necessity of a driver. Before autonomous technology can be relied upon to reduce gasoline consumption on a large scale, robust programming strategies must be designed and tested. The goal of this thesis work was to design and deploy an autonomous control algorithm to navigate a four cylinder, gasoline combustion engine through a series of changing load profiles in a manner that prioritizes fuel efficiency. The experimental setup is analogous to a passenger vehicle driving over hilly terrain at highway speeds. The proposed approach accomplishes this using a model-predictive, real-time optimization algorithm that was calibrated to the engine. Performance of the optimal control algorithm was tested on the engine against contemporary cruise control. Results indicate that the "efficient'' strategy achieved one to two percent reductions in total fuel consumed for all load profiles tested. The consumption data gathered also suggests that further improvements could be realized on a different subject engine and using extended models and a slightly modified optimal control approach.
Efficient amorphous silicon solar cells: characterization, optimization, and optical loss analysis
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Wayesh Qarony
Full Text Available Hydrogenated amorphous silicon (a-Si:H has been effectively utilized as photoactive and doped layers for quite a while in thin-film solar applications but its energy conversion efficiency is limited due to thinner absorbing layer and light degradation issue. To overcome such confinements, it is expected to adjust better comprehension of device structure, material properties, and qualities since a little enhancement in the photocurrent significantly impacts on the conversion efficiency. Herein, some numerical simulations were performed to characterize and optimize different configuration of amorphous silicon-based thin-film solar cells. For the optical simulation, two-dimensional finite-difference time-domain (FDTD technique was used to analyze the superstrate (p-i-n planar amorphous silicon solar cells. Besides, the front transparent contact layer was also inquired by using SnO2:F and ZnO:Al materials to improve the photon absorption in the photoactive layer. The cell was studied for open-circuit voltage, external quantum efficiency, and short-circuit current density, which are building blocks for solar cell conversion efficiency. The optical simulations permit investigating optical losses at the individual layers. The enhancement in both short-circuit current density and open-circuit voltage prompts accomplishing more prominent power conversion efficiency. A maximum short-circuit current density of 15.32â¯mA/cm2 and an energy conversion efficiency of 11.3% were obtained for the optically optimized cell which is the best in class amorphous solar cell. Keywords: Superstrate p-i-n, Power loss, Quantum efficiency, Short circuit current, FDTD
León, Ileana R.; Schwämmle, Veit; Jensen, Ole N.; Sprenger, Richard R.
2013-01-01
The majority of mass spectrometry-based protein quantification studies uses peptide-centric analytical methods and thus strongly relies on efficient and unbiased protein digestion protocols for sample preparation. We present a novel objective approach to assess protein digestion efficiency using a combination of qualitative and quantitative liquid chromatography-tandem MS methods and statistical data analysis. In contrast to previous studies we employed both standard qualitative as well as data-independent quantitative workflows to systematically assess trypsin digestion efficiency and bias using mitochondrial protein fractions. We evaluated nine trypsin-based digestion protocols, based on standard in-solution or on spin filter-aided digestion, including new optimized protocols. We investigated various reagents for protein solubilization and denaturation (dodecyl sulfate, deoxycholate, urea), several trypsin digestion conditions (buffer, RapiGest, deoxycholate, urea), and two methods for removal of detergents before analysis of peptides (acid precipitation or phase separation with ethyl acetate). Our data-independent quantitative liquid chromatography-tandem MS workflow quantified over 3700 distinct peptides with 96% completeness between all protocols and replicates, with an average 40% protein sequence coverage and an average of 11 peptides identified per protein. Systematic quantitative and statistical analysis of physicochemical parameters demonstrated that deoxycholate-assisted in-solution digestion combined with phase transfer allows for efficient, unbiased generation and recovery of peptides from all protein classes, including membrane proteins. This deoxycholate-assisted protocol was also optimal for spin filter-aided digestions as compared with existing methods. PMID:23792921
Real time PI-backstepping induction machine drive with efficiency optimization.
Farhani, Fethi; Ben Regaya, Chiheb; Zaafouri, Abderrahmen; Chaari, Abdelkader
2017-09-01
This paper describes a robust and efficient speed control of a three phase induction machine (IM) subjected to load disturbances. First, a Multiple-Input Multiple-Output (MIMO) PI-Backstepping controller is proposed for a robust and highly accurate tracking of the mechanical speed and rotor flux. Asymptotic stability of the control scheme is proven by Lyapunov Stability Theory. Second, an active online optimization algorithm is used to optimize the efficiency of the drive system. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the IM. A dSPACE DS1104 R&D board is used to implement the proposed solution. The experimental results released on 3kW squirrel cage IM, show that the reference speed as well as the rotor flux are rapidly achieved with a fast transient response and without overshoot. A good load disturbances rejection response and IM parameters variation are fairly handled. The improvement of drive system efficiency reaches up to 180% at light load. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Shi, Shengchao; Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan
2017-09-04
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach's method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.
Salari, Ehsan; Wala, Jeremiah; Craft, David
2012-09-01
To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-plan optimization method, called vmerge, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose quality. vmerge begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the ‘ideal’ dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard vmerge algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-quality plan, we can obtain efficient VMAT plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial plan simplifications, but to deviate in quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the
Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization
Directory of Open Access Journals (Sweden)
Mine Kaya
2017-11-01
Full Text Available Design of efficient thin film photovoltaic (PV cells require optical power absorption to be computed inside a nano-scale structure of photovoltaics, dielectric and plasmonic materials. Calculating power absorption requires Maxwell’s electromagnetic equations which are solved using numerical methods, such as finite difference time domain (FDTD. The computational cost of thin film PV cell design and optimization is therefore cumbersome, due to successive FDTD simulations. This cost can be reduced using a surrogate-based optimization procedure. In this study, we deploy neural networks (NNs to model optical absorption in organic PV structures. We use the corresponding surrogate-based optimization procedure to maximize light trapping inside thin film organic cells infused with metallic particles. Metallic particles are known to induce plasmonic effects at the metal–semiconductor interface, thus increasing absorption. However, a rigorous design procedure is required to achieve the best performance within known design guidelines. As a result of using NNs to model thin film solar absorption, the required time to complete optimization is decreased by more than five times. The obtained NN model is found to be very reliable. The optimization procedure results in absorption enhancement greater than 200%. Furthermore, we demonstrate that once a reliable surrogate model such as the developed NN is available, it can be used for alternative analyses on the proposed design, such as uncertainty analysis (e.g., fabrication error.
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M. Skowron
2009-01-01
Full Text Available This note is devoted to multiperiodically operated complex system with inventory couplings transferring waste products from some subsystems as useful components to other subsystems. The flexibility of the inventory couplings is used to force each of the subsystems with its own period and to exploit its particular dynamic properties. This enhances the performance of the complex system endowed with many recycling loops, which reduce the amount of waste products endangering the natural environment. The subsystems are characterized by generalized populations composed of the individuals (the cycles, each of them encompasses its period, its initial state, its local control, and its inventory interaction. An evolutionary optimization algorithm employing such generalized populations coordinated on the basis of the inventory interaction constraints is developed. It includes the stability requirements imposed on the cyclic control processes connected with particular subsystems. The algorithm proposed is applied to the global multiperiodic optimization of some interconnected chemical production processes.
DEFF Research Database (Denmark)
Achtziger, Wolfgang; Stolpe, Mathias
2009-01-01
bar areas. We consider here the difficult situation that the truss must be built from pre-produced bars with given areas. This paper together with Part I proposes an algorithmic framework for the calculation of a global optimizer of the underlying non-convex mixed integer design problem. In this paper......A classical problem within the field of structural optimization is to find the stiffest truss design subject to a given external static load and a bound on the total volume. The design variables describe the cross sectional areas of the bars. This class of problems is well-studied for continuous...... on the implementation details but also establish finite convergence of the branch-and-bound method. The algorithm is based on solving a sequence of continuous non-convex relaxations which can be formulated as quadratic programs according to the theory in Part I. The quadratic programs to be treated within the branch...
A genetic algorithm for first principles global structure optimization of supported nano structures
Energy Technology Data Exchange (ETDEWEB)
Vilhelmsen, Lasse B.; Hammer, Bjørk, E-mail: hammer@phys.au.dk [Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C (Denmark)
2014-07-28
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA’s with a description of optimal parameters to use. New results for the adsorption of M{sub 8} clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO{sub 2}(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
A genetic algorithm for first principles global structure optimization of supported nano structures.
Vilhelmsen, Lasse B; Hammer, Bjørk
2014-07-28
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA's with a description of optimal parameters to use. New results for the adsorption of M8 clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO2(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
Directory of Open Access Journals (Sweden)
Somayya Komal
2016-10-01
Full Text Available The purpose of this article is to establish the global optimization with partial orders for the pair of non-self mappings, by introducing new type of contractions like $\\alpha$-ordered contractions and $\\alpha$-ordered proximal contraction in the frame work of complete metric spaces. Also calculates some fixed point theorems with the help of these generalized contractions. In addition, established an example to show the validity of our main result. These results extended and unify many existing results in the literature.
Global Optimal Multiple Object Detection Using the Fusion of Shape and Color Information
Schikora, Marek
In this work we present a novel method for detecting multiple objects of interest in one image, when the only available information about these objects are their shape and color. To solve this task we use a global optimal variational approach based on total variation. The presented energy functional can be minimized locally due its convex formulation. To improve the runtime of our algorithm we show how this approach can be scheduled in parallel.Our algorithm works fully automatically and does not need any user interaction. In experiments we show the capabilities in non-artificial images, e.g. aerial or bureau images.
Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization
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Jinkui Liu
2012-01-01
Full Text Available A new nonlinear spectral conjugate descent method for solving unconstrained optimization problems is proposed on the basis of the CD method and the spectral conjugate gradient method. For any line search, the new method satisfies the sufficient descent condition gkTdk<−∥gk∥2. Moreover, we prove that the new method is globally convergent under the strong Wolfe line search. The numerical results show that the new method is more effective for the given test problems from the CUTE test problem library (Bongartz et al., 1995 in contrast to the famous CD method, FR method, and PRP method.
Global optimization of a neural network-hidden Markov model hybrid.
Bengio, Y; De Mori, R; Flammia, G; Kompe, R
1992-01-01
The integration of multilayered and recurrent artificial neural networks (ANNs) with hidden Markov models (HMMs) is addressed. ANNs are suitable for approximating functions that compute new acoustic parameters, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters. Results on speaker-independent recognition experiments using this integrated ANN-HMM system on the TIMIT continuous speech database are reported.
Electronic neural network for solving traveling salesman and similar global optimization problems
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
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Miró Anton
2012-05-01
Full Text Available Abstract Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap, but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance in a fraction of
Miró, Anton; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Egea, Jose A; Jiménez, Laureano
2012-05-10
The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.
Optimization and Modification of the SeaQuest Trigger Efficiency Program
White, Nattapat
2017-09-01
The primary purpose E906/SeaQuest is to examine the quark and antiquark distributions within the nucleon. This experiment uses the proton beam from the 120 GeV Fermi National Accelerator Laboratory Main Injector to collide with one of several fixed targets. From the collision, a pair of muons produced by the Drell-Yan process directly probes the nucleon sea antiquarks. The Seaquest spectrometer consists of two focusing magnets, several detectors, and multiple planes of scintillating hodoscopes that helped track and analyze the properties of particles. Hodoscope hits are compared to predetermined hit combinations that would result from a pair of muons that originated in the target. Understanding the trigger efficiency is part of the path to determine the probability of Drell Yan muon pair production in the experiment. Over the years of data taking, the trigger efficiency varied as individual scintillator detection efficiency changed. To accurately determine how the trigger efficiency varied over time, the trigger efficiency program needed to be upgraded to include the effects of inefficiencies in the 284 individual channels in the hodoscope systems. The optimization, modification, and results of the upgraded trigger efficiency program will be presented. Supported by U.S. D.O.E. Medium Energy Nuclear Physics under Grant DE-FG02-03ER41243.
Global Optimized Shapes of Flying Configurations Compared with Those of Gliding Birds
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Adriana NASTASE
2014-04-01
Full Text Available The determination of global optimized (GO shape of a flying configuration (FC (namely, the simultaneous optimization of its camber, twist and thickness distributions and also of the similarity parameters of its planform leads to an enlarged variational problem with free boundaries. An own optimum-optimorum (OO theory was developed in order to solve this enlarged variational problem. According to this OO theory, a lower limit hypersurface of the drag coefficients of elitary FCs versus the corresponding set of similarity parameters of their planforms is defined. The elitary FCs corresponding to the optimum set of similarity parameters, which is obtained by the numerical determination of the position of the minimum of this hypersurface is, at the same time, the GO FC of the set. The GO shapes of three FCs models were designed by the author according to her OO theory. The transversal cuts of the GO FCs look like those of gliding birds and also their behaviors, by changing of start values of optimization, are similar because nature optimizes too.
Global efficiency of structural networks mediates cognitive control in Mild Cognitive Impairment
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Rok Berlot
2016-12-01
Full Text Available Background: Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. Objective: To determine the contribution of both localised white matter tract damage and disruption of global network architecture to cognitive control, in older age and Mild Cognitive Impairment (MCI.Methods: 25 patients with MCI and 20 age, sex and intelligence-matched healthy volunteers were investigated with 3 Tesla structural magnetic resonance imaging (MRI. Cognitive control and episodic memory were evaluated with established tests. Structural network graphs were constructed from diffusion MRI-based whole-brain tractography. Their global measures were calculated using graph theory. Regression models utilized both global network metrics and microstructure of specific connections, known to be critical for each domain, to predict cognitive scores. Results: Global efficiency and the mean clustering coefficient of networks were reduced in MCI. Cognitive control was associated with global network topology. Episodic memory, in contrast, correlated with individual temporal tracts only. Relationships between cognitive control and network topology were attenuated by addition of single tract measures to regression models, consistent with a partial mediation effect. The mediation effect was stronger in MCI than healthy volunteers, explaining 23-36% of the effect of cingulum microstructure on cognitive control performance. Network clustering was a significant mediator in the relationship between tract microstructure and cognitive control in both groups. Conclusions: The status of critical connections and large-scale network topology are both important for maintenance of cognitive control in MCI. Mediation via large-scale networks is more important in patients with MCI than healthy volunteers. This effect is domain-specific, and true for cognitive
Šoukal, Jakub; Benada, Oldřich; Matoušek, Tomáš; Dědina, Jiří; Musil, Stanislav
2017-07-18
This work is a comprehensive study on chemical generation of volatile species (VSG) of copper for analytical atomic spectrometry. VSG was carried out in a flow injection mode in a special arrangement of the generator. Atomization in a diffusion flame atomizer (DF) with atomic absorption spectrometry detection was mostly used for VSG optimization. Inductively coupled plasma mass spectrometry (ICP-MS) was utilized to investigate generation efficiencies and feasibility of VSG system for ultratrace analysis. Concentration of individual reagents, namely of nitric acid, sodium tetrahydroborate and various reaction modifiers, was optimized with respect to generation efficiency. Triton X-100 and Antifoam B were chosen as the best combination of the modifiers owing to sixfold increase in sensitivity, decrease of tailing of measured signals and long-term repeatability. The addition of 500 μg L(-1) of Ag was found crucial to maintain identical generation efficiency at low concentrations of Cu. This phenomenon was ascribed to the change in the size of generated species. The release and generation efficiency were accurately determined as 56-58 and 31-32%, respectively. The contribution of co-generated aerosol to release and generation efficiency measured by means of Cs and Ba was found negligible, only 0.40 and 0.13%, respectively, which underlines highly efficient VSG of Cu. The nature of volatile species was investigated by various approaches. The results cannot provide the decisive evidence. However, experiments with the DF, ICP-MS and transmission electron microscopy (TEM) indicate that the generated species are not volatile in the true sense but that they are strongly associated with fine aerosol co-generated during VSG. Cu clusters or nanoparticles of very small size (< 10 nm) are presumed but the formation of metastable copper hydride cannot be conclusively excluded. Copyright © 2017 Elsevier B.V. All rights reserved.
Complementarity and Area-Efficiency in the Prioritization of the Global Protected Area Network.
Kullberg, Peter; Toivonen, Tuuli; Montesino Pouzols, Federico; Lehtomäki, Joona; Di Minin, Enrico; Moilanen, Atte
2015-01-01
Complementarity and cost-efficiency are widely used principles for protected area network design. Despite the wide use and robust theoretical underpinnings, their effects on the performance and patterns of priority areas are rarely studied in detail. Here we compare two approaches for identifying the management priority areas inside the global protected area network: 1) a scoring-based approach, used in recently published analysis and 2) a spatial prioritization method, which accounts for complementarity and area-efficiency. Using the same IUCN species distribution data the complementarity method found an equal-area set of priority areas with double the mean species ranges covered compared to the scoring-based approach. The complementarity set also had 72% more species with full ranges covered, and lacked any coverage only for half of the species compared to the scoring approach. Protected areas in our complementarity-based solution were on average smaller and geographically more scattered. The large difference between the two solutions highlights the need for critical thinking about the selected prioritization method. According to our analysis, accounting for complementarity and area-efficiency can lead to considerable improvements when setting management priorities for the global protected area network.
Complementarity and Area-Efficiency in the Prioritization of the Global Protected Area Network.
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Peter Kullberg
Full Text Available Complementarity and cost-efficiency are widely used principles for protected area network design. Despite the wide use and robust theoretical underpinnings, their effects on the performance and patterns of priority areas are rarely studied in detail. Here we compare two approaches for identifying the management priority areas inside the global protected area network: 1 a scoring-based approach, used in recently published analysis and 2 a spatial prioritization method, which accounts for complementarity and area-efficiency. Using the same IUCN species distribution data the complementarity method found an equal-area set of priority areas with double the mean species ranges covered compared to the scoring-based approach. The complementarity set also had 72% more species with full ranges covered, and lacked any coverage only for half of the species compared to the scoring approach. Protected areas in our complementarity-based solution were on average smaller and geographically more scattered. The large difference between the two solutions highlights the need for critical thinking about the selected prioritization method. According to our analysis, accounting for complementarity and area-efficiency can lead to considerable improvements when setting management priorities for the global protected area network.
Energy Provider: Delivered Energy Efficiency: A global stock-taking based on case studies
Energy Technology Data Exchange (ETDEWEB)
NONE
2013-06-01
In 2011 the IEA and the Regulatory Assistance Project (RAP) took on a work programme focused on the role of energy providers in delivering energy efficiency to end-users. This work was part of the IEA’s contribution to the PEPDEE Task Group, which falls under the umbrella of the International Partnership on Energy Efficiency Cooperation (IPEEC). In addition to organizing regional dialogues between governments, regulators, and energy providers, the PEPDEE work stream conducted global stock-takings of regulatory mechanisms adopted by governments to obligate or encourage energy providers to delivery energy savings and the energy savings activities of energy providers. For its part the IEA conducted a global review of energy provider-delivered energy savings programmes. The IEA reached out to energy providers to identify the energy savings activities they engaged in. Some 250 energy saving activities were considered, and 41 detailed case studies spanning 18 countries were developed. Geographic balance was a major consideration, and much effort was expended identifying energy provider-delivered energy savings case studies from around the world. Taken together these case studies represent over USD 1 billion in annual spending, or about 8% of estimated energy provider spending on energy efficiency.
Efficient algorithms for optimal arrival scheduling and air traffic flow management
Saraf, Aditya
The research presented in this dissertation is motivated by the need for new, efficient algorithms for the solution of two important problems currently faced by the air-traffic control community: (i) optimal scheduling of aircraft arrivals at congested airports, and (ii) optimal National Airspace System (NAS) wide traffic flow management. In the first part of this dissertation, we present an optimal airport arrival scheduling algorithm, which works within a hierarchical scheduling structure. This structure consists of schedulers at multiple points along the arrival-route. Schedulers are linked through acceptance-rate constraints, which are passed up from downstream metering-points. The innovation in this scheduling algorithm is that these constraints are computed by using an Eulerian model-based optimization scheme. This rate computation removes inefficiencies introduced in the schedule through ad hoc acceptance-rate computations. The scheduling process at every metering-point uses its optimal acceptance-rate as a constraint and computes optimal arrival sequences by using a combinatorial search-algorithm. We test this algorithm in a dynamic air-traffic environment, which can be customized to emulate different arrival scenarios. In the second part of this dissertation, we introduce a novel two-level control system for optimal traffic-flow management. The outer-level control module of this two-level control system generates an Eulerian-model of the NAS by aggregating aircraft into interconnected control-volumes. Using this Eulerian model of the airspace, control strategies like Model Predictive Control are applied to find the optimal inflow and outflow commands for each control-volume so that efficient flows are achieved in the NAS. Each control-volume has its separate inner-level control-module. The inner-level control-module takes in the optimal inflow and outflow commands generated by the outer control-module as reference inputs and uses hybrid aircraft models to
French, S. W.; Lekic, V.; Romanowicz, B. A.
2010-12-01
As global waveform-modeling schemes rooted in perturbation theory are supplanted by fully numerical alternatives, such as the Spectral Element Method (e.g. SEM: Komatitsch and Tromp, 2002), the improved wavefield accuracy for complex 3D structures also carries increased computational cost. Lekic and Romanowicz (2010) inverted waveforms of fundamental and higher mode surface waves for a radially anisotropic upper-mantle Vs model using SEM (SEMum). The SEM computations were made feasible by an appropriate choice of cutoff period (T≥ 60 s.), as well as the implementation of a homogenized anisotropic crustal layer based on fitting of short period group velocity dispersion curves. These choices allowed for an efficient SEM mesh undeformed by true Moho topography. Further, instead of homogenization of a possibly biased a priori crustal model, Lekic and Romanowicz jointly inverted for the crustal layer, constrained by surface wave group velocity dispersion maps for T≥ 25 s. We are currently developing a radially anisotropic Vs model of the whole mantle using SEM, following an approach broadly similar to that employed in SEMum. Extension of this methodology to imaging of lower-mantle structure requires the inclusion of a body wave dataset, and thus shorter-period modeling of the global wavefield (T≥ 32 s.). While this period range dictates finer sampling of our SEM mesh, reduced computational cost is still possible through the crustal homogenization scheme. Here, we first discuss the development of an analogous homogenized crustal model and its validity for both the fundamental and higher mode surface wave and the body wave datasets. We focus on maintaining a simplified Moho topography, thus obviating expensive deformation of the SEM mesh, while accurately treating valuable surface-reflected body wave phases (ex: multiple ScS). Second, we discuss implications of treating the crust in this manner for the overall inversion methodology. In particular, we intend to
Nemani, Ramakrishna R.
2016-01-01
Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.
The Effects of the Global Financial Crisis on Brazilian Banking Efficiency
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Mark Edward Wolters
2014-05-01
Full Text Available With Brazil and the BRIC economies becoming more important to world growth and financial investment, it is important to understand the inner-workings of the financial institutions that will help spur on continued economic growth. This article focuses on the recent history of the market structure of the Brazilian banking sector as well as the effects of the global financial crisis of the late 2000s on the overall relative efficiency of the Brazilian banking sector by using Data Envelopment Analysis. The period studied—2002-2011—shows a marked decrease in overall relative efficiency in the Brazilian banking sector. The negative effects were felt across the board regardless of bank size or ownership type. Small and medium sized banks had the most significant drop in relative efficiency while larger banks were able to weather the crisis more successfully. This alludes to the idea that “Bigger is Better” when dealing with financial shocks to banking efficiency, and would allow us to summarize that the Brazilian banking sector is not participating in the “Quiet Life” of concentrated markets. Also, looking at ownership type, the study shows that Brazilian banks that are controlled by the government were ranked as the most efficient types of banks. Their foreignowned counterparts, however, were ranked as the least efficient and had a larger drop in overall efficiency and participation during the financial crisis than their domestic Brazilian counterparts. The article contributes to the continued need for more information on the Brazilian banking sector’s history and development.
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Álvaro Gutiérrez
2011-11-01
Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
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Ayon Chakraborty
2009-01-01
Full Text Available Energy efficiency of sensor nodes is a sizzling issue,given the severe resource constraints of sensor nodes andpervasive nature of sensor networks. The base station beinglocated at variable distances from the nodes in the sensor field,each node actually dissipates a different amount of energy totransmit data to the same. The LEACH [4] and PEGASIS [5]protocols provide elegant solutions to this problem, but may notalways result in optimal performance. In this paper we haveproposed a novel data gathering protocol for enhancing thenetwork lifetime by optimizing energy dissipation in the nodes.To achieve our design objective we have applied particle swarmoptimization (PSO with Simulated Annealing (SA to form asub-optimal data gathering chain and devised a method forselecting an efficient leader for communicating to the basestation. In our scheme each node only communicates with aclose neighbor and takes turns in being the leader depending onits residual energy and location. This helps to rule out theunequal energy dissipation by the individual nodes of thenetwork and results in superior performance as compared toLEACH and PEGASIS. Extensive computer simulations havebeen carried out which shows that significant improvement isover these schemes.
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Eleonora Sforza
Full Text Available Biofuels from algae are highly interesting as renewable energy sources to replace, at least partially, fossil fuels, but great research efforts are still needed to optimize growth parameters to develop competitive large-scale cultivation systems. One factor with a seminal influence on productivity is light availability. Light energy fully supports algal growth, but it leads to oxidative stress if illumination is in excess. In this work, the influence of light intensity on the growth and lipid productivity of Nannochloropsis salina was investigated in a flat-bed photobioreactor designed to minimize cells self-shading. The influence of various light intensities was studied with both continuous illumination and alternation of light and dark cycles at various frequencies, which mimic illumination variations in a photobioreactor due to mixing. Results show that Nannochloropsis can efficiently exploit even very intense light, provided that dark cycles occur to allow for re-oxidation of the electron transporters of the photosynthetic apparatus. If alternation of light and dark is not optimal, algae undergo radiation damage and photosynthetic productivity is greatly reduced. Our results demonstrate that, in a photobioreactor for the cultivation of algae, optimizing mixing is essential in order to ensure that the algae exploit light energy efficiently.
Sanghyun, Ahn; Seungwoong, Ha; Kim, Soo Yong
2016-06-01
A vital challenge for many socioeconomic systems is determining the optimum use of limited information. Traffic systems, wherein the range of resources is limited, are a particularly good example of this challenge. Based on bounded information accessibility in terms of, for example, high costs or technical limitations, we develop a new optimization strategy to improve the efficiency of a traffic system with signals and intersections. Numerous studies, including the study by Chowdery and Schadschneider (whose method we denote by ChSch), have attempted to achieve the maximum vehicle speed or the minimum wait time for a given traffic condition. In this paper, we introduce a modified version of ChSch with an independently functioning, decentralized control system. With the new model, we determine the optimization strategy under bounded information accessibility, which proves the existence of an optimal point for phase transitions in the system. The paper also provides insight that can be applied by traffic engineers to create more efficient traffic systems by analyzing the area and symmetry of local sites. We support our results with a statistical analysis using empirical traffic data from Seoul, Korea.
Morelli, Eugene A.; Cunningham, Kevin; Hill, Melissa A.
2013-01-01
Flight test and modeling techniques were developed for efficiently identifying global aerodynamic models that can be used to accurately simulate stall, upset, and recovery on large transport airplanes. The techniques were developed and validated in a high-fidelity fixed-base flight simulator using a wind-tunnel aerodynamic database, realistic sensor characteristics, and a realistic flight deck representative of a large transport aircraft. Results demonstrated that aerodynamic models for stall, upset, and recovery can be identified rapidly and accurately using relatively simple piloted flight test maneuvers. Stall maneuver predictions and comparisons of identified aerodynamic models with data from the underlying simulation aerodynamic database were used to validate the techniques.
The Multipoint Global Shape Optimization of Flying Configuration with Movable Leading Edges Flaps
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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.
Efficiency and optimal size of hospitals: Results of a systematic search.
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Monica Giancotti
Full Text Available National Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.This paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969-2014 of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal's category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.Studies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200-300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.
Efficiency and optimal size of hospitals: Results of a systematic search.
Giancotti, Monica; Guglielmo, Annamaria; Mauro, Marianna
2017-01-01
National Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources. This paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969-2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal's category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care. Studies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200-300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.
Efficiency and optimal size of hospitals: Results of a systematic search
Guglielmo, Annamaria
2017-01-01
Background National Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources. Methods and findings This paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care. Conclusions Studies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds. PMID:28355255
Gao, Ya; Cheng, Wenchi; Zhang, Hailin
2017-08-23
Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.
Optimal Power and Efficiency of Quantum Thermoacoustic Micro-cycle Working in 1D Harmonic Trap
E, Qing; Wu, Feng; Yin, Yong; Liu, XiaoWei
2017-10-01
Thermoacoustic engines (including heat engines and refrigerators) are energy conversion devices without moving part. They have great potential in aviation, new energy utilization, power technology, refrigerating and cryogenics. The thermoacoustic parcels, which compose the working fluid of a thermoacoustic engine, oscillate within the sound channel with a temperature gradient. The thermodynamic foundation of a thermoacoustic engine is the thermoacoustic micro-cycle (TAMC). In this paper, the theory of quantum mechanics is applied to the study of the actual thermoacoustic micro-cycle for the first time. A quantum mechanics model of the TAMC working in a 1D harmonic trap, which is named as a quantum thermoacoustic micro-cycle (QTAMC), is established. The QTAMC is composed of two constant force processes connected by two straight line processes. Analytic expressions of the power output and the efficiency for QTAMC have been derived. The effects of the trap width and the temperature amplitude on the power output and the thermal efficiency have been discussed. Some optimal characteristic curves of power output versus efficiency are plotted, and then the optimization region of QTAMC is given in this paper. The results obtained here not only enrich the thermoacoustic theory but also expand the application of quantum thermodynamics.
Cheng, Wenchi; Zhang, Hailin
2017-01-01
Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks. PMID:28832509
Energy Technology Data Exchange (ETDEWEB)
McKane, Aimee; Desai, Deann; Matteini, Marco; Meffert, William; Williams, Robert; Risser, Roland
2009-08-01
Industry utilizes very complex systems, consisting of equipment and their human interface, which are organized to meet the production needs of the business. Effective and sustainable energy efficiency programs in an industrial setting require a systems approach to optimize the integrated whole while meeting primary business requirements. Companies that treat energy as a manageable resource and integrate their energy program into their management practices have an organizational context to continually seek opportunities for optimizing their energy use. The purpose of an energy management system standard is to provide guidance for industrial and commercial facilities to integrate energy efficiency into their management practices, including fine-tuning production processes and improving the energy efficiency of industrial systems. The International Organization for Standardization (ISO) has identified energy management as one of its top five priorities for standards development. The new ISO 50001 will establish an international framework for industrial, commercial, or institutional facilities, or entire companies, to manage their energy, including procurement and use. This standard is expected to achieve major, long-term increases in energy efficiency (20percent or more) in industrial, commercial, and institutional facilities and to reduce greenhouse gas (GHG) emissions worldwide.This paper describes the impetus for the international standard, its purpose, scope and significance, and development progress to date. A comparative overview of existing energy management standards is provided, as well as a discussion of capacity-building needs for skilled individuals to assist organizations in adopting the standard. Finally, opportunities and challenges are presented for implementing ISO 50001 in emerging economies and developing countries.
Optimal Cross-Layer Design for Energy Efficient D2D Sharing Systems
Alabbasi, Abdulrahman
2016-11-23
In this paper, we propose a cross-layer design, which optimizes the energy efficiency of a potential future 5G spectrum-sharing environment, in two sharing scenarios. In the first scenario, underlying sharing is considered. We propose and minimize a modified energy per good bit (MEPG) metric, with respect to the spectrum sharing user’s transmission power and media access frame length. The cellular users, legacy users, are protected by an outage probability constraint. To optimize the non-convex targeted problem, we utilize the generalized convexity theory and verify the problem’s strictly pseudoconvex structure. We also derive analytical expressions of the optimal resources. In the second scenario, we minimize a generalized MEPG function while considering a probabilistic activity of cellular users and its impact on the MEPG performance of the spectrum sharing users. Finally, we derive the associated optimal resource allocation of this problem. Selected numerical results show the improvement of the proposed system compared with other systems.
Energy-Efficient Transmission Strategy by Using Optimal Stopping Approach for Mobile Networks
Directory of Open Access Journals (Sweden)
Ying Peng
2016-01-01
Full Text Available In mobile networks, transmission energy consumption dominates the major part of network energy consumption. To reduce energy consumption for data transmission is an important topic for constructing green mobile networks. According to Shannon formula, when the transmission power is constant, the better the channel quality is, the greater the transmission rate is. Then, more data will be delivered in a given period. And energy consumption per bit data transmitted will be reduced. Because channel quality varies with time randomly, it is a good opportunity for decreasing energy consumption to deliver data in the best channel quality. However, data has delay demand. The sending terminal cannot wait for the best channel quality unlimitedly. Actually, sending terminal has to select an optimal time to deliver data before data exceeds delay. For this, this paper obtains the optimal transmission rate threshold at each detection slot time by using optimal stopping approach. Then, sending terminal determines whether current time is the optimal time through comparing current transmission rate with the corresponding rate threshold, thus realizing energy-efficient transmission strategy, so as to decrease average energy consumption per bit data transmitted.
Peng, Ting; Sun, Xiaochun; Mumm, Rita H
2014-01-01
Multiple trait integration (MTI) is a multi-step process of converting an elite variety/hybrid for value-added traits (e.g. transgenic events) through backcross breeding. From a breeding standpoint, MTI involves four steps: single event introgression, event pyramiding, trait fixation, and version testing. This study explores the feasibility of marker-aided backcross conversion of a target maize hybrid for 15 transgenic events in the light of the overall goal of MTI of recovering equivalent performance in the finished hybrid conversion along with reliable expression of the value-added traits. Using the results to optimize single event introgression (Peng et al. Optimized breeding strategies for multiple trait integration: I. Minimizing linkage drag in single event introgression. Mol Breed, 2013) which produced single event conversions of recurrent parents (RPs) with ≤8 cM of residual non-recurrent parent (NRP) germplasm with ~1 cM of NRP germplasm in the 20 cM regions flanking the event, this study focused on optimizing process efficiency in the second and third steps in MTI: event pyramiding and trait fixation. Using computer simulation and probability theory, we aimed to (1) fit an optimal breeding strategy for pyramiding of eight events into the female RP and seven in the male RP, and (2) identify optimal breeding strategies for trait fixation to create a 'finished' conversion of each RP homozygous for all events. In addition, next-generation seed needs were taken into account for a practical approach to process efficiency. Building on work by Ishii and Yonezawa (Optimization of the marker-based procedures for pyramiding genes from multiple donor lines: I. Schedule of crossing between the donor lines. Crop Sci 47:537-546, 2007a), a symmetric crossing schedule for event pyramiding was devised for stacking eight (seven) events in a given RP. Options for trait fixation breeding strategies considered selfing and doubled haploid approaches to achieve homozygosity
Efficient heuristic algorithm used for optimal capacitor placement in distribution systems
Energy Technology Data Exchange (ETDEWEB)
Segura, Silvio; Rider, Marcos J. [Department of Electric Energy Systems, University of Campinas, Campinas, Sao Paulo (Brazil); Romero, Ruben [Faculty of Engineering of Ilha Solteira, Paulista State University, Ilha Solteira, Sao Paulo (Brazil)
2010-01-15
An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods. (author)
Directory of Open Access Journals (Sweden)
S. Saravanan
2015-05-01
Full Text Available Thin film solar cells are cheaper but having low absorption in longer wavelength and hence, an effective light trapping mechanism is essential. In this work, we proposed an ultrathin crystalline silicon solar cell which showed extraordinary performance due to enhanced light absorption in visible and infrared part of solar spectrum. Various designing parameters such as number of distributed Bragg reflector (DBR pairs, anti-reflection layer thickness, grating thickness, active layer thickness, grating duty cycle and period were optimized for the optimal performance of solar cell. An ultrathin silicon solar cell with 40 nm active layer could produce an enhancement in cell efficiency ∼15 % and current density ∼23 mA/cm2. This design approach would be useful for the realization of new generation of solar cells with reduced active layer thickness.
Efficiency optimization of a photovoltaic water pumping system for irrigation in Ouargla, Algeria
Louazene, M. L.; Garcia, M. C. Alonso; Korichi, D.
2017-02-01
This work is technical study to contribute to the optimization of pumping systems powered by solar energy (clean) and used in the field of agriculture. To achieve our goals, we studied the techniques that must be entered on a photovoltaic system for maximum energy from solar panels. Our scientific contribution in this research is the realization of an efficient photovoltaic pumping system for irrigation needs. To achieve this and extract maximum power from the PV generator, two axes have been optimized: 1. Increase in the uptake of solar radiation by choice an optimum tilt angle of the solar panels, and 2. it is necessary to add an adaptation device, MPPT controller with a DC-DC converter, between the source and the load.
Directory of Open Access Journals (Sweden)
Mariana B. Laborde
2015-03-01
Full Text Available A healthy dehydrated food of high nutritional-quality and added-value was developed: low-calories raisin obtained by an ultrasonic assisted combined-dehydration with two-stage osmotic treatment (D3S complemented by drying. Pink Red Globe grape produced at Mendoza (Argentina, experienced a substitution of sugar by natural sweetener Stevia in two osmotic stages under different conditions (treatment with/without ultrasound; sweetener concentration 18, 20, 22% w/w; time 35, 75, 115 minutes, evaluating soluble solids (SS, moisture (M, total polyphenols (PF, antioxidant efficiency (AE and sugar profile. The multiple optimization of the process by response surface methodology and desirability analysis, allowed to minimize M, maximize SS (Stevia incorporation, and preserve the maximum amount of PF. After the first stage, the optimal treatment reduced the majority sugars of the grape in 32% (sucrose, glucose, and the 57% at the end of the dehydration process.
Value-based differential pricing: efficient prices for drugs in a global context.
Danzon, Patricia; Towse, Adrian; Mestre-Ferrandiz, Jorge
2015-03-01
This paper analyzes pharmaceutical pricing between and within countries to achieve second-best static and dynamic efficiency. We distinguish countries with and without universal insurance, because insurance undermines patients' price sensitivity, potentially leading to prices above second-best efficient levels. In countries with universal insurance, if each payer unilaterally sets an incremental cost-effectiveness ratio (ICER) threshold based on its citizens' willingness-to-pay for health; manufacturers price to that ICER threshold; and payers limit reimbursement to patients for whom a drug is cost-effective at that price and ICER, then the resulting price levels and use within each country and price differentials across countries are roughly consistent with second-best static and dynamic efficiency. These value-based prices are expected to differ cross-nationally with per capita income and be broadly consistent with Ramsey optimal prices. Countries without comprehensive insurance avoid its distorting effects on prices but also lack financial protection and affordability for the poor. Improving pricing efficiency in these self-pay countries includes improving regulation and consumer information about product quality and enabling firms to price discriminate within and between countries. © 2013 The Authors. Health Economics published by John Wiley & Sons Ltd.
Penuelas, J.; Sardans, J.
2016-12-01
There are several processes underlying the shifts in organism's functions, species composition and ecosystem adaptation to the fast rates of environmental changes resulting from global change drivers. These environmental changes imply a shift in the use and cycling of resources, and in particular of nutrients, by organisms, communities and ecosystems. We will review the different use of bio-elements related to global change drivers such as climate change (warming and drought), increased concentrations of atmospheric CO2, or expansion of invasive species among others. Thereafter, we will discuss the resulting progressive change in nutrient cycling and its coupling with organism's, species, communities and ecosystem function in the frame of the biogeochemical niche hypothesis (Peñuelas et al., 2008; 2010). This hypothesis, based on the fact that each bio-element participates in different proportion in distinct organism's structures and functions, claims that each species has an optimal elemental composition and stoichiometry that allows reaching an optimal functioning. Species are nonetheless expected to exhibit a certain degree of stoichiometric flexibility (adaptive capacity) necessary to respond to environmental changes and competition, probably with a trade-off with stability. We will present data for the dominant tree species in Europe showing that the elemental foliar composition differences among species can be explained by their phylogenetic distances, current climate differences in their distribution areas and niche speciation in sympatric species, but also by some more recent human-driven impacts such as N deposition, thus showing the suitability and sensitivity of the "biogeochemical niche" concept to understand recent organism's, species, and ecosystem responses to novel environmental conditions imposed by human activity. We will finally discuss possible clues to improve the projections of ecosystem shifts in global change scenarios based on this concept.
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.
Directory of Open Access Journals (Sweden)
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.
Lagos, Soledad R.; Velis, Danilo R.
2018-02-01
We perform the location of microseismic events generated in hydraulic fracturing monitoring scenarios using two global optimization techniques: Very Fast Simulated Annealing (VFSA) and Particle Swarm Optimization (PSO), and compare them against the classical grid search (GS). To this end, we present an integrated and optimized workflow that concatenates into an automated bash script the different steps that lead to the microseismic events location from raw 3C data. First, we carry out the automatic detection, denoising and identification of the P- and S-waves. Secondly, we estimate their corresponding backazimuths using polarization information, and propose a simple energy-based criterion to automatically decide which is the most reliable estimate. Finally, after taking proper care of the size of the search space using the backazimuth information, we perform the location using the aforementioned algorithms for 2D and 3D usual scenarios of hydraulic fracturing processes. We assess the impact of restricting the search space and show the advantages of using either VFSA or PSO over GS to attain significant speed-ups.
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.
Lin, Chia-Ying; Hsiao, Chun-Ching; Chen, Po-Quan; Hollister, Scott J
2004-08-15
An approach combining global layout and local microstructure topology optimization was used to create a new interbody fusion cage design that concurrently enhanced stability, biofactor delivery, and mechanical tissue stimulation for improved arthrodesis. To develop a new interbody fusion cage design by topology optimization with porous internal architecture. To compare the performance of this new design to conventional threaded cage designs regarding early stability and long-term stress shielding effects on ingrown bone. Conventional interbody cage designs mainly fall into categories of cylindrical or rectangular shell shapes. The designs contribute to rigid stability and maintain disc height for successful arthrodesis but may also suffer mechanically mediated failures of dislocation or subsidence, as well as the possibility of bone resorption. The new optimization approach created a cage having designed microstructure that achieved desired mechanical performance while providing interconnected channels for biofactor delivery. The topology optimization algorithm determines the material layout under desirable volume fraction (50%) and displacement constraints favorable to bone formation. A local microstructural topology optimization method was used to generate periodic microstructures for porous isotropic materials. Final topology was generated by the integration of the two-scaled structures according to segmented regions and the corresponding material density. Image-base finite element analysis was used to compare the mechanical performance of the topology-optimized cage and conventional threaded cage. The final design can be fabricated by a variety of Solid Free-Form systems directly from the image output. The new design exhibited a narrower, more uniform displacement range than the threaded cage design and lower stress at the cage-vertebra interface, suggesting a reduced risk of subsidence. Strain energy density analysis also indicated that a higher portion of
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Po-Chih Kuo
2014-12-01
Full Text Available In this article, the conceptual design of biomass steam gasification (BSG processes using raw oil palm (ROP and torrefied oil palm (TOP are examined in an Aspen Plus simulator. Through thermodynamic analysis, it is verified that the BSG process with torrefied feedstock can effectively enhance the hydrogen yield. When the heat recovery design is added into the BSG process, the system energetic efficiency (SEE is significantly improved. Finally, an optimization algorithm with respect to SEE and hydrogen yield is solved, and the optimum operating conditions are validated by simulations.
Optimizing efficiency on conventional transformer based low power AC/DC standby power supplies
DEFF Research Database (Denmark)
Nielsen, Nils
2004-01-01
on two common power supply topologies designed for this power level. The two described topologies uses either a series (or linear) or a buck regulation approach. Common to the test power supplies is they either are using a standard cheap off-the-shelf transformer, or one, which are loss optimized by very......This article describes the research results for simple and cheap methods to reduce the idle- and load-losses in very low power conventional transformer based power supplies intended for standby usage. In this case "very low power" means 50 Hz/230 V-AC to 5 V-DC@1 W. The efficiency is measured...
The global anthropogenic gallium system: determinants of demand, supply and efficiency improvements.
Løvik, Amund N; Restrepo, Eliette; Müller, Daniel B
2015-05-05
Gallium has been labeled as a critical metal due to rapidly growing consumption, importance for low-carbon technologies such as solid state lighting and photovoltaics, and being produced only as a byproduct of other metals (mainly aluminum). The global system of primary production, manufacturing, use and recycling has not yet been described or quantified in the literature. This prevents predictions of future demand, supply and possibilities for efficiency improvements on a system level. We present a description of the global anthropogenic gallium system and quantify the system using a combination of statistical data and technical parameters. We estimated that gallium was produced from 8 to 21% of alumina plants in 2011. The most important applications of gallium are NdFeB permanent magnets, integrated circuits and GaAs/GaP-based light-emitting diodes, demanding 22-37%, 16-27%, and 11-21% of primary metal production, respectively. GaN-based light-emitting diodes and photovoltaics are less important, both with 2-6%. We estimated that 120-170 tons, corresponding to 40-60% of primary production, ended up in production wastes that were either disposed of or stored. While demand for gallium is expected to rise in the future, our results indicated that it is possible to increase primary production substantially with conventional technology, as well as improve the system-wide material efficiency.
Global convergence of maixmum light use efficiency using fraction of PAR absorbed by chlorophyll
Zhang, Y.; Xiao, X.; Cescatti, A.; Wolf, S.; Wu, J.; Wu, X.; Gioli, B.; Wohlfahrt, G.; Zhou, S.; Steinbrecher, R.; Gough, C. M.
2016-12-01
The efficiency that plants utilize light for photosynthesis has been long established and been used for simple light use efficiency (LUE) models to estimate gross primary production (GPP) from site level to global scales. However, the LUE can be defined at different scales which cause a large variation of maximum LUE values across different models and ecosystem types. Here we use solar-induced chlorophyll fluorescence (SIF) as a proxy of photosynthetically active radiation (PAR) absorbed by chlorophyll (APARchl) and derived an estimation of fraction of APARchl (fPARchl) from four remote sensing based optical vegetation activity indicators (OVAIs). By comparing this fPARchl estimation with eddy flux based GPP from 136 sites, we found that the maximum daily LUE tends to converge to a common value ignorance of the biome types. The seasonal variation of LUEPAR (GPP/PAR) in tropical forest where little environmental limits presents can also be tracked by the change of fPARchl. This global convergence can be used to build simple models to estimate GPP and to constrain process-based models using satellite observations.
Efficiency and Optimality of 2-period Gait from Kinetic Energy Point of View
Asano, Fumihiko
This paper investigates the efficiency of a 2-period limit-cycle gait from the kinetic energy viewpoint. First, we formulate a steady 2-period gait by using simple recurrence formulas for the kinetic energy of an asymmetric rimless wheel. Second, we theoretically show that, in the case that the mean value of the hip angle is constant, the generated 2-period steady gait is less efficient than a 1-period symmetric one in terms of kinetic energy. Furthermore, we show that the symmetric gait is not always optimal from another viewpoint. Finally, we investigate the validity of the derived theory through numerical simulations of virtual passive dynamic walking using a compass-like biped robot.
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Vittorio M. N. Passaro
2012-02-01
Full Text Available Slot waveguides are becoming more and more attractive optical components, especially for chemical and bio-chemical sensing. In this paper an accurate analysis of slot waveguide fabrication tolerances is carried out, in order to find optimum design criteria for either homogeneous or absorption sensing mechanisms, in cases of low and high aspect ratio slot waveguides. In particular, we have focused on Silicon On Insulator (SOI technology, representing the most popular technology for this kind of devices, simultaneously achieving high integration capabilities, small dimensions and low cost. An accurate analysis of single mode behavior for high aspect ratio slot waveguide has been also performed, in order to provide geometric limits for waveguide design purposes. Finally, the problem of coupling into a slot waveguide is addressed and a very compact and efficient slot coupler is proposed, whose geometry has been optimized to give a strip-slot-strip coupling efficiency close to 100%.
An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets
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Ke Li
2007-10-01
Full Text Available Because of the development of modern-day satellites and other data acquisition systems, global climate research often involves overwhelming volume and complexity of high dimensional datasets. As a data preprocessing and analysis method, the clustering method is playing a more and more important role in these researches. In this paper, we propose a spatial clustering algorithm that, to some extent, cures the problem of dimensionality in high dimensional clustering. The similarity measure of our algorithm is based on the number of top-k nearest neighbors that two grids share. The neighbors of each grid are computed based on the time series associated with each grid, and computing the nearest neighbor of an object is the most time consuming step. According to Tobler's "First Law of Geography," we add a spatial window constraint upon each grid to restrict the number of grids considered and greatly improve the efficiency of our algorithm. We apply this algorithm to a 100-year global climate dataset and partition the global surface into sub areas under various spatial granularities. Experiments indicate that our spatial clustering algorithm works well.
Evidence on the Efficient Market Hypothesis from 44 Global Financial Market Indexes
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Huijian Dong
2013-01-01
Full Text Available This paper employs Granger causality tests to identify the impacts of historical information from global financial markets on their current levels in 30-day windows. The dataset consists primarily of the daily index levels of the (1 open, (2 closed, (3 intraday high, (4 intraday low, and (5 trading volume series for the world’s 37 most influential equity market indexes, two crude oil prices, a gold price, and four major money market prices in the United States are used as control groups. Our results indicate a persistent impact of historical information from global markets on their current levels, and this impact duplicates itself in a cyclical pattern consistently over decades. Such persistence in the patterns causes some market indexes to be upgraded to global or regional market leaders. These findings can be interpreted as constituting violations of the weak-form efficient market hypothesis. The results also reveal recursive impacts of information in these markets and the existence of an information digestion effect.