Competing intelligent search agents in global optimization

Energy Technology Data Exchange (ETDEWEB)

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.

Global climate change and the equity-efficiency puzzle

International Nuclear Information System (INIS)

Manne, Alan S.; Stephan, Gunter

2005-01-01

There is a broad consensus that the costs of abatement of global climate change can be reduced efficiently through the assignment of quota rights and through international trade in these rights. There is, however, no consensus on whether the initial assignment of emissions permits can affect the Pareto-optimal global level of abatement. This paper provides some insight into the equity-efficiency puzzle. Qualitative results are obtained from a small-scale model; then quantitative evidence of separability is obtained from MERGE, a multiregion integrated assessment model. It is shown that if all the costs of climate change can be expressed in terms of GDP losses, Pareto-efficient abatement strategies are independent of the initial allocation of emissions rights. This is the case sometimes described as 'market damages'. If, however, different regions assign different values to nonmarket damages such as species losses, different sharing rules may affect the Pareto-optimal level of greenhouse gas abatement. Separability may then be demonstrated only in specific cases (e.g. identical welfare functions or quasi-linearity of preferences or small shares of wealth devoted to abatement)

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.

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

Science.gov (United States)

Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior.

Science.gov (United States)

Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

2013-01-01

We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

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.

Deterministic global optimization an introduction to the diagonal approach

CERN Document Server

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...

Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization

Science.gov (United States)

Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei

2014-04-01

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

Science.gov (United States)

Chen, Shuangqing; Wei, Lixin; Guan, Bing

2018-01-01

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. PMID:29675036

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

Science.gov (United States)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

An Evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions

Science.gov (United States)

Butler, Roger A. R.; Slaminka, Edward E.

1992-03-01

The performance of Sniffer—a new global optimization algorithm—is compared with that of Simulated Annealing. Using the number of function evaluations as a measure of efficiency, the new algorithm is shown to be significantly better at finding the global minimum of seven standard test functions. Several of the test functions used have many local minima and very steep walls surrounding the global minimum. Such functions are intended to thwart global minimization algorithms.

Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

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.

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

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......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...

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

Science.gov (United States)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

A perturbed martingale approach to global optimization

Energy Technology Data Exchange (ETDEWEB)

Sarkar, Saikat [Computational Mechanics Lab, Department of Civil Engineering, Indian Institute of Science, Bangalore 560012 (India); Roy, Debasish, E-mail: royd@civil.iisc.ernet.in [Computational Mechanics Lab, Department of Civil Engineering, Indian Institute of Science, Bangalore 560012 (India); Vasu, Ram Mohan [Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore 560012 (India)

2014-08-01

A new global stochastic search, guided mainly through derivative-free directional information computable from the sample statistical moments of the design variables within a Monte Carlo setup, is proposed. The search is aided by imparting to the directional update term additional layers of random perturbations referred to as ‘coalescence’ and ‘scrambling’. A selection step, constituting yet another avenue for random perturbation, completes the global search. The direction-driven nature of the search is manifest in the local extremization and coalescence components, which are posed as martingale problems that yield gain-like update terms upon discretization. As anticipated and numerically demonstrated, to a limited extent, against the problem of parameter recovery given the chaotic response histories of a couple of nonlinear oscillators, the proposed method appears to offer a more rational, more accurate and faster alternative to most available evolutionary schemes, prominently the particle swarm optimization. - Highlights: • Evolutionary global optimization is posed as a perturbed martingale problem. • Resulting search via additive updates is a generalization over Gateaux derivatives. • Additional layers of random perturbation help avoid trapping at local extrema. • The approach ensures efficient design space exploration and high accuracy. • The method is numerically assessed via parameter recovery of chaotic oscillators.

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

An efficient algorithm for global periodic orbits generation near irregular-shaped asteroids

Science.gov (United States)

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.

Essays and surveys in global optimization

CERN Document Server

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.

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

Science.gov (United States)

Ng, Hok Kwan; Sridhar, Banavar

2016-01-01

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.

Global-local optimization of flapping kinematics in hovering flight

KAUST Repository

Ghommem, Mehdi; Hajj, M. R.; Mook, Dean T.; Stanford, Bret K.; Bé ran, Philip S.; Watson, Layne T.

2013-01-01

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.

Global-local optimization of flapping kinematics in hovering flight

KAUST Repository

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.

Global optimization based on noisy evaluations: An empirical study of two statistical approaches

International Nuclear Information System (INIS)

Vazquez, Emmanuel; Villemonteix, Julien; Sidorkiewicz, Maryan; Walter, Eric

2008-01-01

The optimization of the output of complex computer codes has often to be achieved with a small budget of evaluations. Algorithms dedicated to such problems have been developed and compared, such as the Expected Improvement algorithm (El) or the Informational Approach to Global Optimization (IAGO). However, the influence of noisy evaluation results on the outcome of these comparisons has often been neglected, despite its frequent appearance in industrial problems. In this paper, empirical convergence rates for El and IAGO are compared when an additive noise corrupts the result of an evaluation. IAGO appears more efficient than El and various modifications of El designed to deal with noisy evaluations. Keywords. Global optimization; computer simulations; kriging; Gaussian process; noisy evaluations.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

Science.gov (United States)

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Jui-Yu Wu

2013-01-01

Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO 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.

Efficiency, sustainability and global warming

International Nuclear Information System (INIS)

Woodward, Richard T.; Bishop, Richard C.

1995-01-01

Economic analyses of global warming have typically been grounded in the theory of economic efficiency. Such analyses may be inappropriate because many of the underlying concerns about climate change are rooted not in efficiency, but in the intergenerational allocation of economic endowments. A simple economic model is developed which demonstrates that an efficient economy is not necessarily a sustainable economy. This result leads directly to questions about the policy relevance of several economic studies of the issue. We then consider policy alternatives to address global warming in the context of economies with the dual objectives of efficiency and sustainability, with particular attention to carbon-based taxes

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.

Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm

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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

Introduction to Nonlinear and Global Optimization

NARCIS (Netherlands)

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

Conference on Convex Analysis and Global Optimization

CERN Document Server

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...

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.

Material discovery by combining stochastic surface walking global optimization with a neural network.

Science.gov (United States)

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Stochastic and global optimization

National Research Council Canada - National Science Library

Dzemyda, Gintautas; Šaltenis, Vydūnas; Zhilinskas, A; Mockus, Jonas

2002-01-01

... and Effectiveness of Controlled Random Search E. M. T. Hendrix, P. M. Ortigosa and I. García 129 9. Discrete Backtracking Adaptive Search for Global Optimization B. P. Kristinsdottir, Z. B. Zabinsky and...

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.......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...... their Curie temperature. Simulations are implemented to investigate how to layer the FOPT materials for obtaining higher cooling capacity. Moreover, based on entropy generation minimization, optimization of the regenerator geometry and related operating parameters is presented for improving the AMR efficiency...

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....... As the efficiency of each converter changes with output power, virtual resistances (VRs) are set as decision variables for adjusting power sharing proportion among converters. It is noteworthy that apart from restoring the voltage deviation, secondary control plays an important role to stabilize dc bus voltage when...

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

Science.gov (United States)

Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A

2012-07-02

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of

Global optimization and simulated annealing

NARCIS (Netherlands)

Dekkers, A.; Aarts, E.H.L.

1988-01-01

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of Rn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing

Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaced by CentER DP 2011-015

NARCIS (Netherlands)

Kleijnen, Jack P.C.; van Beers, W.C.M.; van Nieuwenhuyse, I.

2010-01-01

This paper 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). This

A global optimization method for evaporative cooling systems based on the entransy theory

International Nuclear Information System (INIS)

Yuan, Fang; Chen, Qun

2012-01-01

Evaporative cooling technique, one of the most widely used methods, is essential to both energy conservation and environment protection. This contribution introduces a global optimization method for indirect evaporative cooling systems with coupled heat and mass transfer processes based on the entransy theory to improve their energy efficiency. First, we classify the irreversible processes in the system into the heat transfer process, the coupled heat and mass transfer process and the mixing process of waters in different branches, where the irreversibility is evaluated by the entransy dissipation. Then through the total system entransy dissipation, we establish the theoretical relationship of the user demands with both the geometrical structures of each heat exchanger and the operating parameters of each fluid, and derive two optimization equation groups focusing on two typical optimization problems. Finally, an indirect evaporative cooling system is taken as an example to illustrate the applications of the newly proposed optimization method. It is concluded that there exists an optimal circulating water flow rate with the minimum total thermal conductance of the system. Furthermore, with different user demands and moist air inlet conditions, it is the global optimization, other than parametric analysis, will obtain the optimal performance of the system. -- Highlights: ► Introduce a global optimization method for evaporative cooling systems. ► Establish the direct relation between user demands and the design parameters. ► Obtain two groups of optimization equations for two typical optimization objectives. ► Solving the equations offers the optimal design parameters for the system. ► Provide the instruction for the design of coupled heat and mass transfer systems.

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

Comprehensive effective and efficient global public health surveillance.

Science.gov (United States)

McNabb, Scott J N

2010-12-03

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 to the ownership and stewardship

Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.

2013-01-01

Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems

4th International Conference on Frontiers in Global Optimization

CERN Document Server

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-...

A theoretical global optimization method for vapor-compression refrigeration systems based on entransy theory

International Nuclear Information System (INIS)

Xu, Yun-Chao; Chen, Qun

2013-01-01

The vapor-compression refrigeration systems have been one of the essential energy conversion systems for humankind and exhausting huge amounts of energy nowadays. Surrounding the energy efficiency promotion of the systems, there are lots of effectual optimization methods but mainly relied on engineering experience and computer simulations rather than theoretical analysis due to the complex and vague physical essence. We attempt to propose a theoretical global optimization method based on in-depth physical analysis for the involved physical processes, i.e. heat transfer analysis for condenser and evaporator, through introducing the entransy theory and thermodynamic analysis for compressor and expansion valve. The integration of heat transfer and thermodynamic analyses forms the overall physical optimization model for the systems to describe the relation between all the unknown parameters and known conditions, which makes theoretical global optimization possible. With the aid of the mathematical conditional extremum solutions, an optimization equation group and the optimal configuration of all the unknown parameters are analytically obtained. Eventually, via the optimization of a typical vapor-compression refrigeration system with various working conditions to minimize the total heat transfer area of heat exchangers, the validity and superior of the newly proposed optimization method is proved. - Highlights: • A global optimization method for vapor-compression systems is proposed. • Integrating heat transfer and thermodynamic analyses forms the optimization model. • A mathematical relation between design parameters and requirements is derived. • Entransy dissipation is introduced into heat transfer analysis. • The validity of the method is proved via optimization of practical cases

On benchmarking Stochastic Global Optimization Algorithms

NARCIS (Netherlands)

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

Assessing global resource utilization efficiency in the industrial sector

International Nuclear Information System (INIS)

Rosen, Marc A.

2013-01-01

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. - Highlights: ► The global industrial sector and its industries are assessed by using energy and exergy methods. ► Global industrial sector efficiencies are evaluated as 51% based on energy and 30% based on exergy. ► Exergy analysis shows global industrial energy to be less efficient than does energy analysis. ► A misleadingly low margin for efficiency improvement is indicated by energy analysis. ► A significant and rational margin for efficiency improvement exists from an exergy perspective

Development of Pathways to Achieve the SE4ALL Energy Efficiency Objective: Global and Regional Potential for Energy Efficiency Improvements

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....... Double the share of renewable energy in global final energy from 18% to 36% by 2030. The integrated assessment model, ETSAP-TIAM, was used in this study to compare, from an economic optimization point of view, different scenarios for the development of the energy system between 2010 and 2030....... 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...

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.

A Mixed Integer Efficient Global Optimization Framework: Applied to the Simultaneous Aircraft Design, Airline Allocation and Revenue Management Problem

Science.gov (United States)

Roy, Satadru

Traditional approaches to design and optimize a new system, often, use a system-centric objective and do not take into consideration how the operator will use this new system alongside of other existing systems. This "hand-off" between the design of the new system and how the new system operates alongside other systems might lead to a sub-optimal performance with respect to the operator-level objective. In other words, the system that is optimal for its system-level objective might not be best for the system-of-systems level objective of the operator. Among the few available references that describe attempts to address this hand-off, most follow an MDO-motivated subspace decomposition approach of first designing a very good system and then provide this system to the operator who decides the best way to use this new system along with the existing systems. The motivating example in this dissertation presents one such similar problem that includes aircraft design, airline operations and revenue management "subspaces". The research here develops an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem. The monolithic approach makes the problem a Mixed Integer/Discrete Non-Linear Programming (MINLP/MDNLP) problem, which are extremely difficult to solve. The presence of expensive, sophisticated engineering analyses further aggravate the problem. To tackle this challenge problem, the work here presents a new optimization framework that simultaneously solves the subspaces to capture the "synergism" in the problem that the previous decomposition approaches may not have exploited, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. The framework combines concepts from efficient global optimization, Kriging partial least squares, and gradient-based optimization. This approach then demonstrates its ability to solve an 11 route airline network

Global optimization and sensitivity analysis

International Nuclear Information System (INIS)

Cacuci, D.G.

1990-01-01

A new direction for the analysis of nonlinear models of nuclear systems is suggested to overcome fundamental limitations of sensitivity analysis and optimization methods currently prevalent in nuclear engineering usage. This direction is toward a global analysis of the behavior of the respective system as its design parameters are allowed to vary over their respective design ranges. Presented is a methodology for global analysis that unifies and extends the current scopes of sensitivity analysis and optimization by identifying all the critical points (maxima, minima) and solution bifurcation points together with corresponding sensitivities at any design point of interest. The potential applicability of this methodology is illustrated with test problems involving multiple critical points and bifurcations and comprising both equality and inequality constraints

Regional and global exergy and energy efficiencies

Energy Technology Data Exchange (ETDEWEB)

Nakicenovic, N; Kurz, R [International Inst. for Applied Systems Analysis, Laxenburg (Austria). Environmentally Compatible Energy Strategies (Ecuador) Project; Gilli, P V [Graz Univ. of Technology (Austria)

1996-03-01

We present estimates of global energy efficiency by applying second-law (exergy) analysis to regional and global energy balances. We use a uniform analysis of national and regional energy balances and aggregate these balances first for three main economic regions and subsequently into world totals. The procedure involves assessment of energy and exergy efficiencies at each step of energy conversion, from primary exergy to final and useful exergy. Ideally, the analysis should be extended to include actual delivered energy services; unfortunately, data are scarce and only rough estimates can be given for this last stage of energy conversion. The overall result is that the current global primary to useful exergy efficiency is about one-tenth of the theoretical maximum and the service efficiency is even lower. (Author)

Microwave tomography global optimization, parallelization and performance evaluation

CERN Document Server

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.

Evolutionary global optimization, manifolds and applications

CERN Document Server

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....

Efficient AUC optimization for classification

NARCIS (Netherlands)

Calders, T.; Jaroszewicz, S.; Kok, J.N.; Koronacki, J.; Lopez de Mantaras, R.; Matwin, S.; Mladenic, D.; Skowron, A.

2007-01-01

In this paper we show an efficient method for inducing classifiers that directly optimize the area under the ROC curve. Recently, AUC gained importance in the classification community as a mean to compare the performance of classifiers. Because most classification methods do not optimize this

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...... lead to around 30 % of efficiency improvement at 1.3 W output power without significant effects on both audio performance and the efficiency at high power levels....

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

Science.gov (United States)

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.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

Science.gov (United States)

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

Combustion system optimization of a P-62 lignite boiler in ContourGlobal Maritsa East 3 with NOx-reduction and efficiency improvement

International Nuclear Information System (INIS)

Petkov, Ch.; Thierbach, Hans-Ulrich; Totev, T.

2013-01-01

Steinmueller Engineering GmbH, Gummersbach, Germany, successfully concluded in consortium with Siemens EOOD, Sofia, the combustion system modification of a P62 lignite fired boiler in TPP ContourGlobal Maritsa East 3, which was targeting mainly the reduction of the NOx emissions below 180 mg/Nm 3 at 6 % O 2 . The modification is part of an EPC contract covering the design, fabrication, installation and commissioning works needed to upgrade the boilers at the power station. The Modification concept involves optimization of PF- and Vapor distribution, replacement of the coal burners, installation of new Over-fire air (OFA) system and Side-wall air (SWA) system and minor modification of the existing control system to allow control of the OFAflow. The main results of the modification are: Reduction of the NOx emissions (at ESP exit) from approximately 390 g/Nm³ to below 180 mg/Nm³ at 6% O 2 , Efficiency increase of the furnace by reduction of the excess air ratio from 1.2 to 1.15 (at furnace outlet) and overall increase of the boiler efficiency. (authors)

Navigating the Impact of Globalization on Bank Efficiency in China%Navigating the Impact of Globalization on Bank Efficiency in China

Institute of Scientific and Technical Information of China (English)

Fadzlan Sufian; Muzafar Shah Habibullah

2011-01-01

The paper provides for the first time empirical evidence on the impact of economic globalization on bank efficiency in a developing economy. Using the data envelopment analysis method, we compute the efficiency of the Chinese banking sector during 2000- 2002 The empirical findings suggest that the inefficiency of the Chinese banking sector stems largely from scale rather than pure technical inefficiencies. Examining different components of economic globalization, we find that greater economic integration through higher trade flows, cultural proximity and political globalization have significant andpositive influence on bank efficiency levels. The empirical findings suggest that liberalization （restrictions） of the capital account exerts a negative （positive） influence on bank efficiency levels in China.

On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Science.gov (United States)

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

Assessing global resource utilization efficiency in the industrial sector.

Science.gov (United States)

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.

3rd World Congress on Global Optimization in Engineering & Science

CERN Document Server

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.

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...... on approximate reanalysis. For cases where memory limitations require the utilization of iterative equation solvers, we suggest efficient procedures based on alternative termination criteria for such solvers. These approaches are tested on two- and three-dimensional topology optimization problems including...

Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review

Directory of Open Access Journals (Sweden)

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.

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 by taking exergetic efficiency as the optimization objective combining exergy concept with finite-time thermodynamics (FTT). Exergetic efficiency is ...

Finite-size effect on optimal efficiency of heat engines.

Science.gov (United States)

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.

Closing the Gap GEF Experiences in Global Energy Efficiency

CERN Document Server

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...

Acceleration techniques in the univariate Lipschitz global optimization

Science.gov (United States)

Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela

2016-10-01

Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.

3D prostate TRUS segmentation using globally optimized volume-preserving prior.

Science.gov (United States)

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.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

Science.gov (United States)

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

The SEAD global efficiency medal competition: accelerating market transformation for efficient televisions

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.

Modeling and energy efficiency optimization of belt conveyors

International Nuclear Information System (INIS)

Zhang, Shirong; Xia, Xiaohua

2011-01-01

Highlights: → We take optimization approach to improve operation efficiency of belt conveyors. → An analytical energy model, originating from ISO 5048, is proposed. → Then an off-line and an on-line parameter estimation schemes are investigated. → In a case study, six optimization problems are formulated with solutions in simulation. - Abstract: The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment and operation levels. Specifically, variable speed control, an equipment level intervention, is recommended to improve operation efficiency of belt conveyors. However, the current implementations mostly focus on lower level control loops without operational considerations at the system level. This paper intends to take a model based optimization approach to improve the efficiency of belt conveyors at the operational level. An analytical energy model, originating from ISO 5048, is firstly proposed, which lumps all the parameters into four coefficients. Subsequently, both an off-line and an on-line parameter estimation schemes are applied to identify the new energy model, respectively. Simulation results are presented for the estimates of the four coefficients. Finally, optimization is done to achieve the best operation efficiency of belt conveyors under various constraints. Six optimization problems of a typical belt conveyor system are formulated, respectively, with solutions in simulation for a case study.

Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

Institute of Scientific and Technical Information of China (English)

Zhang Junbin; Cai Jinyan; Meng Yafeng

2016-01-01

Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.

Comparison of global optimization approaches for robust calibration of hydrologic model parameters

Science.gov (United States)

Jung, I. W.

2015-12-01

Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Energy Efficiency - Spectral Efficiency Trade-off: A Multiobjective Optimization Approach

KAUST Repository

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.

Energy Efficiency - Spectral Efficiency Trade-off: A Multiobjective Optimization Approach

KAUST Repository

Amin, Osama; Bedeer, Ebrahim; Ahmed, Mohamed; Dobre, Octavia

2015-01-01

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 hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Parallel Global Optimization with the Particle Swarm Algorithm (Preprint)

National Research Council Canada - National Science Library

Schutte, J. F; Reinbolt, J. A; Fregly, B. J; Haftka, R. T; George, A. D

2004-01-01

.... To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the Particle Swarm Optimization (PSO) algorithm...

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

Science.gov (United States)

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-05-04

Particle swarm optimization is a powerful metaheuristic population-based global optimization algorithm. However, when applied to non-separable objective functions its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant particle swarm optimization algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates a superior performance across several nonlinear, multimodal benchmark functions compared to the rotation-invariant Particle Swam Optimization (PSO) algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in ReaxFF-lg reactive force field is carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents a better performance compared to a Genetic Algorithm optimization method in the optimization of a ReaxFF-lg correction model parameters. The computational framework is implemented in a standalone C++ code that allows a straightforward development of ReaxFF reactive force fields.

Globally convergent optimization algorithm using conservative convex separable diagonal quadratic approximations

NARCIS (Netherlands)

Groenwold, A.A.; Wood, D.W.; Etman, L.F.P.; Tosserams, S.

2009-01-01

We implement and test a globally convergent sequential approximate optimization algorithm based on (convexified) diagonal quadratic approximations. The algorithm resides in the class of globally convergent optimization methods based on conservative convex separable approximations developed by

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models.

Science.gov (United States)

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.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

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.

Interactive Cosegmentation Using Global and Local Energy Optimization

OpenAIRE

Xingping Dong,; Jianbing Shen,; Shao, Ling; Yang, Ming-Hsuan

2015-01-01

We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint, which attempts to match the histograms of common objects. To minimize the local energy, we apply the spline regression to learn the smoothne...

Global energy efficiency governance in the context of climate politics

International Nuclear Information System (INIS)

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 taking action is not commensurate with the potential in the sector or the urgency with which measures need to be taken to deal with climate change. The current global energy (efficiency) governance framework is diffuse. This paper submits that there are four substantive reasons why global governance should play a complementary role in promoting energy efficiency worldwide. Furthermore, given that market mechanisms are unable to rapidly mobilize energy efficiency projects and that there are no clear vested interests in this field which involves a large number of actors, there is need for a dedicated agency to promote energy efficiency and conservation. This paper provides an overview of energy efficiency options presented by IPCC, the current energy efficiency governance structure at global level, and efforts taken at supranational and national levels, and makes suggestions for a governance framework.

Optimizing rice yields while minimizing yield-scaled global warming potential.

Science.gov (United States)

Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A

2014-05-01

To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. © 2013 John Wiley & Sons Ltd.

SGO: A fast engine for ab initio atomic structure global optimization by differential evolution

Science.gov (United States)

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.

An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos

2009-01-01

The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature

An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br

2009-04-15

The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.

Lean and Efficient Software: Whole-Program Optimization of Executables

Science.gov (United States)

2015-09-30

Lean and Efficient Software: Whole-Program Optimization of Executables” Project Summary Report #5 (Report Period: 7/1/2015 to 9/30/2015...TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Lean and Efficient Software: Whole-Program Optimization of Executables 5a...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Lean and Efficient Software: Whole-Program

Carbon and nutrient use efficiencies optimally balance stoichiometric imbalances

Science.gov (United States)

Manzoni, Stefano; Čapek, Petr; Lindahl, Björn; Mooshammer, Maria; Richter, Andreas; Šantrůčková, Hana

2016-04-01

Decomposer organisms face large stoichiometric imbalances because their food is generally poor in nutrients compared to the decomposer cellular composition. The presence of excess carbon (C) requires adaptations to utilize nutrients effectively while disposing of or investing excess C. As food composition changes, these adaptations lead to variable C- and nutrient-use efficiencies (defined as the ratios of C and nutrients used for growth over the amounts consumed). For organisms to be ecologically competitive, these changes in efficiencies with resource stoichiometry have to balance advantages and disadvantages in an optimal way. We hypothesize that efficiencies are varied so that community growth rate is optimized along stoichiometric gradients of their resources. Building from previous theories, we predict that maximum growth is achieved when C and nutrients are co-limiting, so that the maximum C-use efficiency is reached, and nutrient release is minimized. This optimality principle is expected to be applicable across terrestrial-aquatic borders, to various elements, and at different trophic levels. While the growth rate maximization hypothesis has been evaluated for consumers and predators, in this contribution we test it for terrestrial and aquatic decomposers degrading resources across wide stoichiometry gradients. The optimality hypothesis predicts constant efficiencies at low substrate C:N and C:P, whereas above a stoichiometric threshold, C-use efficiency declines and nitrogen- and phosphorus-use efficiencies increase up to one. Thus, high resource C:N and C:P lead to low C-use efficiency, but effective retention of nitrogen and phosphorus. Predictions are broadly consistent with efficiency trends in decomposer communities across terrestrial and aquatic ecosystems.

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 (... 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....

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.

Adjusting process count on demand for petascale global optimization

KAUST Repository

Sosonkina, Masha; Watson, Layne T.; Radcliffe, Nicholas R.; Haftka, Rafael T.; Trosset, Michael W.

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.

Efficient solution to the stagnation problem of the particle swarm optimization algorithm for phase diversity.

Science.gov (United States)

Qi, Xin; Ju, Guohao; Xu, Shuyan

2018-04-10

The phase diversity (PD) technique needs optimization algorithms to minimize the error metric and find the global minimum. Particle swarm optimization (PSO) is very suitable for PD due to its simple structure, fast convergence, and global searching ability. However, the traditional PSO algorithm for PD still suffers from the stagnation problem (premature convergence), which can result in a wrong solution. In this paper, the stagnation problem of the traditional PSO algorithm for PD is illustrated first. Then, an explicit strategy is proposed to solve this problem, based on an in-depth understanding of the inherent optimization mechanism of the PSO algorithm. Specifically, a criterion is proposed to detect premature convergence; then a redistributing mechanism is proposed to prevent premature convergence. To improve the efficiency of this redistributing mechanism, randomized Halton sequences are further introduced to ensure the uniform distribution and randomness of the redistributed particles in the search space. Simulation results show that this strategy can effectively solve the stagnation problem of the PSO algorithm for PD, especially for large-scale and high-dimension wavefront sensing and noisy conditions. This work is further verified by an experiment. This work can improve the robustness and performance of PD wavefront sensing.

Iron control on global productivity: an efficient inverse model of the ocean's coupled phosphate and iron cycles.

Science.gov (United States)

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.

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks.

Science.gov (United States)

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.

Theory and Algorithms for Global/Local Design Optimization

National Research Council Canada - National Science Library

Watson, Layne T; Guerdal, Zafer; Haftka, Raphael T

2005-01-01

The motivating application for this research is the global/local optimal design of composite aircraft structures such as wings and fuselages, but the theory and algorithms are more widely applicable...

Theory and Algorithms for Global/Local Design Optimization

National Research Council Canada - National Science Library

Haftka, Raphael T

2004-01-01

... the component and overall design as well as on exploration of global optimization algorithms. In the former category, heuristic decomposition was followed with proof that it solves the original problem...

Optimal control of operation efficiency of belt conveyor systems

International Nuclear Information System (INIS)

Zhang, Shirong; Xia, Xiaohua

2010-01-01

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study.

Optimal control of operation efficiency of belt conveyor systems

Energy Technology Data Exchange (ETDEWEB)

Zhang, Shirong [Department of Automation, Wuhan University, Wuhan 430072 (China); Xia, Xiaohua [Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002 (South Africa)

2010-06-15

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study. (author)

Efficient Round-Trip Time Optimization for Replica-Exchange Enveloping Distribution Sampling (RE-EDS).

Science.gov (United States)

Sidler, Dominik; Cristòfol-Clough, Michael; Riniker, Sereina

2017-06-13

Replica-exchange enveloping distribution sampling (RE-EDS) allows the efficient estimation of free-energy differences between multiple end-states from a single molecular dynamics (MD) simulation. In EDS, a reference state is sampled, which can be tuned by two types of parameters, i.e., smoothness parameters(s) and energy offsets, such that all end-states are sufficiently sampled. However, the choice of these parameters is not trivial. Replica exchange (RE) or parallel tempering is a widely applied technique to enhance sampling. By combining EDS with the RE technique, the parameter choice problem could be simplified and the challenge shifted toward an optimal distribution of the replicas in the smoothness-parameter space. The choice of a certain replica distribution can alter the sampling efficiency significantly. In this work, global round-trip time optimization (GRTO) algorithms are tested for the use in RE-EDS simulations. In addition, a local round-trip time optimization (LRTO) algorithm is proposed for systems with slowly adapting environments, where a reliable estimate for the round-trip time is challenging to obtain. The optimization algorithms were applied to RE-EDS simulations of a system of nine small-molecule inhibitors of phenylethanolamine N-methyltransferase (PNMT). The energy offsets were determined using our recently proposed parallel energy-offset (PEOE) estimation scheme. While the multistate GRTO algorithm yielded the best replica distribution for the ligands in water, the multistate LRTO algorithm was found to be the method of choice for the ligands in complex with PNMT. With this, the 36 alchemical free-energy differences between the nine ligands were calculated successfully from a single RE-EDS simulation 10 ns in length. Thus, RE-EDS presents an efficient method for the estimation of relative binding free energies.

A Simple But Effective Canonical Dual Theory Unified Algorithm for Global Optimization

OpenAIRE

Zhang, Jiapu

2011-01-01

Numerical global optimization methods are often very time consuming and could not be applied for high-dimensional nonconvex/nonsmooth optimization problems. Due to the nonconvexity/nonsmoothness, directly solving the primal problems sometimes is very difficult. This paper presents a very simple but very effective canonical duality theory (CDT) unified global optimization algorithm. This algorithm has convergence is proved in this paper. More important, for this CDT-unified algorithm, numerous...

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

Global Optimization Ensemble Model for Classification Methods

Science.gov (United States)

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

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.

Conference on "State of the Art in Global Optimization : Computational Methods and Applications"

CERN Document Server

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 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.

A dynamic global and local combined particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Chen Qunxian

2009-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

A Power Efficient Exaflop Computer Design for Global Cloud System Resolving Climate Models.

Science.gov (United States)

Wehner, M. F.; Oliker, L.; Shalf, J.

2008-12-01

Exascale computers would allow routine ensemble modeling of the global climate system at the cloud system resolving scale. Power and cost requirements of traditional architecture systems are likely to delay such capability for many years. We present an alternative route to the exascale using embedded processor technology to design a system optimized for ultra high resolution climate modeling. These power efficient processors, used in consumer electronic devices such as mobile phones, portable music players, cameras, etc., can be tailored to the specific needs of scientific computing. We project that a system capable of integrating a kilometer scale climate model a thousand times faster than real time could be designed and built in a five year time scale for US$75M with a power consumption of 3MW. This is cheaper, more power efficient and sooner than any other existing technology.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

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

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

Science.gov (United States)

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

Performance indices and evaluation of algorithms in building energy efficient design optimization

International Nuclear Information System (INIS)

Si, Binghui; Tian, Zhichao; Jin, Xing; Zhou, Xin; Tang, Peng; Shi, Xing

2016-01-01

Building energy efficient design optimization is an emerging technique that is increasingly being used to design buildings with better overall performance and a particular emphasis on energy efficiency. To achieve building energy efficient design optimization, algorithms are vital to generate new designs and thus drive the design optimization process. Therefore, the performance of algorithms is crucial to achieving effective energy efficient design techniques. This study evaluates algorithms used for building energy efficient design optimization. A set of performance indices, namely, stability, robustness, validity, speed, coverage, and locality, is proposed to evaluate the overall performance of algorithms. A benchmark building and a design optimization problem are also developed. Hooke–Jeeves algorithm, Multi-Objective Genetic Algorithm II, and Multi-Objective Particle Swarm Optimization algorithm are evaluated by using the proposed performance indices and benchmark design problem. Results indicate that no algorithm performs best in all six areas. Therefore, when facing an energy efficient design problem, the algorithm must be carefully selected based on the nature of the problem and the performance indices that matter the most. - Highlights: • Six indices of algorithm performance in building energy optimization are developed. • For each index, its concept is defined and the calculation formulas are proposed. • A benchmark building and benchmark energy efficient design problem are proposed. • The performance of three selected algorithms are evaluated.

High-efficiency design optimization of a centrifugal pump

Energy Technology Data Exchange (ETDEWEB)

Heo, Man Woong; Ma, Sang Bum; Shim, Hyeon Seok; Kim, Kwang Yong [Dept. of Mechanical Engineering, Inha University, Incheon (Korea, Republic of)

2016-09-15

Design optimization of a backward-curved blades centrifugal pump with specific speed of 150 has been performed to improve hydraulic performance of the pump using surrogate modeling and three-dimensional steady Reynolds-averaged Navier-Stokes analysis. The shear stress transport model was used for the analysis of turbulence. Four geometric variables defining the blade hub inlet angle, hub contours, blade outlet angle, and blade angle profile of impeller were selected as design variables, and total efficiency of the pump at design flow rate was set as the objective function for the optimization. Thirty-six design points were chosen using the Latin hypercube sampling, and three different surrogate models were constructed using the objective function values calculated at these design points. The optimal point was searched from the constructed surrogate model by using sequential quadratic programming. The optimum designs of the centrifugal pump predicted by the surrogate models show considerable increases in efficiency compared to a reference design. Performance of the best optimum design was validated compared to experimental data for total efficiency and head.

Shape optimization for aerodynamic efficiency and low observability

Science.gov (United States)

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.

A new global particle swarm optimization for the economic emission dispatch with or without transmission losses

International Nuclear Information System (INIS)

Zou, Dexuan; Li, Steven; Li, Zongyan; Kong, Xiangyong

2017-01-01

Highlights: • A new global particle swarm optimization (NGPSO) is proposed. • NGPSO has strong convergence and desirable accuracy. • NGPSO is used to handle the economic emission dispatch with or without transmission losses. • The equality constraint can be satisfied by solving a quadratic equation. • The inequality constraints can be satisfied by using penalty function method. - Abstract: A new global particle swarm optimization (NGPSO) algorithm is proposed to solve the economic emission dispatch (EED) problems in this paper. NGPSO is different from the traditional particle swarm optimization (PSO) algorithm in two aspects. First, NGPSO uses a new position updating equation which relies on the global best particle to guide the searching activities of all particles. Second, it uses the randomization based on the uniform distribution to slightly disturb the flight trajectories of particles during the late evolutionary process. The two steps enable NGPSO to effectively execute a number of global searches, and thus they increase the chance of exploring promising solution space, and reduce the probabilities of getting trapped into local optima for all particles. On the other hand, the two objective functions of EED are normalized separately according to all candidate solutions, and then they are incorporated into one single objective function. The transformation steps are very helpful in eliminating the difference caused by the different dimensions of the two functions, and thus they strike a balance between the fuel cost and emission. In addition, a simple and common penalty function method is employed to facilitate the satisfactions of EED’s constraints. Based on these improvements in PSO, objective functions and constraints handling, high-quality solutions can be obtained for EED problems. Five examples are chosen to testify the performance of three improved PSOs on solving EED problems with or without transmission losses. Experimental results show that

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.

Optimal Energy-Efficient Sensing and Power Allocation in Cognitive Radio Networks

Directory of Open Access Journals (Sweden)

Xia Wu

2014-01-01

Full Text Available We consider a joint optimization of sensing parameter and power allocation for an energy-efficient cognitive radio network (CRN in which the primary user (PU is protected. The optimization problem to maximize the energy efficiency of CRN is formulated as a function of two variables, which are sensing time and transmit power, subject to the average interference power to the PU and the target detection probability. During the optimizing process, the quality of service parameter (the minimum rate acceptable to secondary users (SUs has also been taken into consideration. The optimal solutions are analyzed and an algorithm combined with fractional programming that maximizes the energy efficiency for CRN is presented. Numerical results show that the performance improvement is achieved by the joint optimization of sensing time and power allocation.

Lean and Efficient Software: Whole Program Optimization of Executables

Science.gov (United States)

2016-12-31

19b. TELEPHONE NUMBER (Include area code) 12/31/2016 Final Technical Report (Phase I - Base Period) 30-06-2014 - 31-12-2016 Lean and Efficient...Software: Whole-Program Optimization of Executables Final Report Evan Driscoll Tom Johnson GrammaTech, Inc. 531 Esty Street Ithaca, NY 14850 Office of...hardening U U U UU 30 Tom Johnson (607) 273-7340 x.134 Page 1 of 30 “ Lean and Efficient Software: Whole-Program Optimization of Executables

Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

Science.gov (United States)

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.

Global optimization for overall HVAC systems - Part I problem formulation and analysis

International Nuclear Information System (INIS)

Lu Lu; Cai Wenjian; Chai, Y.S.; Xie Lihua

2005-01-01

This paper presents the global optimization technologies for overall heating, ventilating and air conditioning (HVAC) systems. The objective function of global optimization and constraints are formulated based on mathematical models of the major components. All these models are associated with power consumption components and heat exchangers for transferring cooling load. The characteristics of all the major components are briefly introduced by models, and the interactions between them are analyzed and discussed to show the complications of the problem. According to the characteristics of the operating components, the complicated original optimization problem for overall HVAC systems is transformed and simplified into a compact form ready for optimization

Neoliberal Optimism: Applying Market Techniques to Global Health.

Science.gov (United States)

Mei, Yuyang

2017-01-01

Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.

Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals.

Science.gov (United States)

Bergeest, Jan-Philip; Rohr, Karl

2012-10-01

In high-throughput applications, accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression and the understanding of cell function. We propose an approach for segmenting cell nuclei which is based on active contours using level sets and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We consider three different well-known energy functionals for active contour-based segmentation and introduce convex formulations of these functionals. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images from different experiments comprising different cell types. We have also performed a quantitative comparison with previous segmentation approaches. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficiency optimization potential in supercritical Organic Rankine Cycles

Energy Technology Data Exchange (ETDEWEB)

Schuster, A.; Aumann, R. [Technische Universitaet Muenchen Institute of Energy Systems Boltzmannstr. 15, 85748 Garching (Germany); Karellas, S. [National Technical University of Athens Laboratory of Steam Boilers and Thermal Plants Heroon Polytechniou 9, 15780 Athens (Greece)

2010-02-15

Nowadays, the use of Organic Rankine Cycle (ORC) in decentralised applications is linked with the fact that this process allows the use of low temperature heat sources and offers an advantageous efficiency in small-scale concepts. Many state-of-the-art and innovative applications can successfully use the ORC process. In this process, according to the heat source level, special attention must be drawn to the choice of the appropriate working fluid, which is a factor that affects the thermal and exergetic efficiency of the cycle. The investigation of supercritical parameters of various working fluids in ORC applications seems to bring promising results concerning the efficiency of the application. This paper presents the results from a simulation of the ORC and the optimization potential of the process when using supercritical parameters. In order to optimize the process, various working fluids are considered and compared concerning their thermal efficiency and the usable percentage of heat. The reduction of exergy losses is discussed based on the need of surplus heat exchanger surface. (author)

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.

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.

Energy-Efficient Optimization for HARQ Schemes over Time-Correlated Fading Channels

KAUST Repository

Shi, Zheng

2018-03-19

Energy efficiency of three common hybrid automatic repeat request (HARQ) schemes including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), is analyzed and joint power allocation and rate selection to maximize the energy efficiency is investigated in this paper. Unlike prior literature, time-correlated fading channels is considered and two widely concerned quality of service (QoS) constraints, i.e., outage and goodput constraints, are also considered in the optimization, which further differentiates this work from prior ones. Using a unified expression of asymptotic outage probabilities, optimal transmission powers and optimal rate are derived in closed-forms to maximize the energy efficiency while satisfying the QoS constraints. These closed-form solutions then enable a thorough analysis of the maximal energy efficiencies of various HARQ schemes. It is revealed that with low outage constraint, the maximal energy efficiency achieved by Type I HARQ is $\\\\frac{1}{4\\\\ln2}$ bits/J, while HARQ-CC and HARQ-IR can achieve the same maximal energy efficiency as $\\\\frac{\\\\kappa_\\\\infty}{4\\\\ln2}$ bits/J where $\\\\kappa_\\\\infty = 1.6617$. Moreover, time correlation in the fading channels has a negative impact on the energy efficiency, while large maximal allowable number of transmissions is favorable for the improvement of energy efficiency. The effectiveness of the energy-efficient optimization is verified by extensive simulations and the results also show that HARQ-CC can achieve the best tradeoff between energy efficiency and spectral efficiency among the three HARQ schemes.

A heterogeneous computing accelerated SCE-UA global optimization method using OpenMP, OpenCL, CUDA, and OpenACC.

Science.gov (United States)

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Liang, Ke; Hong, Yang

2017-10-01

The shuffled complex evolution optimization developed at the University of Arizona (SCE-UA) has been successfully applied in various kinds of scientific and engineering optimization applications, such as hydrological model parameter calibration, for many years. The algorithm possesses good global optimality, convergence stability and robustness. However, benchmark and real-world applications reveal the poor computational efficiency of the SCE-UA. This research aims at the parallelization and acceleration of the SCE-UA method based on powerful heterogeneous computing technology. The parallel SCE-UA is implemented on Intel Xeon multi-core CPU (by using OpenMP and OpenCL) and NVIDIA Tesla many-core GPU (by using OpenCL, CUDA, and OpenACC). The serial and parallel SCE-UA were tested based on the Griewank benchmark function. Comparison results indicate the parallel SCE-UA significantly improves computational efficiency compared to the original serial version. The OpenCL implementation obtains the best overall acceleration results however, with the most complex source code. The parallel SCE-UA has bright prospects to be applied in real-world applications.

Global blending optimization of laminated composites with discrete material candidate selection and thickness variation

DEFF Research Database (Denmark)

Sørensen, Søren N.; Stolpe, Mathias

2015-01-01

rate. The capabilities of the method and the effect of active versus inactive manufacturing constraints are demonstrated on several numerical examples of limited size, involving at most 320 binary variables. Most examples are solved to guaranteed global optimality and may constitute benchmark examples...... but is, however, convex in the original mixed binary nested form. Convexity is the foremost important property of optimization problems, and the proposed method can guarantee the global or near-global optimal solution; unlike most topology optimization methods. The material selection is limited...... for popular topology optimization methods and heuristics based on solving sequences of non-convex problems. The results will among others demonstrate that the difficulty of the posed problem is highly dependent upon the composition of the constitutive properties of the material candidates....

Biological optimization systems for enhancing photosynthetic efficiency and methods of use

Science.gov (United States)

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.

Comparative evaluation of various optimization methods and the development of an optimization code system SCOOP

International Nuclear Information System (INIS)

Suzuki, Tadakazu

1979-11-01

Thirty two programs for linear and nonlinear optimization problems with or without constraints have been developed or incorporated, and their stability, convergence and efficiency have been examined. On the basis of these evaluations, the first version of the optimization code system SCOOP-I has been completed. The SCOOP-I is designed to be an efficient, reliable, useful and also flexible system for general applications. The system enables one to find global optimization point for a wide class of problems by selecting the most appropriate optimization method built in it. (author)

Use Conditions and Efficiency Measurements of DC Power Optimizers for Photovoltaic Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Deline, C.; MacAlpine, S.

2013-10-01

No consensus standard exists for estimating annual conversion efficiency of DC-DC converters or power optimizers in photovoltaic (PV) applications. The performance benefits of PV power electronics including per-panel DC-DC converters depend in large part on the operating conditions of the PV system, along with the performance characteristics of the power optimizer itself. This work presents acase study of three system configurations that take advantage of the capabilities of DC power optimizers. Measured conversion efficiencies of DC-DC converters are applied to these scenarios to determine the annual weighted operating efficiency. A simplified general method of reporting weighted efficiency is given, based on the California Energy Commission's CEC efficiency rating and severalinput / output voltage ratios. Efficiency measurements of commercial power optimizer products are presented using the new performance metric, along with a description of the limitations of the approach.

Global Optimization of Minority Game by Smart Agents

OpenAIRE

Yan-Bo Xie; Bing-Hong Wang; Chin-Kun Hu; Tao Zhou

2004-01-01

We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests that the economic systems may have the ability to self-organize into a highly optimized state by agents who are forced to make decisions based on inductive thinking for their lim...

Efficient optimization of electrostatic interactions between biomolecules.

Energy Technology Data Exchange (ETDEWEB)

Bardhan, J. P.; Altman, M. D.; White, J. K.; Tidor, B.; Mathematics and Computer Science; MIT

2007-01-01

We present a PDE-constrained approach to optimizing the electrostatic interactions between two biomolecules. These interactions play important roles in the determination of binding affinity and specificity, and are therefore of significant interest when designing a ligand molecule to bind tightly to a receptor. Using a popular continuum model and physically reasonable assumptions, the electrostatic component of the binding free energy is a convex, quadratic function of the ligand charge distribution. Traditional optimization methods require exhaustive pre-computation, and the expense has precluded a full exploration of the promise of electrostatic optimization in biomolecule analysis and design. In this paper we describe an approach in which the electrostatic simulations and optimization problem are solved simultaneously; unlike many PDE- constrained optimization frameworks, the proposed method does not incorporate the PDE as a set of equality constraints. This co-optimization approach can be used by itself to solve unconstrained problems or those with linear equality constraints, or in conjunction with primal-dual interior point methods to solve problems with inequality constraints. Model problems demonstrate that the co-optimization method is computationally efficient and can be used to solve realistic problems.

Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

International Nuclear Information System (INIS)

Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

2016-01-01

Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

Efficient reanalysis techniques for robust topology optimization

DEFF Research Database (Denmark)

Amir, Oded; Sigmund, Ole; Lazarov, Boyan Stefanov

2012-01-01

efficient robust topology optimization procedures based on reanalysis techniques. The approach is demonstrated on two compliant mechanism design problems where robust design is achieved by employing either a worst case formulation or a stochastic formulation. It is shown that the time spent on finite...

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.

Statistical distributions of optimal global alignment scores of random protein sequences

Directory of Open Access Journals (Sweden)

Tang Jiaowei

2005-10-01

Full Text Available Abstract Background The inference of homology from statistically significant sequence similarity is a central issue in sequence alignments. So far the statistical distribution function underlying the optimal global alignments has not been completely determined. Results In this study, random and real but unrelated sequences prepared in six different ways were selected as reference datasets to obtain their respective statistical distributions of global alignment scores. All alignments were carried out with the Needleman-Wunsch algorithm and optimal scores were fitted to the Gumbel, normal and gamma distributions respectively. The three-parameter gamma distribution performs the best as the theoretical distribution function of global alignment scores, as it agrees perfectly well with the distribution of alignment scores. The normal distribution also agrees well with the score distribution frequencies when the shape parameter of the gamma distribution is sufficiently large, for this is the scenario when the normal distribution can be viewed as an approximation of the gamma distribution. Conclusion We have shown that the optimal global alignment scores of random protein sequences fit the three-parameter gamma distribution function. This would be useful for the inference of homology between sequences whose relationship is unknown, through the evaluation of gamma distribution significance between sequences.

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 CO2 efficiency: Country-wise estimates using a stochastic cost frontier

International Nuclear Information System (INIS)

Herrala, Risto; Goel, Rajeev K.

2012-01-01

This paper examines global carbon dioxide (CO 2 ) efficiency by employing a stochastic cost frontier analysis of about 170 countries in 1997 and 2007. The main contribution lies in providing a new approach to environmental efficiency estimation, in which the efficiency estimates quantify the distance from the policy objective of minimum emissions. We are able to examine a very large pool of nations and provide country-wise efficiency estimates. We estimate three econometric models, corresponding with alternative interpretations of the Cancun vision (Conference of the Parties 2011). The models reveal progress in global environmental efficiency during a preceding decade. The estimates indicate vast differences in efficiency levels, and efficiency changes across countries. The highest efficiency levels are observed in Africa and Europe, while the lowest are clustered around China. The largest efficiency gains were observed in central and eastern Europe. CO 2 efficiency also improved in the US and China, the two largest emitters, but their ranking in terms of CO 2 efficiency deteriorated. Policy implications are discussed. - Highlights: ► We estimate global environmental efficiency in line with the Cancun vision, using a stochastic cost frontier. ► The study covers 170 countries during a 10 year period, ending in 2007. ► The biggest improvements occurred in Europe, and efficiency falls in South America. ► The efficiency ranking of US and China, the largest emitters, deteriorated. ► In 2007, highest efficiency was observed in Africa and Europe, and the lowest around China.

Application of surrogate-based global optimization to aerodynamic design

CERN Document Server

Pérez, Esther

2016-01-01

Aerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive – this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations. The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques. In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogat...

Energy-Efficient Optimization for HARQ Schemes over Time-Correlated Fading Channels

KAUST Repository

Shi, Zheng; Ma, Shaodan; Yang, Guanghua; Alouini, Mohamed-Slim

2018-01-01

in the optimization, which further differentiates this work from prior ones. Using a unified expression of asymptotic outage probabilities, optimal transmission powers and optimal rate are derived in closed-forms to maximize the energy efficiency while satisfying

Improving the efficiency of aerodynamic shape optimization

Science.gov (United States)

Burgreen, Greg W.; Baysal, Oktay; Eleshaky, Mohamed E.

1994-01-01

The computational efficiency of an aerodynamic shape optimization procedure that is based on discrete sensitivity analysis is increased through the implementation of two improvements. The first improvement involves replacing a grid-point-based approach for surface representation with a Bezier-Bernstein polynomial parameterization of the surface. Explicit analytical expressions for the grid sensitivity terms are developed for both approaches. The second improvement proposes the use of Newton's method in lieu of an alternating direction implicit methodology to calculate the highly converged flow solutions that are required to compute the sensitivity coefficients. The modified design procedure is demonstrated by optimizing the shape of an internal-external nozzle configuration. Practically identical optimization results are obtained that are independent of the method used to represent the surface. A substantial factor of 8 decrease in computational time for the optimization process is achieved by implementing both of the design procedure improvements.

Global Sufficient Optimality Conditions for a Special Cubic Minimization Problem

Directory of Open Access Journals (Sweden)

Xiaomei Zhang

2012-01-01

Full Text Available We present some sufficient global optimality conditions for a special cubic minimization problem with box constraints or binary constraints by extending the global subdifferential approach proposed by V. Jeyakumar et al. (2006. The present conditions generalize the results developed in the work of V. Jeyakumar et al. where a quadratic minimization problem with box constraints or binary constraints was considered. In addition, a special diagonal matrix is constructed, which is used to provide a convenient method for justifying the proposed sufficient conditions. Then, the reformulation of the sufficient conditions follows. It is worth noting that this reformulation is also applicable to the quadratic minimization problem with box or binary constraints considered in the works of V. Jeyakumar et al. (2006 and Y. Wang et al. (2010. Finally some examples demonstrate that our optimality conditions can effectively be used for identifying global minimizers of the certain nonconvex cubic minimization problem.

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

Science.gov (United States)

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 multilevel optimization method for engineering design

Science.gov (United States)

Vanderplaats, G. N.; Yang, Y. J.; Kim, D. S.

1988-01-01

An efficient multilevel deisgn optimization technique is presented. The proposed method is based on the concept of providing linearized information between the system level and subsystem level optimization tasks. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to use. The disadvantage is that the coupling between subsystems is not dealt with in a precise mathematical manner.

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.

Efficiency Optimization Methods in Low-Power High-Frequency Digitally Controlled SMPS

Directory of Open Access Journals (Sweden)

Aleksandar Prodić

2010-06-01

Full Text Available This paper gives a review of several power efficiency optimization techniques that are utilizing advantages of emerging digital control in high frequency switch-mode power supplies (SMPS, processing power from a fraction of watt to several hundreds of watts. Loss mechanisms in semiconductor components are briefly reviewed and the related principles of online efficiency optimization through power stage segmentation and gate voltage variation presented. Practical implementations of such methods utilizing load prediction or data extraction from a digital control loop are shown. The benefits of the presented efficiency methods are verified through experimental results, showing efficiency improvements, ranging from 2% to 30%,depending on the load conditions.

Bayesian optimization for materials science

CERN Document Server

Packwood, Daniel

2017-01-01

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...

Efficient search by optimized intermittent random walks

International Nuclear Information System (INIS)

Oshanin, Gleb; Lindenberg, Katja; Wio, Horacio S; Burlatsky, Sergei

2009-01-01

We study the kinetics for the search of an immobile target by randomly moving searchers that detect it only upon encounter. The searchers perform intermittent random walks on a one-dimensional lattice. Each searcher can step on a nearest neighbor site with probability α or go off lattice with probability 1 - α to move in a random direction until it lands back on the lattice at a fixed distance L away from the departure point. Considering α and L as optimization parameters, we seek to enhance the chances of successful detection by minimizing the probability P N that the target remains undetected up to the maximal search time N. We show that even in this simple model, a number of very efficient search strategies can lead to a decrease of P N by orders of magnitude upon appropriate choices of α and L. We demonstrate that, in general, such optimal intermittent strategies are much more efficient than Brownian searches and are as efficient as search algorithms based on random walks with heavy-tailed Cauchy jump-length distributions. In addition, such intermittent strategies appear to be more advantageous than Levy-based ones in that they lead to more thorough exploration of visited regions in space and thus lend themselves to parallelization of the search processes.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Optimization of thermal efficiency of nuclear central power like as PWR

International Nuclear Information System (INIS)

Lapa, Nelbia da Silva

2005-10-01

The main purpose of this work is the definition of operational conditions for the steam and power conservation of Pressurized Water Reactor (PWR) plant in order to increase its system thermal efficiency without changing any component, based on the optimization of operational parameters of the plant. The thermal efficiency is calculated by a thermal balance program, based on conservation equations for homogeneous modeling. The circuit coefficients are estimated by an optimization tool, allowing a more realistic thermal balance for the plans under analysis, as well as others parameters necessary to some component models. With the operational parameter optimization, it is possible to get a level of thermal efficiency that increase capital gain, due to a better relationship between the electricity production and the amount of fuel used, without any need to change components plant. (author)

Performance optimization of low-temperature power generation by supercritical ORCs (organic Rankine cycles) using low GWP (global warming potential) working fluids

International Nuclear Information System (INIS)

Le, Van Long; Feidt, Michel; Kheiri, Abdelhamid; Pelloux-Prayer, Sandrine

2014-01-01

This paper presents the system efficiency optimization scenarios of basic and regenerative supercritical ORCs (organic Rankine cycles) using low-GWP (global warming potential) organic compounds as working fluid. A more common refrigerant, i.e. R134a, was also employed to make the comparison. A 150-°C, 5-bar-pressurized hot water is used to simulate the heat source medium. Power optimization was equally performed for the basic configuration of supercritical ORC. Thermodynamic performance comparison of supercritical ORCs using different working fluids was achieved by ranking method and exergy analysis method. The highest optimal efficiency of the system (η sys ) is always obtained with R152a in both basic (11.6%) and regenerative (13.1%) configurations. The highest value of optimum electrical power output (4.1 kW) is found with R1234ze. By using ranking method and considering low-GWP criterion, the best working fluids for system efficiency optimization of basic and regenerative cycles are R32 and R152a, respectively. The best working fluid for net electrical power optimization of basic cycle is R1234ze. Although CO 2 has many desirable environmental and safety properties (e.g. zero ODP (Ozone Depletion Potential), ultra low-GWP, non toxicity, non flammability, etc.), the worst thermodynamic performance is always found with the cycle using this compound as working fluid. - Highlights: • Performance optimizations were carried out for the supercritical ORCs using low-GWP working fluids. • Heat regeneration was used to improve the system efficiency of the supercritical ORC. • Thermodynamic performances of supercritical ORCs at the optima were evaluated by ranking method and exergy analysis

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.

Robust active combustion control for the optimization of environmental performance and energy efficiency

Science.gov (United States)

Demayo, Trevor Nat

optimization. The active control system was demonstrated and evaluated by optimizing the burners under practical conditions. In most cases, the controller was able to locate, within 10--15 min, a global performance peak that simultaneously minimized emissions and maximized system efficiency within specified stability limits. The active controller demonstrated flexibility and robustness by (a) successfully optimizing different burners for different J functions, initial conditions, and sensor combinations, and (b) successfully reoptimizing a burner under the effect of simulated window fouling and following sudden inlet perturbations, including load cycling and a misaligned fuel injector.

Global Crisis as Enterprise Software Motivator: from Lifecycle Optimization to Efficient Implementation Series

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.

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

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.

Study on Design Optimization of Centrifugal Compressors Considering Efficiency and Weight

International Nuclear Information System (INIS)

Lee, Younghwan; Kang, Shinhyoung; Ha, Kyunggu

2015-01-01

Various centrifugal compressors are currently used extensively in industrial fields, where the design requirements are more complicated. This makes it more difficult to determine the optimal design point of a centrifugal compressor. Traditionally, the efficiency is an important factor for optimization. In this study, the weight of the compressor was also considered. The aim of this study was to present the design tendency considering the stage efficiency and weight. In addition, this study suggested possibility of a selection of compressor design objectives at an early design stage based on the optimization results. Only a vaneless diffuser was considered in this case. The Kriging method was used with sample points from 1D design program data. The optimal points were determined in a substitute design space.

Study on Design Optimization of Centrifugal Compressors Considering Efficiency and Weight

Energy Technology Data Exchange (ETDEWEB)

Lee, Younghwan; Kang, Shinhyoung [Seoul National University, Seoul (Korea, Republic of); Ha, Kyunggu [Hyundai Motor Group, Ulsan (Korea, Republic of)

2015-04-15

Various centrifugal compressors are currently used extensively in industrial fields, where the design requirements are more complicated. This makes it more difficult to determine the optimal design point of a centrifugal compressor. Traditionally, the efficiency is an important factor for optimization. In this study, the weight of the compressor was also considered. The aim of this study was to present the design tendency considering the stage efficiency and weight. In addition, this study suggested possibility of a selection of compressor design objectives at an early design stage based on the optimization results. Only a vaneless diffuser was considered in this case. The Kriging method was used with sample points from 1D design program data. The optimal points were determined in a substitute design space.

Pure global externalities. International efficiency and equity

International Nuclear Information System (INIS)

Musgrave, P.B.

1995-01-01

The argument for a carbon tax to reduce CO 2 emissions to the global arena is extended and the distribution as well as efficiency issues posed by a global agreement on carbon taxation are examined. The author distinguishes four alternative international arrangements in addition to the 'no cooperation' case. The arrangements allow for various equity rules to reflect what are considered to be equitable changes from the baseline position. Although the results of Musgrave's exercise lay no strong claims to validity as empirical findings, the relative patterns that emerge, nonetheless, contribute to an understanding of the issues that must be faced in arriving at acceptable international forms of cooperation. 1 fig., 6 tabs., 10 refs

Proposal of Evolutionary Simplex Method for Global Optimization Problem

Science.gov (United States)

Shimizu, Yoshiaki

To make an agile decision in a rational manner, role of optimization engineering has been notified increasingly under diversified customer demand. With this point of view, in this paper, we have proposed a new evolutionary method serving as an optimization technique in the paradigm of optimization engineering. The developed method has prospects to solve globally various complicated problem appearing in real world applications. It is evolved from the conventional method known as Nelder and Mead’s Simplex method by virtue of idea borrowed from recent meta-heuristic method such as PSO. Mentioning an algorithm to handle linear inequality constraints effectively, we have validated effectiveness of the proposed method through comparison with other methods using several benchmark problems.

The Energy Efficient Enterprise

Energy Technology Data Exchange (ETDEWEB)

Ahmad, Bashir

2010-09-15

Since rising energy costs have become a crucial factor for the economy of production processes, the optimization of energy efficiency is of essential importance for industrial enterprises. Enterprises establish energy saving programs, specific to their needs. The most important elements of these energy efficiency programs are energy savings, energy controlling, energy optimization, and energy management. This article highlights the industrial enterprise approach to establish sustainable energy management programs based on the above elements. Globally, if organizations follow this approach, they can significantly reduce the overall energy consumption and cost.

On-line efficiency optimization of a synchronous reluctance motor

Energy Technology Data Exchange (ETDEWEB)

Lubin, Thierry; Razik, Hubert; Rezzoug, Abderrezak [Groupe de Recherche en Electrotechnique et Electronique de Nancy, GREEN, CNRS-UMR 7037, Universite Henri Poincare, BP 239, 54506 Vandoeuvre-les-Nancy Cedex (France)

2007-04-15

This paper deals with an on-line optimum-efficiency control of a synchronous reluctance motor drive. The input power minimization control is implemented with a search controller using Fibonacci search algorithm. It searches the optimal reference value of the d-axis stator current for which the input power is minimum. The input power is calculated from the measured dc-bus current and dc-bus voltage of the inverter. A rotor-oriented vector control of the synchronous reluctance machine with the optimization efficiency controller is achieved with a DSP board (TMS302C31). Experimental results are presented to validate the proposed control methods. It is shown that stability problems can appear during the search process. (author)

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.

A non-linear branch and cut method for solving discrete minimum compliance problems to global optimality

DEFF Research Database (Denmark)

Stolpe, Mathias; Bendsøe, Martin P.

2007-01-01

This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities...

Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review)

International Nuclear Information System (INIS)

Sewsynker-Sukai, Yeshona; Faloye, Funmilayo; Kana, Evariste Bosco Gueguim

2016-01-01

In view of the looming energy crisis as a result of depleting fossil fuel resources and environmental concerns from greenhouse gas emissions, the need for sustainable energy sources has secured global attention. Research is currently focused towards renewable sources of energy due to their availability and environmental friendliness. Biofuel production like other bioprocesses is controlled by several process parameters including pH, temperature and substrate concentration; however, the improvement of biofuel production requires a robust process model that accurately relates the effect of input variables to the process output. Artificial neural networks (ANNs) have emerged as a tool for modelling complex, non-linear processes. ANNs are applied in the prediction of various processes; they are useful for virtual experimentations and can potentially enhance bioprocess research and development. In this study, recent findings on the application of ANN for the modelling and optimization of biohydrogen, biogas, biodiesel, microbial fuel cell technology and bioethanol are reviewed. In addition, comparative studies on the modelling efficiency of ANN and other techniques such as the response surface methodology are briefly discussed. The review highlights the efficiency of ANNs as a modelling and optimization tool in biofuel process development

Recent Progress on Data-Based Optimization for Mineral Processing Plants

Directory of Open Access Journals (Sweden)

Jinliang Ding

2017-04-01

Full Text Available In the globalized market environment, increasingly significant economic and environmental factors within complex industrial plants impose importance on the optimization of global production indices; such optimization includes improvements in production efficiency, product quality, and yield, along with reductions of energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelligence optimization methods and technologies in improving the performance of global production indices in mineral processing. First, we provide the problem description. Next, we summarize recent progress in data-based optimization for mineral processing plants. This optimization consists of four layers: optimization of the target values for monthly global production indices, optimization of the target values for daily global production indices, optimization of the target values for operational indices, and automation systems for unit processes. We briefly overview recent progress in each of the different layers. Finally, we point out opportunities for future works in data-based optimization for mineral processing plants.

Mathematical efficiency calibration with uncertain source geometries using smart optimization

International Nuclear Information System (INIS)

Menaa, N.; Bosko, A.; Bronson, F.; Venkataraman, R.; Russ, W. R.; Mueller, W.; Nizhnik, V.; Mirolo, L.

2011-01-01

The In Situ Object Counting Software (ISOCS), a mathematical method developed by CANBERRA, is a well established technique for computing High Purity Germanium (HPGe) detector efficiencies for a wide variety of source shapes and sizes. In the ISOCS method, the user needs to input the geometry related parameters such as: the source dimensions, matrix composition and density, along with the source-to-detector distance. In many applications, the source dimensions, the matrix material and density may not be well known. Under such circumstances, the efficiencies may not be very accurate since the modeled source geometry may not be very representative of the measured geometry. CANBERRA developed an efficiency optimization software known as 'Advanced ISOCS' that varies the not well known parameters within user specified intervals and determines the optimal efficiency shape and magnitude based on available benchmarks in the measured spectra. The benchmarks could be results from isotopic codes such as MGAU, MGA, IGA, or FRAM, activities from multi-line nuclides, and multiple counts of the same item taken in different geometries (from the side, bottom, top etc). The efficiency optimization is carried out using either a random search based on standard probability distributions, or using numerical techniques that carry out a more directed (referred to as 'smart' in this paper) search. Measurements were carried out using representative source geometries and radionuclide distributions. The radionuclide activities were determined using the optimum efficiency and compared against the true activities. The 'Advanced ISOCS' method has many applications among which are: Safeguards, Decommissioning and Decontamination, Non-Destructive Assay systems and Nuclear reactor outages maintenance. (authors)

A dynamic optimization on economic energy efficiency in development: A numerical case of China

International Nuclear Information System (INIS)

Wang, Dong

2014-01-01

This paper is based on dynamic optimization methodology to investigate the economic energy efficiency issues in developing countries. The paper introduces some definitions about energy efficiency both in economics and physics, and establishes a quantitative way for measuring the economic energy efficiency. The linkage between economic energy efficiency, energy consumption and other macroeconomic variables is demonstrated primarily. Using the methodology of dynamic optimization, a maximum problem of economic energy efficiency over time, which is subjected to the extended Solow growth model and instantaneous investment rate, is modelled. In this model, the energy consumption is set as a control variable and the capital is regarded as a state variable. The analytic solutions can be derived and the diagrammatic analysis provides saddle-point equilibrium. A numerical simulation based on China is also presented; meanwhile, the optimal paths of investment and energy consumption can be drawn. The dynamic optimization encourages governments in developing countries to pursue higher economic energy efficiency by controlling the energy consumption and regulating the investment state as it can conserve energy without influencing the achievement of steady state in terms of Solow model. If that, a sustainable development will be achieved. - Highlights: • A new definition on economic energy efficiency is proposed mathematically. • A dynamic optimization modelling links economic energy efficiency with other macroeconomic variables in long run. • Economic energy efficiency is determined by capital stock level and energy consumption. • Energy saving is a key solution for improving economic energy efficiency

Data Center Energy Efficiency Standards in India: Preliminary Findings from Global Practices

Energy Technology Data Exchange (ETDEWEB)

Raje, Sanyukta; Maan, Hermant; Ganguly, Suprotim; Singh, Tanvin; Jayaram, Nisha; Ghatikar, Girish; Greenberg, Steve; Kumar, Satish; Sartor, Dale

2015-06-01

Global data center energy consumption is growing rapidly. In India, information technology industry growth, fossil-fuel generation, and rising energy prices add significant operational costs and carbon emissions from energy-intensive data centers. Adoption of energy-efficient practices can improve the global competitiveness and sustainability of data centers in India. Previous studies have concluded that advancement of energy efficiency standards through policy and regulatory mechanisms is the fastest path to accelerate the adoption of energy-efficient practices in the Indian data centers. In this study, we reviewed data center energy efficiency practices in the United States, Europe, and Asia. Using evaluation metrics, we identified an initial set of energy efficiency standards applicable to the Indian context using the existing policy mechanisms. These preliminary findings support next steps to recommend energy efficiency standards and inform policy makers on strategies to adopt energy-efficient technologies and practices in Indian data centers.

Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Wei Li

2015-01-01

Full Text Available We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Optimal correction and design parameter search by modern methods of rigorous global optimization

International Nuclear Information System (INIS)

Makino, K.; Berz, M.

2011-01-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle

Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems.

Science.gov (United States)

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.

A global optimization algorithm inspired in the behavior of selfish herds.

Science.gov (United States)

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.

Measuring capital market efficiency: Global and local correlations structure

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav; Vošvrda, Miloslav

2013-01-01

Roč. 392, č. 1 (2013), s. 184-193 ISSN 0378-4371 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Capital market efficiency * Fractal dimension * Long-range dependence * Short-range dependence Subject RIV: AH - Economics Impact factor: 1.722, year: 2013 http://library.utia.cas.cz/separaty/2012/E/kristoufek-measuring capital market efficiency global and local correlations structure.pdf

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

A non-linear branch and cut method for solving discrete minimum compliance problems to global optimality

DEFF Research Database (Denmark)

Stolpe, Mathias; Bendsøe, Martin P.

2007-01-01

This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities......) and cuts....

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.

Theoretical properties of the global optimizer of two layer neural network

OpenAIRE

Boob, Digvijay; Lan, Guanghui

2017-01-01

In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves "almost" all functions which are not piecewise linear), we have that first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. ...

Solving global optimization problems on GPU cluster

Energy Technology Data Exchange (ETDEWEB)

Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya [Lobachevsky State University of Nizhni Novgorod, Gagarin Avenue 23, 603950 Nizhni Novgorod (Russian Federation)

2016-06-08

The paper contains the results of investigation of a parallel global optimization algorithm combined with a dimension reduction scheme. This allows solving multidimensional problems by means of reducing to data-independent subproblems with smaller dimension solved in parallel. The new element implemented in the research consists in using several graphic accelerators at different computing nodes. The paper also includes results of solving problems of well-known multiextremal test class GKLS on Lobachevsky supercomputer using tens of thousands of GPU cores.

Comparing effectiveness and efficiency in technical specifications and maintenance optimization

International Nuclear Information System (INIS)

Martorell, Sebastian; Sanchez, Ana; Carlos, Sofia; Serradell, Vicente

2002-01-01

Optimization of technical specification requirements and maintenance (TS and M) has been found interesting from the very beginning at Nuclear Power Plants (NPPs). However, the resolution of such a kind of optimization problem has been limited often to focus only on individual TS and M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the interest to focus on the coordinated optimization of all of the relevant TS and M-related parameters based on multiple criteria. The purpose of this paper is on signifying benefits and improvement areas in performing the coordinated optimization of TS and M through reviewing the effectiveness and efficiency of common strategies for optimizing TS and M at system level. A case of application is provided for a stand-by safety-related system to demonstrate the basic procedure and to extract a number of conclusions and recommendations from the results achieved. Thus, it is concluded that the optimized values depend on the particular TS and M-related parameters being involved and the solutions with the largest benefit (minimum risk or minimum cost) are achieved when considering the simultaneous optimization of all of them, although increased computational resources are also required. Consequently, it is necessary to analyze not only the value reached but also the performance of the optimization procedure through effectiveness and efficiency measures which lead to recommendations on potential improvement areas

Efficient optimal joint channel estimation and data detection for massive MIMO systems

KAUST Repository

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.

Adaptive extremal optimization by detrended fluctuation analysis

International Nuclear Information System (INIS)

Hamacher, K.

2007-01-01

Global optimization is one of the key challenges in computational physics as several problems, e.g. protein structure prediction, the low-energy landscape of atomic clusters, detection of community structures in networks, or model-parameter fitting can be formulated as global optimization problems. Extremal optimization (EO) has become in recent years one particular, successful approach to the global optimization problem. As with almost all other global optimization approaches, EO is driven by an internal dynamics that depends crucially on one or more parameters. Recently, the existence of an optimal scheme for this internal parameter of EO was proven, so as to maximize the performance of the algorithm. However, this proof was not constructive, that is, one cannot use it to deduce the optimal parameter itself a priori. In this study we analyze the dynamics of EO for a test problem (spin glasses). Based on the results we propose an online measure of the performance of EO and a way to use this insight to reformulate the EO algorithm in order to construct optimal values of the internal parameter online without any input by the user. This approach will ultimately allow us to make EO parameter free and thus its application in general global optimization problems much more efficient

A genetic algorithm applied to a PWR turbine extraction optimization to increase cycle efficiency

International Nuclear Information System (INIS)

Sacco, Wagner F.; Schirru, Roberto

2002-01-01

In nuclear power plants feedwater heaters are used to heat feedwater from its temperature leaving the condenser to final feedwater temperature using steam extracted from various stages of the turbines. The purpose of this process is to increase cycle efficiency. The determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines is a complex optimization problem. This kind of problem has been efficiently solved by means of evolutionary computation techniques, such as Genetic Algorithms (GAs). GAs, which are systems based upon principles from biological genetics, have been successfully applied to several combinatorial optimization problems in nuclear engineering, as the nuclear fuel reload optimization problem. We introduce the use of GAs in cycle efficiency optimization by finding an optimal combination of turbine extractions. In order to demonstrate the effectiveness of our approach, we have chosen a typical PWR as case study. The secondary side of the PWR was simulated using PEPSE, which is a modeling tool used to perform integrated heat balances for power plants. The results indicate that the GA is a quite promising tool for cycle efficiency optimization. (author)

Global map of solar power production efficiency, considering micro climate factors

Science.gov (United States)

Hassanpour Adeh, E.; Higgins, C. W.

2017-12-01

Natural resources degradation and greenhouse gas emissions are creating a global crisis. Renewable energy is the most reliable option to mitigate this environmental dilemma. Abundancy of solar energy makes it highly attractive source of electricity. The existing global spatial maps of available solar energy are created with various models which consider the irradiation, latitude, cloud cover, elevation, shading and aerosols, and neglect the influence of local meteorological conditions. In this research, the influences of microclimatological variables on solar energy productivity were investigated with an in-field study at the Rabbit Hills solar arrays near Oregon State University. The local studies were extended to a global level, where global maps of solar power were produced, taking the micro climate variables into account. These variables included: temperature, relative humidity, wind speed, wind direction, solar radiation. The energy balance approach was used to synthesize the data and compute the efficiencies. The results confirmed that the solar power efficiency can be directly affected by the air temperature and wind speed.

An efficient cost function for the optimization of an n-layered isotropic cloaked cylinder

International Nuclear Information System (INIS)

Paul, Jason V; Collins, Peter J; Coutu, Ronald A Jr

2013-01-01

In this paper, we present an efficient cost function for optimizing n-layered isotropic cloaked cylinders. Cost function efficiency is achieved by extracting the expression for the angle independent scatterer contribution of an associated Green's function. Therefore, since this cost function is not a function of angle, accounting for every bistatic angle is not necessary and thus more efficient than other cost functions. With this general and efficient cost function, isotropic cloaked cylinders can be optimized for many layers and material parameters. To demonstrate this, optimized cloaked cylinders made of 10, 20 and 30 equal thickness layers are presented for TE and TM incidence. Furthermore, we study the effect layer thickness has on optimized cloaks by optimizing a 10 layer cloaked cylinder over the material parameters and individual layer thicknesses. The optimized material parameters in this effort do not exhibit the dual nature that is evident in the ideal transformation optics design. This indicates that the inevitable field penetration and subsequent PEC boundary condition at the cylinder must be taken into account for an optimal cloaked cylinder design. Furthermore, a more effective cloaked cylinder can be designed by optimizing both layer thickness and material parameters than by additional layers alone. (paper)

Efficiency Enhancement for an Inductive Wireless Power Transfer System by Optimizing the Impedance Matching Networks.

Science.gov (United States)

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.

Optimal Stomatal Behaviour Around the World: Synthesis of a Global Stomatal Conductance Database and Scaling from Leaf to Ecosystem

Science.gov (United States)

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.

Design and optimization of automotive thermoelectric generators for maximum fuel efficiency improvement

International Nuclear Information System (INIS)

Kempf, Nicholas; Zhang, Yanliang

2016-01-01

Highlights: • A three-dimensional automotive thermoelectric generator (TEG) model is developed. • Heat exchanger design and TEG configuration are optimized for maximum fuel efficiency increase. • Heat exchanger conductivity has a strong influence on maximum fuel efficiency increase. • TEG aspect ratio and fin height increase with heat exchanger thermal conductivity. • A 2.5% fuel efficiency increase is attainable with nanostructured half-Heusler modules. - Abstract: Automotive fuel efficiency can be increased by thermoelectric power generation using exhaust waste heat. A high-temperature thermoelectric generator (TEG) that converts engine exhaust waste heat into electricity is simulated based on a light-duty passenger vehicle with a 4-cylinder gasoline engine. Strategies to optimize TEG configuration and heat exchanger design for maximum fuel efficiency improvement are provided. Through comparison of stainless steel and silicon carbide heat exchangers, it is found that both the optimal TEG design and the maximum fuel efficiency increase are highly dependent on the thermal conductivity of the heat exchanger material. Significantly higher fuel efficiency increase can be obtained using silicon carbide heat exchangers at taller fins and a longer TEG along the exhaust flow direction when compared to stainless steel heat exchangers. Accounting for major parasitic losses, a maximum fuel efficiency increase of 2.5% is achievable using newly developed nanostructured bulk half-Heusler thermoelectric modules.

Global and radial variations in the efficiency of massive star formation among galaxies

International Nuclear Information System (INIS)

Allen, L.E.; Young, J.S.

1990-01-01

In order to determine the regions within galaxies which give rise to the most efficient star formation and to test the hypothesis that galaxies with high infrared luminosities per unit molecular mass are efficiently producing high mass stars, researchers have undertaken an H alpha imaging survey in galaxies whose CO distributions have been measured as part of the Five College Radio Astronomy Observatory (FCRAO) Extragalactic CO Survey. From these images researchers have derived global H alpha fluxes and distributions for comparison with far infrared radiation (FIR) fluxes and CO fluxes and distributions. Here, researchers present results on the global massive star formation efficiency (SFE = L sub H sub alpha/M(H2)) as a function of morphological type and environment, and on the radial distribution of the SFE within both peculiar and isolated galaxies. On the basis of comparison of the global L sub H sub alpha/M(H2) and L sub FIR/M(H2) for 111 galaxies, researchers conclude that environment rather than morphological type has the strongest effect on the global efficiency of massive star formation. Based on their study of a small sample, they find that the largest radial gradients are observed in the interacting/peculiar galaxies, indicating that environment affects the star formation efficiency within galaxies as well

A global carbon assimilation system based on a dual optimization method

Science.gov (United States)

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.

A kind of balance between exploitation and exploration on kriging for global optimization of expensive functions

International Nuclear Information System (INIS)

Dong, Huachao; Song, Baowei; Wang, Peng; Huang, Shuai

2015-01-01

In this paper, a novel kriging-based algorithm for global optimization of computationally expensive black-box functions is presented. This algorithm utilizes a multi-start approach to find all of the local optimal values of the surrogate model and performs searches within the neighboring area around these local optimal positions. Compared with traditional surrogate-based global optimization method, this algorithm provides another kind of balance between exploitation and exploration on kriging-based model. In addition, a new search strategy is proposed and coupled into this optimization process. The local search strategy employs a kind of improved 'Minimizing the predictor' method, which dynamically adjusts search direction and radius until finds the optimal value. Furthermore, the global search strategy utilizes the advantage of kriging-based model in predicting unexplored regions to guarantee the reliability of the algorithm. Finally, experiments on 13 test functions with six algorithms are set up and the results show that the proposed algorithm is very promising.

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

Science.gov (United States)

Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

2015-01-01

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

ProxImaL: efficient image optimization using proximal algorithms

KAUST Repository

Heide, Felix

2016-07-11

Computational photography systems are becoming increasingly diverse, while computational resources-for example on mobile platforms-are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

GLOBAL OPTIMIZATION METHODS FOR GRAVITATIONAL LENS SYSTEMS WITH REGULARIZED SOURCES

International Nuclear Information System (INIS)

Rogers, Adam; Fiege, Jason D.

2012-01-01

Several approaches exist to model gravitational lens systems. In this study, we apply global optimization methods to find the optimal set of lens parameters using a genetic algorithm. We treat the full optimization procedure as a two-step process: an analytical description of the source plane intensity distribution is used to find an initial approximation to the optimal lens parameters; the second stage of the optimization uses a pixelated source plane with the semilinear method to determine an optimal source. Regularization is handled by means of an iterative method and the generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly used in standard image deconvolution problems. This approach simultaneously estimates the optimal regularization parameter and the number of degrees of freedom in the source. Using the GCV and UPRE functions, we are able to justify an estimation of the number of source degrees of freedom found in previous work. We test our approach by applying our code to a subset of the lens systems included in the SLACS survey.

Global optimization of minority game by intelligent agents

Science.gov (United States)

Xie, Yan-Bo; Wang, Bing-Hong; Hu, Chin-Kun; Zhou, Tao

2005-10-01

We propose a new model of minority game with intelligent agents who use trail and error method to make a choice such that the standard deviation σ2 and the total loss in this model reach the theoretical minimum values in the long time limit and the global optimization of the system is reached. This suggests that the economic systems can self-organize into a highly optimized state by agents who make decisions based on inductive thinking, limited knowledge, and capabilities. When other kinds of agents are also present, the simulation results and analytic calculations show that the intelligent agent can gain profits from producers and are much more competent than the noise traders and conventional agents in original minority games proposed by Challet and Zhang.

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

use parameters to describe the topology of modular robots, develop a probabilistic model of local communication using these parameters and, using a model of global communication from literature, compare the transmission times of local and global communication in different robots. Based on our results......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...

Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments

Directory of Open Access Journals (Sweden)

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.

Dual Schroedinger Equation as Global Optimization Algorithm

International Nuclear Information System (INIS)

Huang Xiaofei; eGain Communications, Mountain View, CA 94043

2011-01-01

The dual Schroedinger equation is defined as replacing the imaginary number i by -1 in the original one. This paper shows that the dual equation shares the same stationary states as the original one. Different from the original one, it explicitly defines a dynamic process for a system to evolve from any state to lower energy states and eventually to the lowest one. Its power as a global optimization algorithm might be used by nature for constructing atoms and molecules. It shall be interesting to verify its existence in nature.

Effective Energy Methods for Global Optimization for Biopolymer Structure Prediction

National Research Council Canada - National Science Library

Shalloway, David

1998-01-01

.... Its main strength is that it uncovers and exploits the intrinsic "hidden structures" of biopolymer energy landscapes to efficiently perform global minimization using a hierarchical search procedure...

Modeling and operation optimization of a proton exchange membrane fuel cell system for maximum efficiency

International Nuclear Information System (INIS)

Han, In-Su; Park, Sang-Kyun; Chung, Chang-Bock

2016-01-01

Highlights: • A proton exchange membrane fuel cell system is operationally optimized. • A constrained optimization problem is formulated to maximize fuel cell efficiency. • Empirical and semi-empirical models for most system components are developed. • Sensitivity analysis is performed to elucidate the effects of major operating variables. • The optimization results are verified by comparison with actual operation data. - Abstract: This paper presents an operation optimization method and demonstrates its application to a proton exchange membrane fuel cell system. A constrained optimization problem was formulated to maximize the efficiency of a fuel cell system by incorporating practical models derived from actual operations of the system. Empirical and semi-empirical models for most of the system components were developed based on artificial neural networks and semi-empirical equations. Prior to system optimizations, the developed models were validated by comparing simulation results with the measured ones. Moreover, sensitivity analyses were performed to elucidate the effects of major operating variables on the system efficiency under practical operating constraints. Then, the optimal operating conditions were sought at various system power loads. The optimization results revealed that the efficiency gaps between the worst and best operation conditions of the system could reach 1.2–5.5% depending on the power output range. To verify the optimization results, the optimal operating conditions were applied to the fuel cell system, and the measured results were compared with the expected optimal values. The discrepancies between the measured and expected values were found to be trivial, indicating that the proposed operation optimization method was quite successful for a substantial increase in the efficiency of the fuel cell system.

Improving the efficiency of aerodynamic shape optimization procedures

Science.gov (United States)

Burgreen, Greg W.; Baysal, Oktay; Eleshaky, Mohamed E.

1992-01-01

The computational efficiency of an aerodynamic shape optimization procedure which is based on discrete sensitivity analysis is increased through the implementation of two improvements. The first improvement involves replacing a grid point-based approach for surface representation with a Bezier-Bernstein polynomial parameterization of the surface. Explicit analytical expressions for the grid sensitivity terms are developed for both approaches. The second improvement proposes the use of Newton's method in lieu of an alternating direction implicit (ADI) methodology to calculate the highly converged flow solutions which are required to compute the sensitivity coefficients. The modified design procedure is demonstrated by optimizing the shape of an internal-external nozzle configuration. A substantial factor of 8 decrease in computational time for the optimization process was achieved by implementing both of the design improvements.

Model-Based Energy Efficiency Optimization of a Low-Temperature Adsorption Dryer

NARCIS (Netherlands)

Atuonwu, J.C.; Straten, G. van; Deventer, H.C. van; Boxtel, A.J.B. van

2011-01-01

Low-temperature drying is important for heat-sensitive products, but at these temperatures conventional convective dryers have low energy efficiencies. To overcome this challenge, an energy efficiency optimization procedure is applied to a zeolite adsorption dryer subject to product quality. The

Optimizing cost-efficiency in mean exposure assessment--cost functions reconsidered.

Science.gov (United States)

Mathiassen, Svend Erik; Bolin, Kristian

2011-05-21

Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios. Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components. Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set. The analysis procedures developed in the present study can be used

Truss topology optimization with discrete design variables — Guaranteed global optimality and benchmark examples

DEFF Research Database (Denmark)

Achtziger, Wolfgang; Stolpe, Mathias

2007-01-01

this problem is well-studied for continuous bar areas, we consider in this study the case of discrete areas. This problem is of major practical relevance if the truss must be built from pre-produced bars with given areas. As a special case, we consider the design problem for a single available bar area, i.......e., a 0/1 problem. In contrast to the heuristic methods considered in many other approaches, our goal is to compute guaranteed globally optimal structures. This is done by a branch-and-bound method for which convergence can be proven. In this branch-and-bound framework, lower bounds of the optimal......-integer problems. The main intention of this paper is to provide optimal solutions for single and multiple load benchmark examples, which can be used for testing and validating other methods or heuristics for the treatment of this discrete topology design problem....

Economic optimization of a global strategy to address the pandemic threat.

Science.gov (United States)

Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C; Daszak, Peter

2014-12-30

Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual.

Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture

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.

Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture

International Nuclear Information System (INIS)

Rattá, G.A.; Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.

2016-01-01

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.

Incentive Compatible and Globally Efficient Position Based Routing for Selfish Reverse Multicast in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sarvesh Varatharajan

2009-10-01

Full Text Available We consider the problem of all-to-one selfish routing in the absence of a payment scheme in wireless sensor networks, where a natural model for cost is the power required to forward, referring to the resulting game as a Locally Minimum Cost Forwarding (LMCF. Our objective is to characterize equilibria and their global costs in terms of stretch and diameter, in particular finding incentive compatible algorithms that are also close to globally optimal. We find that although social costs for equilibria of LMCF exhibit arbitrarily bad worst-case bounds and computational infeasibility of reaching optimal equilibria, there exist greedy and local incentive compatible heuristics achieving near-optimal global costs.

Global optimization for quantum dynamics of few-fermion systems

Science.gov (United States)

Li, Xikun; Pecak, Daniel; Sowiński, Tomasz; Sherson, Jacob; Nielsen, Anne E. B.

2018-03-01

Quantum state preparation is vital to quantum computation and quantum information processing tasks. In adiabatic state preparation, the target state is theoretically obtained with nearly perfect fidelity if the control parameter is tuned slowly enough. As this, however, leads to slow dynamics, it is often desirable to be able to carry out processes more rapidly. In this work, we employ two global optimization methods to estimate the quantum speed limit for few-fermion systems confined in a one-dimensional harmonic trap. Such systems can be produced experimentally in a well-controlled manner. We determine the optimized control fields and achieve a reduction in the ramping time of more than a factor of four compared to linear ramping. We also investigate how robust the fidelity is to small variations of the control fields away from the optimized shapes.

The International Database of Efficient Appliances (IDEA): A New Resource for Global Efficiency Policy

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.

Optimization of Energy Efficiency and Conservation in Green Building Design Using Duelist, Killer-Whale and Rain-Water Algorithms

Science.gov (United States)

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.

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.

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.

Efficient solution method for optimal control of nuclear systems

International Nuclear Information System (INIS)

Naser, J.A.; Chambre, P.L.

1981-01-01

To improve the utilization of existing fuel sources, the use of optimization techniques is becoming more important. A technique for solving systems of coupled ordinary differential equations with initial, boundary, and/or intermediate conditions is given. This method has a number of inherent advantages over existing techniques as well as being efficient in terms of computer time and space requirements. An example of computing the optimal control for a spatially dependent reactor model with and without temperature feedback is given. 10 refs

Global structural optimizations of surface systems with a genetic algorithm

International Nuclear Information System (INIS)

Chuang, Feng-Chuan

2005-01-01

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al n (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic 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

Game-Theoretic Rate-Distortion-Complexity Optimization of High Efficiency Video Coding

DEFF Research Database (Denmark)

Ukhanova, Ann; Milani, Simone; Forchhammer, Søren

2013-01-01

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......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...

Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Mehrabipour, Reza

2012-01-01

A simple gas turbine cycle namely as the Kraftwerk Union AG unit including a Siemens gas turbine model V93.1 with 60 MW nominal power and 26.0% thermal efficiency utilized in the Fars power plant located is considered for the efficiency enhancement. A typical tubular vertical recuperative heat exchanger is designed in order to integrate into the cycle as an air pre-heater for thermal efficiency improvement. Thermal and geometric specifications of the recuperative heat exchanger are obtained in a multi-objective optimization process. The exergetic efficiency of the gas cycle is maximized while the payback time for the capital investment of the recuperator is minimized. Combination of these objectives and decision variables with suitable engineering and physical constraints makes a set of the MINLP optimization problem. Optimization programming is performed using the NSGA-II algorithm and Pareto optimal frontiers are obtained in three cases including the minimum, average and maximum ambient air temperatures. In each case, the final optimal solution has been selected using three decision-making approaches including the fuzzy Bellman-Zadeh, LINMAP and TOPSIS methods. It has been shown that the TOPSIS and LINMAP decision-makers when applied on the Pareto frontier which is obtained at average ambient air temperature yields best results in comparison to other cases. -- Highlights: ► A simple Brayton gas cycle is considered for the efficiency improvement by integrating of a recuperator. ► Objective functions based on thermodynamic and economic analysis are obtained. ► The payback time for the capital investment is minimized and the exergetic efficiency of the system is maximized. ► Pareto optimal frontiers at various site conditions are obtained. ► A final optimal configuration is found using various decision-making approaches.

A Global Network Alignment Method Using Discrete Particle Swarm Optimization.

Science.gov (United States)

Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia

2016-10-19

Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.

Optimal and efficient decoding of concatenated quantum block codes

International Nuclear Information System (INIS)

Poulin, David

2006-01-01

We consider the problem of optimally decoding a quantum error correction code--that is, to find the optimal recovery procedure given the outcomes of partial ''check'' measurements on the system. In general, this problem is NP hard. However, we demonstrate that for concatenated block codes, the optimal decoding can be efficiently computed using a message-passing algorithm. We compare the performance of the message-passing algorithm to that of the widespread blockwise hard decoding technique. Our Monte Carlo results using the five-qubit and Steane's code on a depolarizing channel demonstrate significant advantages of the message-passing algorithms in two respects: (i) Optimal decoding increases by as much as 94% the error threshold below which the error correction procedure can be used to reliably send information over a noisy channel; and (ii) for noise levels below these thresholds, the probability of error after optimal decoding is suppressed at a significantly higher rate, leading to a substantial reduction of the error correction overhead

Efficiency optimization of wireless power transmission systems for active capsule endoscopes.

Science.gov (United States)

Zhiwei, Jia; Guozheng, Yan; Jiangpingping; Zhiwu, Wang; Hua, Liu

2011-10-01

Multipurpose active capsule endoscopes have drawn considerable attention in recent years, but these devices continue to suffer from energy limitations. A wireless power supply system is regarded as a practical way to overcome the power shortage problem in such devices. This paper focuses on the efficiency optimization of a wireless energy supply system with size and safety constraints. A mathematical programming model in which these constraints are considered is proposed for transmission efficiency, optimal frequency and current, and overall system effectiveness. To verify the feasibility of the proposed method, we use a wireless active capsule endoscope as an illustrative example. The achieved efficiency can be regarded as an index for evaluating the system, and the proposed approach can be used to direct the design of transmitting and receiving coils.

Efficiency optimization of wireless power transmission systems for active capsule endoscopes

International Nuclear Information System (INIS)

Zhiwei, Jia; Guozheng, Yan; Jiangpingping; Zhiwu, Wang; Hua, Liu

2011-01-01

Multipurpose active capsule endoscopes have drawn considerable attention in recent years, but these devices continue to suffer from energy limitations. A wireless power supply system is regarded as a practical way to overcome the power shortage problem in such devices. This paper focuses on the efficiency optimization of a wireless energy supply system with size and safety constraints. A mathematical programming model in which these constraints are considered is proposed for transmission efficiency, optimal frequency and current, and overall system effectiveness. To verify the feasibility of the proposed method, we use a wireless active capsule endoscope as an illustrative example. The achieved efficiency can be regarded as an index for evaluating the system, and the proposed approach can be used to direct the design of transmitting and receiving coils

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.

Increase of Gas-Turbine Plant Efficiency by Optimizing Operation of Compressors

Science.gov (United States)

Matveev, V.; Goriachkin, E.; Volkov, A.

2018-01-01

The article presents optimization method for improving of the working process of axial compressors of gas turbine engines. Developed method allows to perform search for the best geometry of compressor blades automatically by using optimization software IOSO and CFD software NUMECA Fine/Turbo. The calculation of the compressor parameters was performed for work and stall point of its performance map on each optimization step. Study was carried out for seven-stage high-pressure compressor and three-stage low-pressure compressors. As a result of optimization, improvement of efficiency was achieved for all investigated compressors.

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

, we conclude that global communication is convenient for centralized control approaches and local communication is convenient for distributed control approaches. In addition, we conclude that global is in general convenient for low-connectivity configurations, such as chains, trees or limbs......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...... use parameters to describe the topology of modular robots, develop a probabilistic model of local communication using these parameters and, using a model of global communication from literature, compare the transmission times of local and global communication in different robots. Based on our results...

Real time PI-backstepping induction machine drive with efficiency optimization.

Science.gov (United States)

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.

A fractional optimal control problem for maximizing advertising efficiency

OpenAIRE

Igor Bykadorov; Andrea Ellero; Stefania Funari; Elena Moretti

2007-01-01

We propose an optimal control problem to model the dynamics of the communication activity of a firm with the aim of maximizing its efficiency. We assume that the advertising effort undertaken by the firm contributes to increase the firm's goodwill and that the goodwill affects the firm's sales. The aim is to find the advertising policies in order to maximize the firm's efficiency index which is computed as the ratio between "outputs" and "inputs" properly weighted; the outputs are represented...

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.

Economic optimization of a global strategy to address the pandemic threat

Science.gov (United States)

Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C.; Daszak, Peter

2014-01-01

Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral “One Health” pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual. PMID:25512538

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.

Dynamic water allocation policies improve the global efficiency of storage systems

Science.gov (United States)

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.

Optimally efficient swimming in hyper-redundant mechanisms: control, design, and energy recovery

International Nuclear Information System (INIS)

Wiens, A J; Nahon, M

2012-01-01

Hyper-redundant mechanisms (HRMs), also known as snake-like robots, are highly adaptable during locomotion on land. Researchers are currently working to extend their capabilities to aquatic environments through biomimetic undulatory propulsion. In addition to increasing the versatility of the system, truly biomimetic swimming could also provide excellent locomotion efficiency. Unfortunately, the complexity of the system precludes the development of a functional solution to achieve this. To explore this problem, a rapid optimization process is used to generate efficient HRM swimming gaits. The low computational cost of the approach allows for multiple optimizations over a broad range of system conditions. By observing how these conditions affect optimal kinematics, a number of new insights are developed regarding undulatory swimming in robotic systems. Two key conditions are varied within the study, swimming speed and energy recovery. It is found that the swimmer mimics the speed control behaviour of natural fish and that energy recovery drastically increases the system's efficiency. Remarkably, this efficiency increase is accompanied by a distinct change in swimming kinematics. With energy recovery, the swimmer converges to a clearly anguilliform gait, without, it tends towards the carangiform mode. (paper)

Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

International Nuclear Information System (INIS)

Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu

2013-01-01

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.

Energy efficient LED layout optimization for near-uniform illumination

Science.gov (United States)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Optimal power and efficiency of quantum Stirling heat engines

Science.gov (United States)

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 branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

KAUST Repository

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.

Characteristic statistic algorithm (CSA) for in-core loading pattern optimization

International Nuclear Information System (INIS)

Liu Zhihong; Hu Yongming; Shi Gong

2007-01-01

To solve the problem of PWR in-core loading pattern optimization, a more suitable global optimization algorithm, i.e., Characteristic statistic algorithm (CSA), is used. The searching process of this algorithm and how to apply it to this problem are presented. Loading pattern optimization code SCYCLE is developed. Two different problems on real PWR models are calculated and the results are compared with other algorithms. It is shown that SCYCLE has high efficiency and good global performance on this problem. (authors)

Optimal pitching axis location of flapping wings for efficient hovering flight.

Science.gov (United States)

Wang, Q; Goosen, J F L; van Keulen, F

2017-09-01

Flapping wings can pitch passively about their pitching axes due to their flexibility, inertia, and aerodynamic loads. A shift in the pitching axis location can dynamically alter the aerodynamic loads, which in turn changes the passive pitching motion and the flight efficiency. Therefore, it is of great interest to investigate the optimal pitching axis for flapping wings to maximize the power efficiency during hovering flight. In this study, flapping wings are modeled as rigid plates with non-uniform mass distribution. The wing flexibility is represented by a linearly torsional spring at the wing root. A predictive quasi-steady aerodynamic model is used to evaluate the lift generated by such wings. Two extreme power consumption scenarios are modeled for hovering flight, i.e. the power consumed by a drive system with and without the capacity of kinetic energy recovery. For wings with different shapes, the optimal pitching axis location is found such that the cycle-averaged power consumption during hovering flight is minimized. Optimization results show that the optimal pitching axis is located between the leading edge and the mid-chord line, which shows close resemblance to insect wings. An optimal pitching axis can save up to 33% of power during hovering flight when compared to traditional wings used by most of flapping wing micro air vehicles (FWMAVs). Traditional wings typically use the straight leading edge as the pitching axis. With the optimized pitching axis, flapping wings show higher pitching amplitudes and start the pitching reversals in advance of the sweeping reversals. These phenomena lead to higher lift-to-drag ratios and, thus, explain the lower power consumption. In addition, the optimized pitching axis provides the drive system higher potential to recycle energy during the deceleration phases as compared to their counterparts. This observation underlines the particular importance of the wing pitching axis location for energy-efficient FWMAVs when

Efficiency optimization of green phosphorescent organic light-emitting device

Energy Technology Data Exchange (ETDEWEB)

Park, Jung Soo; Jeon, Woo Sik; Yu, Jae Hyung [Department of Information Display, Kyung Hee University, Dongdaemoon-gu, Seoul 130-701 (Korea, Republic of); Pode, Ramchandra, E-mail: rbpode@khu.ac.k [Department of Physics, Kyung Hee University, Dongdaemoon-gu, Seoul 130-701 (Korea, Republic of); Kwon, Jang Hyuk, E-mail: jhkwon@khu.ac.k [Department of Information Display, Kyung Hee University, Dongdaemoon-gu, Seoul 130-701 (Korea, Republic of)

2011-03-01

Using a narrow band gap host of bis[2-(2-hydroxyphenyl)-pyridine]beryllium (Bepp{sub 2}) and green phosphorescent Ir(ppy){sub 3} [fac-tris(2-phenylpyridine) iridium III] guest concentration as low as 2%, high efficiency phosphorescent organic light-emitting diode (PHOLED) is realized. Current and power efficiencies of 62.5 cd/A (max.), 51.0 lm/W (max.), and external quantum efficiency (max.) of 19.8% are reported in this green PHOLED. A low current efficiency roll-off value of 10% over the brightness of 10,000 cd/m{sup 2} is noticed in this Bepp{sub 2} single host device. Such a high efficiency is obtained by the optimization of the doping concentration with the knowledge of the hole trapping and the emission zone situations in this host-guest system. It is suggested that the reported device performance is suitable for applications in high brightness displays and lighting.

A Unified Differential Evolution Algorithm for Global Optimization

Energy Technology Data Exchange (ETDEWEB)

Qiang, Ji; Mitchell, Chad

2014-06-24

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.

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....

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.

Dispositional Optimism and Terminal Decline in Global Quality of Life

Science.gov (United States)

Zaslavsky, Oleg; Palgi, Yuval; Rillamas-Sun, Eileen; LaCroix, Andrea Z.; Schnall, Eliezer; Woods, Nancy F.; Cochrane, Barbara B.; Garcia, Lorena; Hingle, Melanie; Post, Stephen; Seguin, Rebecca; Tindle, Hilary; Shrira, Amit

2015-01-01

We examined whether dispositional optimism relates to change in global quality of life (QOL) as a function of either chronological age or years to impending death. We used a sample of 2,096 deceased postmenopausal women from the Women's Health Initiative clinical trials who were enrolled in the 2005-2010 Extension Study and for whom at least 1…

Air conditioning with methane: Efficiency and economics optimization parameters

International Nuclear Information System (INIS)

Mastrullo, R.; Sasso, M.; Sibilio, S.; Vanoli, R.

1992-01-01

This paper presents an efficiency and economics evaluation method for methane fired cooling systems. Focus is on direct flame two staged absorption systems and alternative engine driven compressor sets. Comparisons are made with conventional vapour compression plants powered by electricity supplied by the national grid. A first and second law based thermodynamics analysis is made in which fuel use coefficients and exergy yields are determined. The economics analysis establishes annual energy savings, unit cooling energy production costs, payback periods and economics/efficiency optimization curves useful for preliminary feasibility studies

Global and regional phosphorus budgets in agricultural systems and their implications for phosphorus-use efficiency

Science.gov (United States)

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.

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.

Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle

Directory of Open Access Journals (Sweden)

B. Mashadi

Full Text Available In this paper, the global optimal path planning of an autonomous vehicle for overtaking a moving obstacle is proposed. In this study, the autonomous vehicle overtakes a moving vehicle by performing a double lane-change maneuver after detecting it in a proper distance ahead. The optimal path of vehicle for performing the lane-change maneuver is generated by a path planning program in which the sum of lateral deviation of the vehicle from a reference path and the rate of steering angle become minimum while the lateral acceleration of vehicle does not exceed a safe limit value. A nonlinear optimal control theory with the lateral vehicle dynamics equations and inequality constraint of lateral acceleration are used to generate the path. The indirect approach for solving the optimal control problem is used by applying the calculus of variation and the Pontryagin's Minimum Principle to obtain first-order necessary conditions for optimality. The optimal path is generated as a global optimal solution and can be used as the benchmark of the path generated by the local motion planning of autonomous vehicles. A full nonlinear vehicle model in CarSim software is used for path following simulation by importing path data from the MATLAB code. The simulation results show that the generated path for the autonomous vehicle satisfies all vehicle dynamics constraints and hence is a suitable overtaking path for the following vehicle.

A Linearized Relaxing Algorithm for the Specific Nonlinear Optimization Problem

Directory of Open Access Journals (Sweden)

Mio Horai

2016-01-01

Full Text Available We propose a new method for the specific nonlinear and nonconvex global optimization problem by using a linear relaxation technique. To simplify the specific nonlinear and nonconvex optimization problem, we transform the problem to the lower linear relaxation form, and we solve the linear relaxation optimization problem by the Branch and Bound Algorithm. Under some reasonable assumptions, the global convergence of the algorithm is certified for the problem. Numerical results show that this method is more efficient than the previous methods.

Optimal Design of Composite Structures Under Manufacturing Constraints

DEFF Research Database (Denmark)

Marmaras, Konstantinos

algorithms to perform the global optimization. The efficiency of the proposed models is examined on a set of well–defined discrete multi material and thickness optimization problems originating from the literature. The inclusion of manufacturing limitations along with structural considerations in the early...... mixed integer 0–1 programming problems. The manufacturing constraints have been treated by developing explicit models with favorable properties. In this thesis we have developed and implemented special purpose global optimization methods and heuristic techniques for solving this class of problems......This thesis considers discrete multi material and thickness optimization of laminated composite structures including local failure criteria and manufacturing constraints. Our models closely follow an immediate extension of the Discrete Material Optimization scheme, which allows simultaneous...

Surface reflectance and conversion efficiency dependence of technologies for mitigating global warming

Energy Technology Data Exchange (ETDEWEB)

Edmonds, Ian [Solartran Pty Ltd., 12 Lentara St, Kenmore, Brisbane 4069 (Australia); Smith, Geoff [Physics and Advanced Materials, University of Technology, Sydney, PO Box 123, Broadway, New South Wales 2007 (Australia)

2011-05-15

A means of assessing the relative impact of different renewable energy technologies on global warming has been developed. All power plants emit thermal energy to the atmosphere. Fossil fuel power plants also emit CO{sub 2} which accumulates in the atmosphere and provides an indirect increase in global warming via the greenhouse effect. A fossil fuel power plant may operate for some time before the global warming due to its CO{sub 2} emission exceeds the warming due to its direct heat emission. When a renewable energy power plant is deployed instead of a fossil fuel power plant there may be a significant time delay before the direct global warming effect is less than the combined direct and indirect global warming effect from an equivalent output coal fired plant - the ''business as usual'' case. Simple expressions are derived to calculate global temperature change as a function of ground reflectance and conversion efficiency for various types of fossil fuelled and renewable energy power plants. These expressions are used to assess the global warming mitigation potential of some proposed Australian renewable energy projects. The application of the expressions is extended to evaluate the deployment in Australia of current and new geo-engineering and carbon sequestration solutions to mitigate global warming. Principal findings are that warming mitigation depends strongly on the solar to electric conversion efficiency of renewable technologies, geo-engineering projects may offer more economic mitigation than renewable energy projects and the mitigation potential of reforestation projects depends strongly on the location of the projects. (author)

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

KAUST Repository

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.

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity.

Science.gov (United States)

Kaya, Mine; Hajimirza, Shima

2018-05-25

This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.

Improving the global efficiency in small hydropower practice

Science.gov (United States)

Razurel, P.; Gorla, L.; Crouzy, B.; Perona, P.

2015-12-01

The global increase in energy production from renewable sources has seen river exploitation for small hydropower plants to also grow considerably in the last decade. River intakes used to divert water from the main course to the power plant are at the base of such practice. A key issue concern with finding innovative concepts to both design and manage such structures in order to improve classic operational rules. Among these, the Minimal Flow Release (MFR) concept has long been used in spite of its environmental inconsistency.In this work, we show that the economical and ecological efficiency of diverting water for energy production in small hydropower plants can be improved towards sustainability by engineering a novel class of flow-redistribution policies. We use the mathematical form of the Fermi-Dirac statistical distribution to define non-proportional dynamic flow-redistribution rules, which broadens the spectrum of dynamic flow releases based on proportional redistribution. The theoretical background as well as the economic interpretation is presented and applied to three case studies in order to systematically test the global performance of such policies. Out of numerical simulations, a Pareto frontier emerges in the economic vs environmental efficiency plot, which show that non-proportional distribution policies improve both efficiencies with respect to those obtained from some traditional MFR and proportional policies. This picture is shown also for long term climatic scenarios affecting water availability and the natural flow regime.In a time of intense and increasing exploitation close to resource saturation, preserving natural river reaches requires to abandon inappropriate static release policies in favor of non-proportional ones towards a sustainable use of the water resource.

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

Energy Technology Data Exchange (ETDEWEB)

Li, Dengwang; Wang, Jie [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States)

2015-06-15

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

International Nuclear Information System (INIS)

Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei

2015-01-01

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

Automatic efficiency optimization of an axial compressor with adjustable inlet guide vanes

Science.gov (United States)

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.

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.

Science.gov (United States)

Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon

2012-01-01

Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.

Research on Power Factor Correction Boost Inductor Design Optimization – Efficiency vs. Power Density

DEFF Research Database (Denmark)

Li, Qingnan; Andersen, Michael A. E.; Thomsen, Ole Cornelius

2011-01-01

Nowadays, efficiency and power density are the most important issues for Power Factor Correction (PFC) converters development. However, it is a challenge to reach both high efficiency and power density in a system at the same time. In this paper, taking a Bridgeless PFC (BPFC) as an example......, a useful compromise between efficiency and power density of the Boost inductors on 3.2kW is achieved using an optimized design procedure. The experimental verifications based on the optimized inductor are carried out from 300W to 3.2kW at 220Vac input....

Efficiency improvement and torque ripple minimization of Switched Reluctance Motor using FEM and Seeker Optimization Algorithm

International Nuclear Information System (INIS)

Navardi, Mohammad Javad; Babaghorbani, Behnaz; Ketabi, Abbas

2014-01-01

Highlights: • This paper proposes a new method to optimize a Switched Reluctance Motor (SRM). • A combination of SOA and GA with Finite Element Method (FEM) analysis is employed to solve the SRM design optimization. • The results show that optimized SRM obtains higher average torque and higher efficiency. - Abstract: In this paper, performance optimization of Switched Reluctance Motor (SRM) was determined using Seeker Optimization Algorithm (SOA). The most efficient aim of the algorithm was found for maximum torque value at a minimum mass of the entire construction, following changing the geometric parameters. The optimization process was carried out using a combination of Seeker Optimization Algorithm and Finite Element Method (FEM). Fitness value was calculated by FEM analysis using COMSOL3.4, and the SOA was realized by MATLAB. The proposed method has been applied for a case study and it has been also compared with Genetic Algorithm (GA). The results show that the optimized motor using SOA had higher torque value and efficiency with lower mass and torque ripple, exhibiting the validity of this methodology for SRM design

Increasing efficiency and optimizing thermoelectric power plant equipment. Povyshenie effektivnosti i optimizatsiia teploenergeticheskikh ustanovok

Energy Technology Data Exchange (ETDEWEB)

Andriushchenko, A.I.

1981-01-01

The problems of increasing the efficiency and optimizing the operational conditions of a thermoelectric power plant and providing efficient operational conditions of the primary and auxillary equipment at a thermoelectric power plant are examined. Methodologies and designs for optimizing the primary parameters of the power-generating equipment based on economic factors are given. A number of recommendations for designing equipment based on the research results are given.

Efficient approach for reliability-based optimization based on weighted importance sampling approach

International Nuclear Information System (INIS)

Yuan, Xiukai; Lu, Zhenzhou

2014-01-01

An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology

Hybrid particle swarm optimization algorithm and its application in nuclear engineering

International Nuclear Information System (INIS)

Liu, C.Y.; Yan, C.Q.; Wang, J.J.

2014-01-01

Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%

Climate, Agriculture, Energy and the Optimal Allocation of Global Land Use

Science.gov (United States)

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

Ademe et Vous. International Newsletter No. 43, December 2017. A global initiative for efficient solutions

International Nuclear Information System (INIS)

Martin, Valerie; Seguin-Jacques, Catherine; Aulas, Camille

2017-12-01

Content: - Focus: A global initiative for efficient solutions. On 14 November, during COP23, ADEME officially joined the World Alliance for Efficient Solutions, an initiative launched by the Solar Impulse Foundation. - Expertise: Energy: an industrial sector in transition. At a time when reducing energy consumption and improving energy efficiency seem to be essential to the development of a sustainable and competitive industry, electrical load management has emerged as a promising solution. - Worldwide: Ivory Coast: promoting sustainable construction. From 18 to 20 September, ADEME took part in two events held in Abidjan dedicated to sustainable construction in the Ivory Coast, a subject in which the Agency had a long-standing experience to share at the heart of the Global Alliance for Building and Construction (Global ABC)

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.

Efficient QoS-aware Service Composition

Science.gov (United States)

Alrifai, Mohammad; Risse, Thomas

Web service composition requests are usually combined with endto-end QoS requirements, which are specified in terms of non-functional properties (e.g. response time, throughput and price). The goal of QoS-aware service composition is to find the best combination of services such that their aggregated QoS values meet these end-to-end requirements. Local selection techniques are very efficient but fail short in handling global QoS constraints. Global optimization techniques, on the other hand, can handle global constraints, but their poor performance render them inappropriate for applications with dynamic and real-time requirements. In this paper we address this problem and propose a solution that combines global optimization with local selection techniques for achieving a better performance. The proposed solution consists of two steps: first we use mixed integer linear programming (MILP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use local search to find the best web services that satisfy these local constraints. Unlike existing MILP-based global planning solutions, the size of the MILP model in our case is much smaller and independent on the number of available services, yields faster computation and more scalability. Preliminary experiments have been conducted to evaluate the performance of the proposed solution.

Assessment of global phase uncertainty in case-control studies

Directory of Open Access Journals (Sweden)

van Houwelingen Hans C

2009-09-01

Full Text Available Abstract Background In haplotype-based candidate gene studies a problem is that the genotype data are unphased, which results in haplotype ambiguity. The measure 1 quantifies haplotype predictability from genotype data. It is computed for each individual haplotype, and for a measure of global relative efficiency a minimum value is suggested. Alternatively, we developed methods directly based on the information content of haplotype frequency estimates to obtain global relative efficiency measures: and based on A- and D-optimality, respectively. All three methods are designed for single populations; they can be applied in cases only, controls only or the whole data. Therefore they are not necessarily optimal for haplotype testing in case-control studies. Results A new global relative efficiency measure was derived to maximize power of a simple test statistic that compares haplotype frequencies in cases and controls. Application to real data showed that our proposed method gave a clear and summarizing measure for the case-control study conducted. Additionally this measure might be used for selection of individuals, who have the highest potential for improving power by resolving phase ambiguity. Conclusion Instead of using relative efficiency measure for cases only, controls only or their combined data, we link uncertainty measure to case-control studies directly. Hence, our global efficiency measure might be useful to assess whether data are informative or have enough power for estimation of a specific haplotype risk.

Utilization of Flexible Airspace Structure in Flight Efficiency Optimization

Directory of Open Access Journals (Sweden)

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.

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.

Spatiotemporal radiotherapy planning using a global optimization approach

Science.gov (United States)

Adibi, Ali; Salari, Ehsan

2018-02-01

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.

Globally optimal superconducting magnets part II: symmetric MSE coil arrangement.

Science.gov (United States)

Tieng, Quang M; Vegh, Viktor; Brereton, Ian M

2009-01-01

A globally optimal superconducting magnet coil design procedure based on the Minimum Stored Energy (MSE) current density map is outlined. The method has the ability to arrange coils in a manner that generates a strong and homogeneous axial magnetic field over a predefined region, and ensures the stray field external to the assembly and peak magnetic field at the wires are in acceptable ranges. The outlined strategy of allocating coils within a given domain suggests that coils should be placed around the perimeter of the domain with adjacent coils possessing alternating winding directions for optimum performance. The underlying current density maps from which the coils themselves are derived are unique, and optimized to possess minimal stored energy. Therefore, the method produces magnet designs with the lowest possible overall stored energy. Optimal coil layouts are provided for unshielded and shielded short bore symmetric superconducting magnets.

RDEL: Restart Differential Evolution algorithm with Local Search Mutation for global numerical optimization

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.

Global and regional phosphorus budgets in agricultural systems and their implications for phosphorus-use efficiency

Directory of Open Access Journals (Sweden)

F. Lun

2018-01-01

Full Text Available 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.

Global optimization in the adaptive assay of subterranean uranium nodules

International Nuclear Information System (INIS)

Vulkan, U.; Ben-Haim, Y.

1989-01-01

An adaptive assay is one in which the design of the assay system is modified during operation in response to measurements obtained on-line. The present work has two aims: to design an adaptive system for borehole assay of isolated subterranean uranium nodules, and to investigate globality of optimal design in adaptive assay. It is shown experimentally that reasonably accurate estimates of uranium mass are obtained for a wide range of nodule shapes, on the basis of an adaptive assay system based on a simple geomorphological model. Furthermore, two concepts are identified which underlie the optimal design of the assay system. The adaptive assay approach shows promise for successful measurement of spatially random material in many geophysical applications. (author)

Reloading optimization of pressurized water reactor core with burnable absorber fuel

International Nuclear Information System (INIS)

Shi Xiuan; Liu Zhihong; Hu Yongming

2008-01-01

The reloading optimization problem of PWR with burnable absorber fuel is very difficult, and common optimization algorithms are inefficient and have bad global performance for it. Characteristic statistic algorithm (CSA) is very fit for the problem. In the past, the reloading optimization using CSA has shortcomings of separating the fuel assemblies' loading pattern (LP) optimization from burnable absorber's placement (BP) optimization. In this study, LP and BP were optimized simultaneously using CSA coupled with CYCLE2D, which is a core analysis code. The corresponding reloading coupling optimization software, CSALPBP, was developed. The 10th cycle reloading design of Daya Bay Nuclear Power Plant was optimized using CSALPBP. The results show that CSALPBP has high efficiency and excellent global performance. (authors)

Global optimization numerical strategies for rate-independent processes

Czech Academy of Sciences Publication Activity Database

Benešová, Barbora

2011-01-01

Roč. 50, č. 2 (2011), s. 197-220 ISSN 0925-5001 R&D Projects: GA ČR GAP201/10/0357 Grant - others:GA MŠk(CZ) LC06052 Program:LC Institutional research plan: CEZ:AV0Z20760514 Keywords : rate-independent processes * numerical global optimization * energy estimates based algorithm Subject RIV: BA - General Mathematics Impact factor: 1.196, year: 2011 http://math.hnue.edu.vn/portal/rss.viewpage.php?id=0000037780&ap=L3BvcnRhbC9ncmFiYmVyLnBocD9jYXRpZD0xMDEyJnBhZ2U9Mg==

An Energy-Efficient Scheme for Multirelay Cooperative Networks with Energy Harvesting

Directory of Open Access Journals (Sweden)

Dingcheng Yang

2016-01-01

Full Text Available This study investigates an energy-efficient scheme in multirelay cooperative networks with energy harvesting where multiple sessions need to communicate with each other via the relay node. A two-step optimal method is proposed which maximizes the system energy efficiency, while taking into account the receiver circuit energy consumption. Firstly, the optimal power allocation for relay nodes is determined to maximize the system throughput; this is based on directional water-filling algorithm. Secondly, using quantum particle swarm optimization (QPSO, a joint relay node selection and session grouping optimization is proposed. With this algorithm, sessions can be classified into multiple groups that are assisted by the specific relay node with the maximum energy efficiency. This approach leads to a better global optimization in searching ability and efficiency. Simulation results show that the proposed scheme can improve the energy efficiency effectively compared with direct transmission and opportunistic relay-selected cooperative transmission.

Two Modified Three-Term Type Conjugate Gradient Methods and Their Global Convergence for Unconstrained Optimization

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.

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

Global-Local Analysis and Optimization of a Composite Civil Tilt-Rotor Wing

Science.gov (United States)

Rais-Rohani, Masound

1999-01-01

This report gives highlights of an investigation on the design and optimization of a thin composite wing box structure for a civil tilt-rotor aircraft. Two different concepts are considered for the cantilever wing: (a) a thin monolithic skin design, and (b) a thick sandwich skin design. Each concept is examined with three different skin ply patterns based on various combinations of 0, +/-45, and 90 degree plies. The global-local technique is used in the analysis and optimization of the six design models. The global analysis is based on a finite element model of the wing-pylon configuration while the local analysis uses a uniformly supported plate representing a wing panel. Design allowables include those on vibration frequencies, panel buckling, and material strength. The design optimization problem is formulated as one of minimizing the structural weight subject to strength, stiffness, and d,vnamic constraints. Six different loading conditions based on three different flight modes are considered in the design optimization. The results of this investigation reveal that of all the loading conditions the one corresponding to the rolling pull-out in the airplane mode is the most stringent. Also the frequency constraints are found to drive the skin thickness limits, rendering the buckling constraints inactive. The optimum skin ply pattern for the monolithic skin concept is found to be (((0/+/-45/90/(0/90)(sub 2))(sub s))(sub s), while for the sandwich skin concept the optimal ply pattern is found to be ((0/+/-45/90)(sub 2s))(sub s).

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 improvemen...... of the basic code to include recent PDE-based and black-and-white projection filtering methods. The complete 88 line code is included as an appendix and can be downloaded from the web site www.topopt.dtu.dk....

Optimal perturbations for nonlinear systems using graph-based optimal transport

Science.gov (United States)

Grover, Piyush; Elamvazhuthi, Karthik

2018-06-01

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

Energy efficient cross layer design for spectrum sharing systems

KAUST Repository

Alabbasi, Abdulrahman

2016-10-06

We propose a cross layer design that optimizes the energy efficiency of spectrum sharing systems. The energy per good bit (EPG) is considered as an energy efficiency metric. We optimize the secondary user\\'s transmission power and media access frame length to minimize the EPG metric. We protect the primary user transmission via an outage probability constraint. The non-convex targeted problem is optimized by utilizing the generalized convexity theory and verifying the strictly pseudo-convex structure of the problem. Analytical results of the optimal power and frame length are derived. We also used these results in proposing an algorithm, which guarantees the existence of a global optimal solution. Selected numerical results show the improvement of the proposed system compared to other systems. © 2016 IEEE.

FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES

Science.gov (United States)

This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...

Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.

Science.gov (United States)

Velichkin, Vladimir A.; Zavyalov, Vladimir A.

2018-03-01

This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.

More efficient optimization of long-term water supply portfolios

Science.gov (United States)

Kirsch, Brian R.; Characklis, Gregory W.; Dillard, Karen E. M.; Kelley, C. T.

2009-03-01

The use of temporary transfers, such as options and leases, has grown as utilities attempt to meet increases in demand while reducing dependence on the expansion of costly infrastructure capacity (e.g., reservoirs). Earlier work has been done to construct optimal portfolios comprising firm capacity and transfers, using decision rules that determine the timing and volume of transfers. However, such work has only focused on the short-term (e.g., 1-year scenarios), which limits the utility of these planning efforts. Developing multiyear portfolios can lead to the exploration of a wider range of alternatives but also increases the computational burden. This work utilizes a coupled hydrologic-economic model to simulate the long-term performance of a city's water supply portfolio. This stochastic model is linked with an optimization search algorithm that is designed to handle the high-frequency, low-amplitude noise inherent in many simulations, particularly those involving expected values. This noise is detrimental to the accuracy and precision of the optimized solution and has traditionally been controlled by investing greater computational effort in the simulation. However, the increased computational effort can be substantial. This work describes the integration of a variance reduction technique (control variate method) within the simulation/optimization as a means of more efficiently identifying minimum cost portfolios. Random variation in model output (i.e., noise) is moderated using knowledge of random variations in stochastic input variables (e.g., reservoir inflows, demand), thereby reducing the computing time by 50% or more. Using these efficiency gains, water supply portfolios are evaluated over a 10-year period in order to assess their ability to reduce costs and adapt to demand growth, while still meeting reliability goals. As a part of the evaluation, several multiyear option contract structures are explored and compared.

Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems.

Science.gov (United States)

Herrero, Mario; Havlík, Petr; Valin, Hugo; Notenbaert, An; Rufino, Mariana C; Thornton, Philip K; Blümmel, Michael; Weiss, Franz; Grace, Delia; Obersteiner, Michael

2013-12-24

We present a unique, biologically consistent, spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs). The dataset contains over 50 new global maps containing high-resolution information for understanding the multiple roles (biophysical, economic, social) that livestock can play in different parts of the world. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system.

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.

Modeling Vertical Flow Treatment Wetland Hydraulics to Optimize Treatment Efficiency

Science.gov (United States)

2011-03-24

be forced to flow in a 90 serpentine manner back and forth as it moves upward through the wetland (think waiting in line at Disneyland ). This...Flow Treatment Wetland Hydraulics to Optimize Treatment Efficiency 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR

18.4%-Efficient Heterojunction Si Solar Cells Using Optimized ITO/Top Electrode.

Science.gov (United States)

Kim, Namwoo; Um, Han-Don; Choi, Inwoo; Kim, Ka-Hyun; Seo, Kwanyong

2016-05-11

We optimize the thickness of a transparent conducting oxide (TCO) layer, and apply a microscale mesh-pattern metal electrode for high-efficiency a-Si/c-Si heterojunction solar cells. A solar cell equipped with the proposed microgrid metal electrode demonstrates a high short-circuit current density (JSC) of 40.1 mA/cm(2), and achieves a high efficiency of 18.4% with an open-circuit voltage (VOC) of 618 mV and a fill factor (FF) of 74.1% as result of the shortened carrier path length and the decreased electrode area of the microgrid metal electrode. Furthermore, by optimizing the process sequence for electrode formation, we are able to effectively restore the reduction in VOC that occurs during the microgrid metal electrode formation process. This work is expected to become a fundamental study that can effectively improve current loss in a-Si/c-Si heterojunction solar cells through the optimization of transparent and metal electrodes.

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Directory of Open Access Journals (Sweden)

Ibidun Christiana Obagbuwa

2016-09-01

Full Text Available The Cockroach Swarm Optimization (CSO algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP, which is considered to be an NP-hard Combinatorial Optimization Problem (COP. A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO algorithm on TSP were compared to other meta-heuristic algorithms.

Improvement of characteristic statistic algorithm and its application on equilibrium cycle reloading optimization

International Nuclear Information System (INIS)

Hu, Y.; Liu, Z.; Shi, X.; Wang, B.

2006-01-01

A brief introduction of characteristic statistic algorithm (CSA) is given in the paper, which is a new global optimization algorithm to solve the problem of PWR in-core fuel management optimization. CSA is modified by the adoption of back propagation neural network and fast local adjustment. Then the modified CSA is applied to PWR Equilibrium Cycle Reloading Optimization, and the corresponding optimization code of CSA-DYW is developed. CSA-DYW is used to optimize the equilibrium cycle of 18 month reloading of Daya bay nuclear plant Unit 1 reactor. The results show that CSA-DYW has high efficiency and good global performance on PWR Equilibrium Cycle Reloading Optimization. (authors)

New drilling optimization technologies make drilling more efficient

Energy Technology Data Exchange (ETDEWEB)

Chen, D.C.-K. [Halliburton Energy Services, Calgary, AB (Canada). Sperry Division

2004-07-01

Several new technologies have been adopted by the upstream petroleum industry in the past two decades in order to optimize drilling operations and improve drilling efficiency. Since financial returns from an oil and gas investment strongly depend on drilling costs, it is important to reduce non-productive time due to stuck pipes, lost circulation, hole cleaning and well bore stability problems. The most notable new technologies are the use of computer-based instrumentation and data acquisition systems, integrated rig site systems and networks, and Measurement-While-Drilling and Logging-While-Drilling (MWD/LWD) systems. Drilling optimization should include solutions for drillstring integrity, hydraulics management and wellbore integrity. New drilling optimization methods emphasize information management and real-time decision making. A recent study for drilling in shallow water in the Gulf of Mexico demonstrates that trouble time accounts for 25 per cent of rig time. This translates to about $1.5 MM U.S. per well. A reduction in trouble time could result in significant cost savings for the industry. This paper presents a case study on vibration prevention to demonstrate how the drilling industry has benefited from new technologies. 13 refs., 10 figs.

Efficient Topological Localization Using Global and Local Feature Matching

Directory of Open Access Journals (Sweden)

Junqiu Wang

2013-03-01

Full Text Available We present an efficient vision-based global topological localization approach in which different image features are used in a coarse-to-fine matching framework. Orientation Adjacency Coherence Histogram (OACH, a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. The computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. The matching of local features is improved by using approximate nearest neighbor searching technique. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. This work has also been compared with previous works. The comparison results show that our approach has better performance with higher correct ratio and lower computational complexity.

Enhancement of the efficiency of the Open Cycle Phillips Optimized Cascade LNG process

International Nuclear Information System (INIS)

Fahmy, M.F.M.; Nabih, H.I.; El-Nigeily, M.

2016-01-01

Highlights: • Expanders replaced JT valves in the Phillips Optimized Cascade liquefaction process. • Improvement in plant liquefaction efficiency was evaluated in presence of expanders. • Comparison of the different optimum cases for the liquefaction process was presented. - Abstract: This study aims to improve the performance of the Open Cycle Phillips Optimized Cascade Process for the production of liquefied natural gas (LNG) through the replacement of Joule–Thomson (JT) valves by expanders. The expander has a higher thermodynamic efficiency than the JT valve. Moreover, the produced shaft power from the expander is integrated into the process. The study is conducted using the Aspen HYSYS-V7 simulation software for simulation of the Open Cycle Phillips Optimized Cascade Process having the JT valves. Simulation of several proposed cases in which expanders are used instead of JT valves at different locations in the process as at the propane cycle, ethylene cycle, methane cycle and the upstream of the heavies removal column is conducted. The optimum cases clearly indicate that expanders not only produce power, but also offer significant improvements in the process performance as shown by the total plant power consumption, LNG production, thermal efficiency, plant specific power and CO_2 emissions reduction. Results also reveal that replacing JT valves by expanders in the methane cycle has a dominating influence on all performance criteria and hence, can be considered as the main key contributor affecting the Phillips Optimized Cascade Process leading to a notable enhancement in its efficiency. This replacement of JT valves by liquid expanders at different locations of the methane cycle encounters power savings in the range of 4.92–5.72%, plant thermal efficiency of 92.64–92.97% and an increase in LNG production of 5.77–7.04%. Moreover, applying liquid expanders at the determined optimum cases for the different cycles, improves process performance and

Global efficiency of local immunization on complex networks.

Science.gov (United States)

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J

2013-01-01

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.

Global efficiency of local immunization on complex networks

Science.gov (United States)

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.

Fast optimization of binary clusters using a novel dynamic lattice searching method

International Nuclear Information System (INIS)

Wu, Xia; Cheng, Wen

2014-01-01

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd) 79 clusters with DFT-fit parameters of Gupta potential

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.

Multiobjective optimal design of runner blade using efficiency and draft tube pulsation criteria

International Nuclear Information System (INIS)

Pilev, I M; Sotnikov, A A; Rigin, V E; Semenova, A V; Cherny, S G; Chirkov, D V; Bannikov, D V; Skorospelov, V A

2012-01-01

In the present work new criteria of optimal design method for turbine runner [1] are proposed. Firstly, based on the efficient method which couples direct simulation of 3D turbulent flow and engineering semi empirical formulas, the combined method is built for hydraulic energy losses estimation in the whole turbine water passage and the efficiency criterion is formulated. Secondly, the criterion of dynamic loads minimization is developed for those caused by vortex rope precession downstream of the runner. This criterion is based on the finding that the monotonic increase of meridional velocity component in the direction to runner hub, downstream of its blades, provides for decreasing the intensity of vortex rope and thereafter, minimization of pressure pulsation amplitude. The developed algorithm was applied to optimal design of 640 MW Francis turbine runner. It can ensure high efficiency at best efficiency operating point as well as diminished pressure pulsations at full load regime.

Inversion of self-potential anomalies caused by simple-geometry bodies using global optimization algorithms

International Nuclear Information System (INIS)

Göktürkler, G; Balkaya, Ç

2012-01-01

Three naturally inspired meta-heuristic algorithms—the genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO)—were used to invert some of the self-potential (SP) anomalies originated by some polarized bodies with simple geometries. Both synthetic and field data sets were considered. The tests with the synthetic data comprised of the solutions with both noise-free and noisy data; in the tests with the field data some SP anomalies observed over a copper belt (India), graphite deposits (Germany) and metallic sulfide (Turkey) were inverted. The model parameters included the electric dipole moment, polarization angle, depth, shape factor and origin of the anomaly. The estimated parameters were compared with those from previous studies using various optimization algorithms, mainly least-squares approaches, on the same data sets. During the test studies the solutions by GA, PSO and SA were characterized as being consistent with each other; a good starting model was not a requirement to reach the global minimum. It can be concluded that the global optimization algorithms considered in this study were able to yield compatible solutions with those from widely used local optimization algorithms. (paper)

Optimization of the n-type HPGe detector parameters to theoretical determination of efficiency curves

International Nuclear Information System (INIS)

Rodriguez-Rodriguez, A.; Correa-Alfonso, C.M.; Lopez-Pino, N.; Padilla-Cabal, F.; D'Alessandro, K.; Corrales, Y.; Garcia-Alvarez, J. A.; Perez-Mellor, A.; Baly-Gil, L.; Machado, A.

2011-01-01

A highly detailed characterization of a 130 cm 3 n-type HPGe detector, employed in low - background gamma spectrometry measurements, was done. Precise measured data and several Monte Carlo (MC) calculations have been combined to optimize the detector parameters. HPGe crystal location inside the Aluminum end-cap as well as its dimensions, including the borehole radius and height, were determined from frontal and lateral scans. Additionally, X-ray radiography and Computed Axial Tomography (CT) studies were carried out to complement the information about detector features. Using seven calibrated point sources ( 241 Am, 133 Ba, 57,60 Co, 137 Cs, 22 Na and 152 Eu), photo-peak efficiency curves at three different source - detector distances (SDD) were obtained. Taking into account the experimental values, an optimization procedure by means of MC simulations (MCNPX 2.6 code) were performed. MC efficiency curves were calculated specifying the optimized detector parameters in the MCNPX input files. Efficiency calculation results agree with empirical data, showing relative deviations lesser 10%. (Author)

A concept for global optimization of topology design problems

DEFF Research Database (Denmark)

Stolpe, Mathias; Achtziger, Wolfgang; Kawamoto, Atsushi

2006-01-01

We present a concept for solving topology design problems to proven global optimality. We propose that the problems are modeled using the approach of simultaneous analysis and design with discrete design variables and solved with convergent branch and bound type methods. This concept is illustrated...... on two applications. The first application is the design of stiff truss structures where the bar areas are chosen from a finite set of available areas. The second considered application is simultaneous topology and geometry design of planar articulated mechanisms. For each application we outline...

Between Efficiency, Capability and Recognition: Competing Epistemes in Global Governance Reforms

Science.gov (United States)

Chan, Jennifer

2007-01-01

This article examines global governance reforms as a site of contestation between three different "truths"/epistemes (the market, human rights principles, and cultural identity) in terms of the competing principles of efficiency, capability, and recognition. Nancy Fraser's conceptions of participation parity and a dialogical approach of…

Global warming and carbon taxation. Optimal policy and the role of administration costs

International Nuclear Information System (INIS)

Williams, M.

1995-01-01

This paper develops a model relating CO 2 emissions to atmosphere concentrations, global temperature change and economic damages. For a variety of parameter assumptions, the model provides estimates of the marginal cost of emissions in various years. The optimal carbon tax is a function of the marginal emission cost and the costs of administering the tax. This paper demonstrates that under any reasonable assumptions, the optimal carbon tax is zero for at least several decades. (author)

Improving adsorption dryer energy efficiency by simultaneous optimization and heat integration

NARCIS (Netherlands)

Atuonwu, J.C.; Straten, G. van; Deventer, H.C. van; Boxtel, A.J.B. van

2011-01-01

Conventionally, energy-saving techniques in drying technology are sequential in nature. First, the dryer is optimized without heat recovery and then, based on the obtained process conditions, heat recovery possibilities are explored. This work presents a methodology for energy-efficient adsorption

Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems

Energy Technology Data Exchange (ETDEWEB)

NONE

2011-07-01

This publication is the first global analysis of energy consumption and energy efficiency potential of EMDS (electric motor- driven system). The electric motors and systems they drive are the largest single electricity end use, accounting for more than 40% of global electricity consumption. Huge energy efficiency potential was found untapped in EMDS - around 25% of EMDS electricity use could be saved cost-effectively, reducing total global electricity demand by about 10%. However, the energy efficiency of EMDS has been relatively neglected in comparison with other sustainable energy opportunities. It is crucial to scale up the operations and resources committed to realizing the vast savings potential of optimized EMDS. This paper proposes a comprehensive package of policy recommendations to help governments realize the potential for energy savings in EMDS.

Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

KAUST Repository

Liang, Faming; Cheng, Yichen; Lin, Guang

2014-01-01

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

A logical approach to optimize the nanostructured lipid carrier system of irinotecan: efficient hybrid design methodology

International Nuclear Information System (INIS)

Negi, Lalit Mohan; Talegaonkar, Sushama; Jaggi, Manu

2013-01-01

Development of an effective formulation involves careful optimization of a number of excipient and process variables. Sometimes the number of variables is so large that even the most efficient optimization designs require a very large number of trials which put stress on costs as well as time. A creative combination of a number of design methods leads to a smaller number of trials. This study was aimed at the development of nanostructured lipid carriers (NLCs) by using a combination of different optimization methods. A total of 11 variables were first screened using the Plackett–Burman design for their effects on formulation characteristics like size and entrapment efficiency. Four out of 11 variables were found to have insignificant effects on the formulation parameters and hence were screened out. Out of the remaining seven variables, four (concentration of tween-80, lecithin, sodium taurocholate, and total lipid) were found to have significant effects on the size of the particles while the other three (phase ratio, drug to lipid ratio, and sonication time) had a higher influence on the entrapment efficiency. The first four variables were optimized for their effect on size using the Taguchi L9 orthogonal array. The optimized values of the surfactants and lipids were kept constant for the next stage, where the sonication time, phase ratio, and drug:lipid ratio were varied using the Box–Behnken design response surface method to optimize the entrapment efficiency. Finally, by performing only 38 trials, we have optimized 11 variables for the development of NLCs with a size of 143.52 ± 1.2 nm, zeta potential of −32.6 ± 0.54 mV, and 98.22 ± 2.06% entrapment efficiency. (paper)

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

Science.gov (United States)

Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang

2016-11-01

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types. © 2016, National Ground Water Association.

Networks that optimize a trade-off between efficiency and dynamical resilience

International Nuclear Information System (INIS)

Brede, Markus; Vries, Bert J.M. de

2009-01-01

In this Letter we study networks that have been optimized to realize a trade-off between communication efficiency and dynamical resilience. While the first is related to the average shortest pathlength, we argue that the second can be measured by the largest eigenvalue of the adjacency matrix of the network. Best efficiency is realized in star-like configurations, while enhanced resilience is related to the avoidance of short loops and degree homogeneity. Thus crucially, very efficient networks are not resilient while very resilient networks lack in efficiency. Networks that realize a trade-off between both limiting cases exhibit core-periphery structures, where the average degree of core nodes decreases but core size increases as the weight is gradually shifted from a strong requirement for efficiency and limited resilience towards a smaller requirement for efficiency and a strong demand for resilience. We argue that both, efficiency and resilience are important requirements for network design and highlight how networks can be constructed that allow for both.

Study on the mechanism and efficiency of simulated annealing using an LP optimization benchmark problem - 113

International Nuclear Information System (INIS)

Qianqian, Li; Xiaofeng, Jiang; Shaohong, Zhang

2010-01-01

Simulated Annealing Algorithm (SAA) for solving combinatorial optimization problems is a popular method for loading pattern optimization. The main purpose of this paper is to understand the underlying search mechanism of SAA and to study its efficiency. In this study, a general SAA that employs random pair exchange of fuel assemblies to search for the optimum fuel Loading Pattern (LP) is applied to an exhaustively searched LP optimization benchmark problem. All the possible LPs of the benchmark problem have been enumerated and evaluated via the use of the very fast and accurate Hybrid Harmonics and Linear Perturbation (HHLP) method, such that the mechanism of SA for LP optimization can be explicitly analyzed and its search efficiency evaluated. The generic core geometry itself dictates that only a small number LPs can be generated by performing random single pair exchanges and that the LPs are necessarily mostly similar to the initial LP. This phase space effect turns out to be the basic mechanism in SAA that can explain its efficiency and good local search ability. A measure of search efficiency is introduced which shows that the stochastic nature of SAA greatly influences the variability of its search efficiency. It is also found that using fuel assembly k-infinity distribution as a technique to filter the LPs can significantly enhance the SAA search efficiency. (authors)

Optimal conditions to use Pfu exo(-) DNA polymerase for highly efficient ligation-mediated polymerase chain reaction protocols.

Science.gov (United States)

Angers, M; Cloutier, J F; Castonguay, A; Drouin, R

2001-08-15

Ligation-Mediated Polymerase Chain Reaction (LMPCR) is the most sensitive sequencing technique available to map single-stranded DNA breaks at the nucleotide level of resolution using genomic DNA. LMPCR has been adapted to map DNA damage and reveal DNA-protein interactions inside living cells. However, the sequence context (GC content), the global break frequency and the current combination of DNA polymerases used in LMPCR affect the quality of the results. In this study, we developed and optimized an LMPCR protocol adapted for Pyrococcus furiosus exo(-) DNA polymerase (Pfu exo(-)). The relative efficiency of Pfu exo(-) was compared to T7-modified DNA polymerase (Sequenase 2.0) at the primer extension step and to Thermus aquaticus DNA polymerase (Taq) at the PCR amplification step of LMPCR. At all break frequencies tested, Pfu exo(-) proved to be more efficient than Sequenase 2.0. During both primer extension and PCR amplification steps, the ratio of DNA molecules per unit of DNA polymerase was the main determinant of the efficiency of Pfu exo(-), while the efficiency of Taq was less affected by this ratio. Substitution of NaCl for KCl in the PCR reaction buffer of Taq strikingly improved the efficiency of the DNA polymerase. Pfu exo(-) was clearly more efficient than Taq to specifically amplify extremely GC-rich genomic DNA sequences. Our results show that a combination of Pfu exo(-) at the primer extension step and Taq at the PCR amplification step is ideal for in vivo DNA analysis and DNA damage mapping using LMPCR.

Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems

Science.gov (United States)

Herrero, Mario; Havlík, Petr; Valin, Hugo; Notenbaert, An; Rufino, Mariana C.; Thornton, Philip K.; Blümmel, Michael; Weiss, Franz; Grace, Delia; Obersteiner, Michael

2013-01-01

We present a unique, biologically consistent, spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs). The dataset contains over 50 new global maps containing high-resolution information for understanding the multiple roles (biophysical, economic, social) that livestock can play in different parts of the world. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system. PMID:24344273

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

Hao, Zhifeng; Yuan, Ganzhao; Ghanem, Bernard

2013-01-01

Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

Hao, Zhifeng

2013-10-03

Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

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.)

Geometric Design of Scalable Forward Scatterers for Optimally Efficient Solar Transformers.

Science.gov (United States)

Kim, Hye-Na; Vahidinia, Sanaz; Holt, Amanda L; Sweeney, Alison M; Yang, Shu

2017-11-01

It will be ideal to deliver equal, optimally efficient "doses" of sunlight to all cells in a photobioreactor system, while simultaneously utilizing the entire solar resource. Backed by the numerical scattering simulation and optimization, here, the design, synthesis, and characterization of the synthetic iridocytes that recapitulated the salient forward-scattering behavior of the Tridacnid clam system are reported, which presents the first geometric solution to allow narrow, precise forward redistribution of flux, utilizing the solar resource at the maximum quantum efficiency possible in living cells. The synthetic iridocytes are composed of silica nanoparticles in microspheres embedded in gelatin, both are low refractive index materials and inexpensive. They show wavelength selectivity, have little loss (the back-scattering intensity is reduced to less than ≈0.01% of the forward-scattered intensity), and narrow forward scattering cone similar to giant clams. Moreover, by comparing experiments and theoretical calculation, it is confirmed that the nonuniformity of the scatter sizes is a "feature not a bug" of the design, allowing for efficient, forward redistribution of solar flux in a micrometer-scaled paradigm. This method is environmentally benign, inexpensive, and scalable to produce optical components that will find uses in efficiency-limited solar conversion technologies, heat sinks, and biofuel production. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Group leaders optimization algorithm

Science.gov (United States)

Daskin, Anmer; Kais, Sabre

2011-03-01

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.

Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

KAUST Repository

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.

Model-data fusion across ecosystems: from multisite optimizations to global simulations

Science.gov (United States)

Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.

2014-11-01

This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP - gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index

Global optimization methods for the aerodynamic shape design of transonic cascades

International Nuclear Information System (INIS)

Mengistu, T.; Ghaly, W.

2003-01-01

Two global optimization algorithms, namely Genetic Algorithm (GA) and Simulated Annealing (SA), have been applied to the aerodynamic shape optimization of transonic cascades; the objective being the redesign of an existing turbomachine airfoil to improve its performance by minimizing the total pressure loss while satisfying a number of constraints. This is accomplished by modifying the blade camber line; keeping the same blade thickness distribution, mass flow rate and the same flow turning. The objective is calculated based on an Euler solver and the blade camber line is represented with non-uniform rational B-splines (NURBS). The SA and GA methods were first assessed for known test functions and their performance in optimizing the blade shape for minimum loss is then demonstrated on a transonic turbine cascade where it is shown to produce a significant reduction in total pressure loss by eliminating the passage shock. (author)

Energy efficiency as a unifying principle for human, environmental, and global health

Science.gov (United States)

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

Globally-Optimized Local Pseudopotentials for (Orbital-Free) Density Functional Theory Simulations of Liquids and Solids.

Science.gov (United States)

Del Rio, Beatriz G; Dieterich, Johannes M; Carter, Emily A

2017-08-08

The accuracy of local pseudopotentials (LPSs) is one of two major determinants of the fidelity of orbital-free density functional theory (OFDFT) simulations. We present a global optimization strategy for LPSs that enables OFDFT to reproduce solid and liquid properties obtained from Kohn-Sham DFT. Our optimization strategy can fit arbitrary properties from both solid and liquid phases, so the resulting globally optimized local pseudopotentials (goLPSs) can be used in solid and/or liquid-phase simulations depending on the fitting process. We show three test cases proving that we can (1) improve solid properties compared to our previous bulk-derived local pseudopotential generation scheme; (2) refine predicted liquid and solid properties by adding force matching data; and (3) generate a from-scratch, accurate goLPS from the local channel of a non-local pseudopotential. The proposed scheme therefore serves as a full and improved LPS construction protocol.

Toward efficient optimization of wind farm layouts: Utilizing exact gradient information

International Nuclear Information System (INIS)

Guirguis, David; Romero, David A.; Amon, Cristina H.

2016-01-01

Highlights: • A mathematical programming approach is proposed to solve the WFLO problem. • Differentiable mathematical models are developed to handle land-use constraints. • Test cases with significant land-use constraints are solved efficiently. • The proposed approach outperforms genetic algorithm. - Abstract: The Wind Farm Layout Optimization (WFLO) problem has attracted a lot of attention from researchers and industry practitioners, as it has been proven that better placement of wind turbines can increase the overall efficiency and the total revenue of a wind farm. Although the engineering wake models are commonly used for layout optimization, the literature seems to have settled on using metaheuristics and stochastic optimization methods. In the present study, we show the effectiveness of non-linear mathematical programming in solving continuous-variable WFLO problems by utilizing exact gradient information of the problem’s objective and constraints. Moreover, mathematical models for handling land-use constraints are developed to solve highly constrained practical problems. For demonstration purposes, the results were compared with those obtained by a genetic algorithm, using a set of test cases that have been frequently used in the WFLO literature. Additional test cases with higher dimensionality, significant land-availability constraints and higher wind farm turbine densities (i.e., turbines per square kilometer) are devised and solved to show the merits of the proposed approach. Our results show the superiority of mathematical programing in solving this problem, as evidenced by the resulting wind farm efficiency and the computational cost required to obtain the solutions.

Implementation and verification of global optimization benchmark problems

Science.gov (United States)

Posypkin, Mikhail; Usov, Alexander

2017-12-01

The paper considers the implementation and verification of a test suite containing 150 benchmarks for global deterministic box-constrained optimization. A C++ library for describing standard mathematical expressions was developed for this purpose. The library automate the process of generating the value of a function and its' gradient at a given point and the interval estimates of a function and its' gradient on a given box using a single description. Based on this functionality, we have developed a collection of tests for an automatic verification of the proposed benchmarks. The verification has shown that literary sources contain mistakes in the benchmarks description. The library and the test suite are available for download and can be used freely.

Efficiency Optimization by Considering the High Voltage Flyback Transformer Parasitics using an Automatic Winding Layout Technique

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...

Opportunities of Optimization in Administrative Structures for Efficient Management

Directory of Open Access Journals (Sweden)

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.

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization.

Science.gov (United States)

Modiri, Arezoo; Gu, Xuejun; Hagan, Aaron M; Sawant, Amit

2017-05-01

Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used. The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.

Science.gov (United States)

Kim, Seongho; Li, Lang

2014-02-01

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Fast globally optimal segmentation of cells in fluorescence microscopy images.

Science.gov (United States)

Bergeest, Jan-Philip; Rohr, Karl

2011-01-01

Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.

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 indus...

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

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.

Efficiency and cost optimization of a regenerative Organic Rankine Cycle power plant through the multi-objective approach

International Nuclear Information System (INIS)

Gimelli, A.; Luongo, A.; Muccillo, M.

2017-01-01

Highlights: • Multi-objective optimization method for ORC design has been addressed. • Trade-off between electric efficiency and overall heat exchangers area is evaluated. • The heat exchangers area was used as objective function to minimize the plant cost. • MDM was considered as organic working fluid for the thermodynamic cycle. • Electric efficiency: 14.1–18.9%. Overall heat exchangers area: 446–1079 m 2 . - Abstract: Multi-objective optimization could be, in the industrial sector, a fundamental strategic approach for defining the target design specifications and operating parameters of new competitive products for the market, especially in renewable energy and energy savings fields. Vector optimization mostly enabled the determination of a set of optimal solutions characterized by different costs, sizes, efficiencies and other key features. The designer can subsequently select the solution with the best compromise between the objective functions for the specific application and constraints. In this paper, a multi-objective optimization problem addressing an Organic Rankine Cycle system is solved with consideration for the electric efficiency and overall heat exchangers area as quantities that should be optimized. In fact, considering that the overall capital cost of the ORC system is dominated by the cost of the heat exchangers rather than that of the pump and turbine, this area is related to the cost of the plant and so it was used to indirectly optimize the economic system performance. For this reason, although cost data have not been used, the heat exchangers area was used as a second objective function to minimize the plant cost. Pareto optimal solutions highlighted a trade-off between the two conflicting objective functions. Octamethyltrisiloxane (MDM) was considered organic working fluid, while the following input parameters were used as decision variables: minimum and maximum pressure of the thermodynamic cycle; superheating and subcooling

Optimized systems for energy efficient optical tweezing

Science.gov (United States)

Kampmann, R.; Kleindienst, R.; Grewe, A.; Bürger, Elisabeth; Oeder, A.; Sinzinger, S.

2013-03-01

Compared to conventional optics like singlet lenses or even microscope objectives advanced optical designs help to develop properties specifically useful for efficient optical tweezers. We present an optical setup providing a customized intensity distribution optimized with respect to large trapping forces. The optical design concept combines a refractive double axicon with a reflective parabolic focusing mirror. The axicon arrangement creates an annular field distribution and thus clears space for additional integrated observation optics in the center of the system. Finally the beam is focused to the desired intensity distribution by a parabolic ring mirror. The compact realization of the system potentially opens new fields of applications for optical tweezers such as in production industries and micro-nano assembly.

Constructing IGA-suitable planar parameterization from complex CAD boundary by domain partition and global/local optimization

Science.gov (United States)

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.

Optimized Energy Efficiency and Spectral Efficiency Resource Allocation Strategies for Phantom Cellular Networks

KAUST Repository

Abdelhady, Amr, M.; Amin, Osama; Alouini, Mohamed-Slim

2016-01-01

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.

Optimized Energy Efficiency and Spectral Efficiency Resource Allocation Strategies for Phantom Cellular Networks

KAUST Repository

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.

A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy

Science.gov (United States)

Jahangoshai Rezaee, Mustafa; Yousefi, Samuel; Hayati, Jamileh

2017-06-01

Supplier selection and allocation of optimal order quantity are two of the most important processes in closed-loop supply chain (CLSC) and reverse logistic (RL). So that providing high quality raw material is considered as a basic requirement for a manufacturer to produce popular products, as well as achieve more market shares. On the other hand, considering the existence of competitive environment, suppliers have to offer customers incentives like discounts and enhance the quality of their products in a competition with other manufacturers. Therefore, in this study, a model is presented for CLSC optimization, efficient supplier selection, as well as orders allocation considering quantity discount policy. It is modeled using multi-objective programming based on the integrated simultaneous data envelopment analysis-Nash bargaining game. In this study, maximizing profit and efficiency and minimizing defective and functions of delivery delay rate are taken into accounts. Beside supplier selection, the suggested model selects refurbishing sites, as well as determining the number of products and parts in each network's sector. The suggested model's solution is carried out using global criteria method. Furthermore, based on related studies, a numerical example is examined to validate it.

Quantum separability and entanglement detection via entanglement-witness search and global optimization

International Nuclear Information System (INIS)

Ioannou, Lawrence M.; Travaglione, Benjamin C.

2006-01-01

We focus on determining the separability of an unknown bipartite quantum state ρ by invoking a sufficiently large subset of all possible entanglement witnesses given the expected value of each element of a set of mutually orthogonal observables. We review the concept of an entanglement witness from the geometrical point of view and use this geometry to show that the set of separable states is not a polytope and to characterize the class of entanglement witnesses (observables) that detect entangled states on opposite sides of the set of separable states. All this serves to motivate a classical algorithm which, given the expected values of a subset of an orthogonal basis of observables of an otherwise unknown quantum state, searches for an entanglement witness in the span of the subset of observables. The idea of such an algorithm, which is an efficient reduction of the quantum separability problem to a global optimization problem, was introduced by [Ioannou et al., Phys. Rev. A 70, 060303(R)], where it was shown to be an improvement on the naive approach for the quantum separability problem (exhaustive search for a decomposition of the given state into a convex combination of separable states). The last section of the paper discusses in more generality such algorithms, which, in our case, assume a subroutine that computes the global maximum of a real function of several variables. Despite this, we anticipate that such algorithms will perform sufficiently well on small instances that they will render a feasible test for separability in some cases of interest (e.g., in 3x3 dimensional systems)

A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

KAUST Repository

Fowkes, Jaroslav M.; Gould, Nicholas I. M.; Farmer, Chris L.

2012-01-01

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

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

Energy Technology Data Exchange (ETDEWEB)

Gonzalez-Barrios, P.; Castro, M.; Pérez, O.; Vilaró, D.; Gutiérrez, L.

2017-07-01

Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

Optimization design of hydroturbine rotors according to the efficiency-strength criteria

Science.gov (United States)

Bannikov, D. V.; Yesipov, D. V.; Cherny, S. G.; Chirkov, D. V.

2010-12-01

The hydroturbine runner designing [1] is optimized by efficient methods for calculation of head loss in entire flow-through part of the turbine and deformation state of the blade. Energy losses are found at modelling of the spatial turbulent flow and engineering semi-empirical formulae. State of deformation is determined from the solution of the linear problem of elasticity for the isolated blade at hydrodynamic pressure with the method of boundary elements. With the use of the proposed system, the problem of the turbine runner design with the capacity of 640 MW providing the preset dependence of efficiency on the turbine work mode (efficiency criterion) is solved. The arising stresses do not exceed the critical value (strength criterion).

Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

Science.gov (United States)

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

Assisted closed-loop optimization of SSVEP-BCI efficiency

Directory of Open Access Journals (Sweden)

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.

Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization

KAUST Repository

Gunnels, John; Lee, Jon; Margulies, Susan

2010-01-01

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.

Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization

KAUST Repository

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.

Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

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.

Implementation and verification of global optimization benchmark problems

Directory of Open Access Journals (Sweden)

Posypkin Mikhail

2017-12-01

Full Text Available The paper considers the implementation and verification of a test suite containing 150 benchmarks for global deterministic box-constrained optimization. A C++ library for describing standard mathematical expressions was developed for this purpose. The library automate the process of generating the value of a function and its’ gradient at a given point and the interval estimates of a function and its’ gradient on a given box using a single description. Based on this functionality, we have developed a collection of tests for an automatic verification of the proposed benchmarks. The verification has shown that literary sources contain mistakes in the benchmarks description. The library and the test suite are available for download and can be used freely.

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

A tool for efficient, model-independent management optimization under uncertainty

Science.gov (United States)

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks.

Science.gov (United States)

Chen, Xi; Xu, Yixuan; Liu, Anfeng

2017-04-19

High transmission reliability, energy efficiency, and long lifetime are pivotal issues for wireless body area networks (WBANs. However, these performance metrics are not independent of each other, making it hard to obtain overall improvements through optimizing one single aspect. Therefore, a Cross Layer Design Optimal (CLDO) scheme is proposed to simultaneously optimize transmission reliability, energy efficiency, and lifetime of WBANs from several layers. Firstly, due to the fact that the transmission power of nodes directly influences the reliability of links, the optimized transmission power of different nodes is deduced, which is able to maximize energy efficiency in theory under the premise that requirements on delay and jitter are fulfilled. Secondly, a relay decision algorithm is proposed to choose optimized relay nodes. Using this algorithm, nodes will choose relay nodes that ensure a balance of network energy consumption, provided that all nodes transmit with optimized transmission power and the same packet size. Thirdly, the energy consumption of nodes is still unbalanced even with optimized transmission power because of their different locations in the topology of the network. In addition, packet size also has an impact on final performance metrics. Therefore, a synthesized cross layer method for optimization is proposed. With this method, the transmission power of nodes with more residual energy will be enhanced while suitable packet size is determined for different links in the network, leading to further improvements in the WBAN system. Both our comprehensive theoretical analysis and experimental results indicate that the performance of our proposed scheme is better than reported in previous studies. Relative to the relay selection and power control game (RSPCG) scheme, the CLDO scheme can enhance transmission reliability by more than 44.6% and prolong the lifetime by as much as 33.2%.

External costs in the global energy optimization models. A tool in favour of sustain ability

International Nuclear Information System (INIS)

Cabal Cuesta, H.

2007-01-01

The aim of this work is the analysis of the effects of the GHG external costs internalization in the energy systems. This may provide a useful tool to support decision makers to help reaching the energy systems sustain ability. External costs internalization has been carried out using two methods. First, CO 2 externalities of different power generation technologies have been internalized to evaluate their effects on the economic competitiveness of these present and future technologies. The other method consisted of analysing and optimizing the global energy system, from an economic and environmental point of view, using the global energy optimization model generator, TIMES, with a time horizon of 50 years. Finally, some scenarios regarding environmental and economic strategic measures have been analysed. (Author)

Analytic clock frequency selection for global DVFS

OpenAIRE

Gerards, Marco Egbertus Theodorus; Hurink, Johann L.; Holzenspies, P.K.F.; Kuper, Jan; Smit, Gerardus Johannes Maria

2014-01-01

Computers can reduce their power consumption by decreasing their speed using Dynamic Voltage and Frequency Scaling (DVFS). A form of DVFS for multicore processors is global DVFS, where the voltage and clock frequency is shared among all processor cores. Because global DVFS is efficient and cheap to implement, it is used in modern multicore processors like the IBM Power 7, ARM Cortex A9 and NVIDIA Tegra 2. This theory oriented paper discusses energy optimal DVFS algorithms for such processors....

Optimal Design of a High Efficiency LLC Resonant Converter with a Narrow Frequency Range for Voltage Regulation

Directory of Open Access Journals (Sweden)

Junhao Luo

2018-05-01

Full Text Available As a key factor in the design of a voltage-adjustable LLC resonant converter, frequency regulation range is very important to the optimization of magnetic components and efficiency improvement. This paper presents a novel optimal design method for LLC resonant converters, which can narrow the frequency variation range and ensure high efficiency under the premise of a required gain achievement. A simplified gain model was utilized to simplify the calculation and the expected efficiency was initially set as 96.5%. The restricted area of parameter optimization design can be obtained by taking the intersection of the gain requirement, the efficiency requirement, and three restrictions of ZVS (Zero Voltage Switch. The proposed method was verified by simulation and experiments of a 150 W prototype. The results show that the proposed method can achieve ZVS from full-load to no-load conditions and can reach 1.6 times the normalized voltage gain in the frequency variation range of 18 kHz with a peak efficiency of up to 96.3%. Moreover, the expected efficiency is adjustable, which means a converter with a higher efficiency can be designed. The proposed method can also be used for the design of large-power LLC resonant converters to obtain a wide output voltage range and higher efficiency.

Spatially Explicit Estimation of Optimal Light Use Efficiency for Improved Satellite Data Driven Ecosystem Productivity Modeling

Science.gov (United States)

Madani, N.; Kimball, J. S.; Running, S. W.

2014-12-01

Remote sensing based light use efficiency (LUE) models, including the MODIS (MODerate resolution Imaging Spectroradiometer) MOD17 algorithm are commonly used for regional estimation and monitoring of vegetation gross primary production (GPP) and photosynthetic carbon (CO2) uptake. A common model assumption is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed biome maximum light use efficiency parameter defines the maximum photosynthetic carbon conversion rate under prevailing climate conditions and is a large source of model uncertainty. Here, we used tower (FLUXNET) eddy covariance measurement based carbon flux data for estimating optimal LUE (LUEopt) over a North American domain. LUEopt was first estimated using tower observed daily carbon fluxes, meteorology and satellite (MODIS) observed fraction of photosynthetically active radiation (FPAR). LUEopt was then spatially interpolated over the domain using empirical models derived from independent geospatial data including global plant traits, surface soil moisture, terrain aspect, land cover type and percent tree cover. The derived LUEopt maps were then used as primary inputs to the MOD17 LUE algorithm for regional GPP estimation; these results were evaluated against tower observations and alternate MOD17 GPP estimates determined using Biome-specific LUEopt constants. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from a sparse North American tower network. Leaf nitrogen content and soil moisture are two important factors explaining LUEopt spatial variability. GPP estimated from spatially explicit LUEopt inputs shows significantly improved model accuracy against independent tower observations (R2 = 0.76; Mean RMSE plant trait information can explain spatial heterogeneity in LUEopt, leading to improved GPP estimates from satellite based LUE models.

Welding Robot Collision-Free Path Optimization

Directory of Open Access Journals (Sweden)

Xuewu Wang

2017-02-01

Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.

Analysis and Optimization of Building Energy Consumption

Science.gov (United States)

Chuah, Jun Wei

Energy is one of the most important resources required by modern human society. In 2010, energy expenditures represented 10% of global gross domestic product (GDP). By 2035, global energy consumption is expected to increase by more than 50% from current levels. The increased pace of global energy consumption leads to significant environmental and socioeconomic issues: (i) carbon emissions, from the burning of fossil fuels for energy, contribute to global warming, and (ii) increased energy expenditures lead to reduced standard of living. Efficient use of energy, through energy conservation measures, is an important step toward mitigating these effects. Residential and commercial buildings represent a prime target for energy conservation, comprising 21% of global energy consumption and 40% of the total energy consumption in the United States. This thesis describes techniques for the analysis and optimization of building energy consumption. The thesis focuses on building retrofits and building energy simulation as key areas in building energy optimization and analysis. The thesis first discusses and evaluates building-level renewable energy generation as a solution toward building energy optimization. The thesis next describes a novel heating system, called localized heating. Under localized heating, building occupants are heated individually by directed radiant heaters, resulting in a considerably reduced heated space and significant heating energy savings. To support localized heating, a minimally-intrusive indoor occupant positioning system is described. The thesis then discusses occupant-level sensing (OLS) as the next frontier in building energy optimization. OLS captures the exact environmental conditions faced by each building occupant, using sensors that are carried by all building occupants. The information provided by OLS enables fine-grained optimization for unprecedented levels of energy efficiency and occupant comfort. The thesis also describes a retrofit

Efficiency-optimized low-cost TDPAC spectrometer using a versatile routing/coincidence unit

International Nuclear Information System (INIS)

Renteria, M.; Bibiloni, A. G.; Darriba, G. N.; Errico, L. A.; Munoz, E. L.; Richard, D.; Runco, J.

2008-01-01

A highly efficient, reliable, and low-cost γ-γ TDPAC spectrometer, PACAr, optimized for 181 Hf-implanted low-activity samples, is presented. A versatile EPROM-based routing/coincidence unit was developed and implemented to be use with the memory-card-based multichannel analyzer hosted in a personal computer. The excellent energy resolution and very good overall resolution and efficiency of PACAr are analyzed and compare with advanced and already tested fast-fast and slow-fast PAC spectrometers.

Efficiency-optimized low-cost TDPAC spectrometer using a versatile routing/coincidence unit

Energy Technology Data Exchange (ETDEWEB)

Renteria, M., E-mail: renteria@fisica.unlp.edu.ar; Bibiloni, A. G.; Darriba, G. N.; Errico, L. A.; Munoz, E. L.; Richard, D.; Runco, J. [Universidad Nacional de La Plata, Departamento de Fisica, Facultad de Ciencias Exactas (Argentina)

2008-01-15

A highly efficient, reliable, and low-cost {gamma}-{gamma} TDPAC spectrometer, PACAr, optimized for {sup 181}Hf-implanted low-activity samples, is presented. A versatile EPROM-based routing/coincidence unit was developed and implemented to be use with the memory-card-based multichannel analyzer hosted in a personal computer. The excellent energy resolution and very good overall resolution and efficiency of PACAr are analyzed and compare with advanced and already tested fast-fast and slow-fast PAC spectrometers.

Global Optimization using Interval Analysis : Interval Optimization for Aerospace Applications

NARCIS (Netherlands)

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

DEGAS: Dynamic Exascale Global Address Space Programming Environments

Energy Technology Data Exchange (ETDEWEB)

Demmel, James [Univ. of California, Berkeley, CA (United States)

2018-02-23

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics.

Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness

International Nuclear Information System (INIS)

Du, Jiuyu; Chen, Jingfu; Song, Ziyou; Gao, Mingming; Ouyang, Minggao

2017-01-01

Energy management strategy and battery capacity are the primary factors for the energy efficiency of range-extended electric buses (REEBs). To improve the energy efficiency of REEBs developed by Tsinghua University, an optimal design method of global optimization-based strategy is investigated. It is real-time and adaptive to variable traction battery capacities of series REEBs. For simulation, the physical model of REEB and key components are established. The optimal strategy is first extracted by the power split ratio (PSR) from REEB simulation result with dynamic program (DP) algorithm. The power distribution map is obtained by series simulations for variable battery capacity options. The control law for developing optimal strategy are achieved by cluster regression for power distribution data. To verify the effect of the proposed energy management strategy, characteristics of powertrain, energy efficiency, operating cost, and computing time are ultimately analyzed. Simulation results show that the energy efficiency of the global optimization-based strategy presented in this paper is similar to that of the DP strategy. Therefore, the overall energy efficiency can be significantly improved compared with that of the CDCS strategy, and operating costs can be substantially reduced. The feasibility of candidate control strategies is thereby assessed via the employment of variable parameters. - Highlights: • Analysis method of powertrain energy efficiency and power distribution is proposed. • The power distribution rules of strategy with variable battery capacities are achieved. • The parametric method of proposed PSR-RB strategy is presented. • The energy efficiency of powertrain is analysis by flow analysis method. • The energy management strategy is global optimization-based and real-time.

Multistage and multiobjective formulations of globally optimal upgradable expansions for electric power distribution systems

Science.gov (United States)

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.

Conditional simulation for efficient global optimization

NARCIS (Netherlands)

Kleijnen, Jack P.C.; Mehdad, E.; Pasupathy, R.; Kim, S.-H.; Tolk, A.; Hill, R.; Kuhl, M.E.

2013-01-01

A classic Kriging or Gaussian process (GP) metamodel estimates the variance of its predictor by plugging-in the estimated GP (hyper)parameters; namely, the mean, variance, and covariances. The problem is that this predictor variance is biased. To solve this problem for deterministic simulations, we

Cost efficiency and optimal scale of electricity distribution firms in Taiwan: An application of metafrontier analysis

International Nuclear Information System (INIS)

Huang, Y.-J.; Chen, K.-H.; Yang, C.-H.

2010-01-01

This paper analyzes the cost efficiency and optimal scale of Taiwan's electricity distribution industry. Due to the substantial difference in network density, firms may differ widely in production technology. We employ the stochastic metafrontier approach to estimate the cost efficiency of 24 distribution units during the period 1997-2002. Empirical results find that the average cost efficiency is overestimated using the traditional stochastic frontier model, especially for low density regions. The average cost efficiency of the high density group is significantly higher than that of the low density group as it benefits from network economies. This study also calculates both short-term and long-term optimal scales of electricity distribution firms, lending policy implications for the deregulation of the electricity distribution industry.

Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

Science.gov (United States)

Poluyan, Sergey; Ershov, Nikolay

2018-02-01

In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

Optimization of power plants management structure based on the generalized criteria of the efficiency

Directory of Open Access Journals (Sweden)

Salov Aleksey

2017-01-01

Full Text Available In the article, the analysis of the operation of power plants in the conditions of economic restructuring to ensure successful entry into the market is carried out. The analysis of the five management structures, including current, typical structure and re-designed by the authors is presented. There are developed the partial efficiency criteria of the management structures that characterize the most important properties - the balance, integrity, controllability and stability. Local criteria of the analyzed structures do not allow to make a definite conclusion about the effectiveness of one of the structures analyzed, formulated global efficiency criterion. There is developed the global criterion of the comparative effectiveness of the management systems based on the DEA method (Data envelopment analysis, taking into account the complex of the proposed local criteria. The considered management structures are ranked based on the generalized criterion of efficiency.

Exploring trade-offs between VMAT dose quality and delivery efficiency using a network optimization approach

International Nuclear Information System (INIS)

Salari, Ehsan; Craft, David; Wala, Jeremiah

2012-01-01

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

Exploring trade-offs between VMAT dose quality and delivery efficiency using a network optimization approach.

Science.gov (United States)

Salari, Ehsan; Wala, Jeremiah; Craft, David

2012-09-07

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 merging

Negotiation and Optimality in an Economic Model of Global Climate Change

International Nuclear Information System (INIS)

Gottinger, H.

2000-03-01

The paper addresses the problem of governmental intervention in a multi-country regime of controlling global climate change. Using a simplified case of a two-country, two-sector general equilibrium model the paper shows that the global optimal time path of economic outputs and temperature will converge to a unique steady state provided that consumers care enough about the future. To answer a set of questions relating to 'what will happen if governments decide to correct the problem of global warming?' we study the equilibrium outcome in a bargaining game where two countries negotiate an agreement on future consumption and production plans for the purpose of correcting the problem of climate change. It is shown that the agreement arising from such a negotiation process achieves the best outcome and that it can be implemented in decentralised economies by a system of taxes, subsidies and transfers. By employing the recent advances in non-cooperative bargaining theory, the agreement between two countries is derived endogenously through a well-specified bargaining procedure

Negotiation and Optimality in an Economic Model of Global Climate Change

Energy Technology Data Exchange (ETDEWEB)

Gottinger, H. [International Institute for Environmental Economics and Management IIEEM, University of Maastricht, Maastricht (Netherlands)

2000-03-01

The paper addresses the problem of governmental intervention in a multi-country regime of controlling global climate change. Using a simplified case of a two-country, two-sector general equilibrium model the paper shows that the global optimal time path of economic outputs and temperature will converge to a unique steady state provided that consumers care enough about the future. To answer a set of questions relating to 'what will happen if governments decide to correct the problem of global warming?' we study the equilibrium outcome in a bargaining game where two countries negotiate an agreement on future consumption and production plans for the purpose of correcting the problem of climate change. It is shown that the agreement arising from such a negotiation process achieves the best outcome and that it can be implemented in decentralised economies by a system of taxes, subsidies and transfers. By employing the recent advances in non-cooperative bargaining theory, the agreement between two countries is derived endogenously through a well-specified bargaining procedure.

Bayesian optimization for computationally extensive probability distributions.

Science.gov (United States)

Tamura, Ryo; Hukushima, Koji

2018-01-01

An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

Performance Optimization of Alternative Lower Global Warming Potential Refrigerants in Mini-Split Room Air Conditioners

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.

The Impact of Digitalization on the Speed of Internationalization of Lean Global Startups

Directory of Open Access Journals (Sweden)

Michael Neubert

2018-05-01

Full Text Available Lean global startups need to internationalize early and fast. The digitalization of new foreign market development helps them to more efficiently identify new market opportunities in global markets. With this approach, they are saving resources while developing the most attractive markets. This article examines how lean global startups develop new foreign markets more rapidly due to digitalization. Thus, the aim is to understand the impact of digitalization on speed of internationalization of lean global startups. The study addresses a gap in the scholarly literature and a practical need to evaluate new foreign markets and business opportunities more quickly and more regularly and to understand what helps lean global startups react more quickly to opportunities and threats with respect to changing market attractiveness. Furthermore, it outlines why and how digitalization is important throughout the internationalization process. The research followed a multiple case-study design using different sources of evidence, including 73 interviews with senior managers of lean global startups. The findings reveal that digitalization allows lean global startups to increase decision-making efficiency and to optimize strategies and processes for evaluating international markets. The findings suggest that lean global startups can benefit from the use of digital technologies by applying a more efficient foreign market development process with regular reviews and a reduced workflow, by faster mediation between local market realities and strategic goals, by analyzing all foreign markets instead of just a sample of them, and by optimizing decision-making processes including the ability to make long-term, strategic decisions due to better market information.

Energy efficiency and SINR maximization beamformers for cognitive radio utilizing sensing information

KAUST Repository

Alabbasi, Abdulrahman

2014-06-01

In this paper we consider a cognitive radio multi-input multi-output environment in which we adapt our beamformer to maximize both energy efficiency and signal to interference plus noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with the sensing information to achieve an optimal energy efficient system. The proposed schemes maximize the energy efficiency and SINR metrics subject to cognitive radio and quality of service constraints. Since the optimization of energy efficiency problem is not a convex problem, we transform it into a standard semi-definite programming (SDP) form to guarantee a global optimal solution. Analytical solution is provided for one scheme, while the other scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

Optimization of output power and transmission efficiency of magnetically coupled resonance wireless power transfer system

Science.gov (United States)

Yan, Rongge; Guo, Xiaoting; Cao, Shaoqing; Zhang, Changgeng

2018-05-01

Magnetically coupled resonance (MCR) wireless power transfer (WPT) system is a promising technology in electric energy transmission. But, if its system parameters are designed unreasonably, output power and transmission efficiency will be low. Therefore, optimized parameters design of MCR WPT has important research value. In the MCR WPT system with designated coil structure, the main parameters affecting output power and transmission efficiency are the distance between the coils, the resonance frequency and the resistance of the load. Based on the established mathematical model and the differential evolution algorithm, the change of output power and transmission efficiency with parameters can be simulated. From the simulation results, it can be seen that output power and transmission efficiency of the two-coil MCR WPT system and four-coil one with designated coil structure are improved. The simulation results confirm the validity of the optimization method for MCR WPT system with designated coil structure.

Optimal network structure to induce the maximal small-world effect

International Nuclear Information System (INIS)

Zhang Zheng-Zhen; Xu Wen-Jun; Lin Jia-Ru; Zeng Shang-You

2014-01-01

In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number (> 500), the small rewiring probability (≍ 0.02) and the small average connection probability (< 0.1). Many previous research results support our results. (interdisciplinary physics and related areas of science and technology)

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

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.