WorldWideScience

Sample records for constrained optimization based

  1. Evolutionary constrained optimization

    CERN Document Server

    Deb, Kalyanmoy

    2015-01-01

    This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful...

  2. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

    Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

  3. Constrained optimization via simulation models for new product innovation

    Science.gov (United States)

    Pujowidianto, Nugroho A.

    2017-11-01

    We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.

  4. Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

    Science.gov (United States)

    Luo, Biao; Liu, Derong; Wu, Huai-Ning

    2018-06-01

    Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

  5. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

    Directory of Open Access Journals (Sweden)

    Vivek Patel

    2012-08-01

    Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

  6. A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Biwei Tang

    2016-01-01

    Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

  7. Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems.

    Science.gov (United States)

    Krohling, Renato A; Coelho, Leandro dos Santos

    2006-12-01

    In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

  8. Neuroevolutionary Constrained Optimization for Content Creation

    DEFF Research Database (Denmark)

    Liapis, Antonios; Yannakakis, Georgios N.; Togelius, Julian

    2011-01-01

    and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution...... and survival tasks and are also visually appealing....

  9. Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

    DEFF Research Database (Denmark)

    Wang, Yong; Cai, Zixing; Zhou, Yuren

    2009-01-01

    A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

  10. Constrained optimization of test intervals using a steady-state genetic algorithm

    International Nuclear Information System (INIS)

    Martorell, S.; Carlos, S.; Sanchez, A.; Serradell, V.

    2000-01-01

    There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Intervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effective and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burden is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in the constrained optimization of TIs based on risk and cost models at system level are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constraints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a great benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constrained optimization problems as it is concluded in this paper

  11. Trends in PDE constrained optimization

    CERN Document Server

    Benner, Peter; Engell, Sebastian; Griewank, Andreas; Harbrecht, Helmut; Hinze, Michael; Rannacher, Rolf; Ulbrich, Stefan

    2014-01-01

    Optimization problems subject to constraints governed by partial differential equations (PDEs) are among the most challenging problems in the context of industrial, economical and medical applications. Almost the entire range of problems in this field of research was studied and further explored as part of the Deutsche Forschungsgemeinschaft (DFG) priority program 1253 on “Optimization with Partial Differential Equations” from 2006 to 2013. The investigations were motivated by the fascinating potential applications and challenging mathematical problems that arise in the field of PDE constrained optimization. New analytic and algorithmic paradigms have been developed, implemented and validated in the context of real-world applications. In this special volume, contributions from more than fifteen German universities combine the results of this interdisciplinary program with a focus on applied mathematics.   The book is divided into five sections on “Constrained Optimization, Identification and Control”...

  12. Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

    International Nuclear Information System (INIS)

    Nguyen, Q H; Choi, S B

    2012-01-01

    This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel–Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given. (paper)

  13. Security constrained optimal power flow by modern optimization tools

    African Journals Online (AJOL)

    Security constrained optimal power flow by modern optimization tools. ... International Journal of Engineering, Science and Technology ... If you would like more information about how to print, save, and work with PDFs, Highwire Press ...

  14. Quasicanonical structure of optimal control in constrained discrete systems

    Science.gov (United States)

    Sieniutycz, S.

    2003-06-01

    This paper considers discrete processes governed by difference rather than differential equations for the state transformation. The basic question asked is if and when Hamiltonian canonical structures are possible in optimal discrete systems. Considering constrained discrete control, general optimization algorithms are derived that constitute suitable theoretical and computational tools when evaluating extremum properties of constrained physical models. The mathematical basis of the general theory is the Bellman method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage criterion which allows a variation of the terminal state that is otherwise fixed in the Bellman's method. Two relatively unknown, powerful optimization algorithms are obtained: an unconventional discrete formalism of optimization based on a Hamiltonian for multistage systems with unconstrained intervals of holdup time, and the time interval constrained extension of the formalism. These results are general; namely, one arrives at: the discrete canonical Hamilton equations, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory along with all basic results of variational calculus. Vast spectrum of applications of the theory is briefly discussed.

  15. Constrained Quadratic Programming and Neurodynamics-Based Solver for Energy Optimization of Biped Walking Robots

    Directory of Open Access Journals (Sweden)

    Liyang Wang

    2017-01-01

    Full Text Available The application of biped robots is always trapped by their high energy consumption. This paper makes a contribution by optimizing the joint torques to decrease the energy consumption without changing the biped gaits. In this work, a constrained quadratic programming (QP problem for energy optimization is formulated. A neurodynamics-based solver is presented to solve the QP problem. Differing from the existing literatures, the proposed neurodynamics-based energy optimization (NEO strategy minimizes the energy consumption and guarantees the following three important constraints simultaneously: (i the force-moment equilibrium equation of biped robots, (ii frictions applied by each leg on the ground to hold the biped robot without slippage and tipping over, and (iii physical limits of the motors. Simulations demonstrate that the proposed strategy is effective for energy-efficient biped walking.

  16. CLFs-based optimization control for a class of constrained visual servoing systems.

    Science.gov (United States)

    Song, Xiulan; Miaomiao, Fu

    2017-03-01

    In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

    Directory of Open Access Journals (Sweden)

    Minggang Dong

    2014-01-01

    Full Text Available Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE for constrained optimization problems (COPs. More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

  18. A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

    Science.gov (United States)

    Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

    2018-03-01

    In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

  19. Fuzzy chance constrained linear programming model for scrap charge optimization in steel production

    DEFF Research Database (Denmark)

    Rong, Aiying; Lahdelma, Risto

    2008-01-01

    the uncertainty based on fuzzy set theory and constrain the failure risk based on a possibility measure. Consequently, the scrap charge optimization problem is modeled as a fuzzy chance constrained linear programming problem. Since the constraints of the model mainly address the specification of the product...

  20. Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems

    OpenAIRE

    Vorozheikin, A.; Gonchar, T.; Panfilov, I.; Sopov, E.; Sopov, S.

    2009-01-01

    A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

  1. A penalty method for PDE-constrained optimization in inverse problems

    International Nuclear Information System (INIS)

    Leeuwen, T van; Herrmann, F J

    2016-01-01

    Many inverse and parameter estimation problems can be written as PDE-constrained optimization problems. The goal is to infer the parameters, typically coefficients of the PDE, from partial measurements of the solutions of the PDE for several right-hand sides. Such PDE-constrained problems can be solved by finding a stationary point of the Lagrangian, which entails simultaneously updating the parameters and the (adjoint) state variables. For large-scale problems, such an all-at-once approach is not feasible as it requires storing all the state variables. In this case one usually resorts to a reduced approach where the constraints are explicitly eliminated (at each iteration) by solving the PDEs. These two approaches, and variations thereof, are the main workhorses for solving PDE-constrained optimization problems arising from inverse problems. In this paper, we present an alternative method that aims to combine the advantages of both approaches. Our method is based on a quadratic penalty formulation of the constrained optimization problem. By eliminating the state variable, we develop an efficient algorithm that has roughly the same computational complexity as the conventional reduced approach while exploiting a larger search space. Numerical results show that this method indeed reduces some of the nonlinearity of the problem and is less sensitive to the initial iterate. (paper)

  2. A one-layer recurrent neural network for constrained nonsmooth invex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2014-02-01

    Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Constrained Optimization and Optimal Control for Partial Differential Equations

    CERN Document Server

    Leugering, Günter; Griewank, Andreas

    2012-01-01

    This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in this emerging field. Indeed, even though the field of optimal control and optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear problems is still lacking. The cont

  4. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

    Directory of Open Access Journals (Sweden)

    Hailong Wang

    2018-01-01

    Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

  5. Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

    Science.gov (United States)

    Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

    2012-12-01

    Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

  6. A Simply Constrained Optimization Reformulation of KKT Systems Arising from Variational Inequalities

    International Nuclear Information System (INIS)

    Facchinei, F.; Fischer, A.; Kanzow, C.; Peng, J.-M.

    1999-01-01

    The Karush-Kuhn-Tucker (KKT) conditions can be regarded as optimality conditions for both variational inequalities and constrained optimization problems. In order to overcome some drawbacks of recently proposed reformulations of KKT systems, we propose casting KKT systems as a minimization problem with nonnegativity constraints on some of the variables. We prove that, under fairly mild assumptions, every stationary point of this constrained minimization problem is a solution of the KKT conditions. Based on this reformulation, a new algorithm for the solution of the KKT conditions is suggested and shown to have some strong global and local convergence properties

  7. A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity.

    Science.gov (United States)

    Quan, Quan; Cai, Kai-Yuan

    2016-02-01

    In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

  8. Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization

    International Nuclear Information System (INIS)

    Zhang Xiaomeng; Wang Jing; Xing Lei

    2011-01-01

    Purpose: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of ''metal shadows'' in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general ''missing data'' image reconstruction problems.

  9. Superalloy design - A Monte Carlo constrained optimization method

    CSIR Research Space (South Africa)

    Stander, CM

    1996-01-01

    Full Text Available optimization method C. M. Stander Division of Materials Science and Technology, CSIR, PO Box 395, Pretoria, Republic of South Africa Received 74 March 1996; accepted 24 June 1996 A method, based on Monte Carlo constrained... successful hit, i.e. when Liow < LMP,,, < Lhiph, and for all the properties, Pj?, < P, < Pi@?. If successful this hit falls within the ROA. Repeat steps 4 and 5 to find at least ten (or more) successful hits with values...

  10. Effective Teaching of Economics: A Constrained Optimization Problem?

    Science.gov (United States)

    Hultberg, Patrik T.; Calonge, David Santandreu

    2017-01-01

    One of the fundamental tenets of economics is that decisions are often the result of optimization problems subject to resource constraints. Consumers optimize utility, subject to constraints imposed by prices and income. As economics faculty, instructors attempt to maximize student learning while being constrained by their own and students'…

  11. Thermally-Constrained Fuel-Optimal ISS Maneuvers

    Science.gov (United States)

    Bhatt, Sagar; Svecz, Andrew; Alaniz, Abran; Jang, Jiann-Woei; Nguyen, Louis; Spanos, Pol

    2015-01-01

    Optimal Propellant Maneuvers (OPMs) are now being used to rotate the International Space Station (ISS) and have saved hundreds of kilograms of propellant over the last two years. The savings are achieved by commanding the ISS to follow a pre-planned attitude trajectory optimized to take advantage of environmental torques. The trajectory is obtained by solving an optimal control problem. Prior to use on orbit, OPM trajectories are screened to ensure a static sun vector (SSV) does not occur during the maneuver. The SSV is an indicator that the ISS hardware temperatures may exceed thermal limits, causing damage to the components. In this paper, thermally-constrained fuel-optimal trajectories are presented that avoid an SSV and can be used throughout the year while still reducing propellant consumption significantly.

  12. Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle

    International Nuclear Information System (INIS)

    Lei, Fei; Du, Bin; Liu, Xin; Xie, Xiaoping; Chai, Tian

    2016-01-01

    In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. - Highlights: • An implicit constrained multi-physics model is built for sizing a motor wheel. • Vehicle dynamic performances are applied as implicit constraints for nonlinear system. • An efficient novel optimization is proposed to explore the constrained design space. • The motor wheel is optimized to achieve maximum efficiency on vehicle dynamics. • Influences of implicit constraints on vehicle performances are compared and analyzed.

  13. Chance-constrained optimization of demand response to price signals

    DEFF Research Database (Denmark)

    Dorini, Gianluca Fabio; Pinson, Pierre; Madsen, Henrik

    2013-01-01

    within a recursive least squares (RLS) framework using data measurable at the grid level, in an adaptive fashion. Optimal price signals are generated by embedding the FIR models within a chance-constrained optimization framework. The objective is to keep the price signal as unchanged as possible from...

  14. Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

    Directory of Open Access Journals (Sweden)

    Weishang Gao

    2013-01-01

    Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.

  15. 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.

  16. Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

    Directory of Open Access Journals (Sweden)

    Zhiwen Hu

    2015-01-01

    Full Text Available The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR. We also show that the proposed new method has a robust detection performance for broad SNR region.

  17. Volume-constrained optimization of magnetorheological and electrorheological valves and dampers

    Science.gov (United States)

    Rosenfeld, Nicholas C.; Wereley, Norman M.

    2004-12-01

    This paper presents a case study of magnetorheological (MR) and electrorheological (ER) valve design within a constrained cylindrical volume. The primary purpose of this study is to establish general design guidelines for volume-constrained MR valves. Additionally, this study compares the performance of volume-constrained MR valves against similarly constrained ER valves. Starting from basic design guidelines for an MR valve, a method for constructing candidate volume-constrained valve geometries is presented. A magnetic FEM program is then used to evaluate the magnetic properties of the candidate valves. An optimized MR valve is chosen by evaluating non-dimensional parameters describing the candidate valves' damping performance. A derivation of the non-dimensional damping coefficient for valves with both active and passive volumes is presented to allow comparison of valves with differing proportions of active and passive volumes. The performance of the optimized MR valve is then compared to that of a geometrically similar ER valve using both analytical and numerical techniques. An analytical equation relating the damping performances of geometrically similar MR and ER valves in as a function of fluid yield stresses and relative active fluid volume, and numerical calculations are provided to calculate each valve's damping performance and to validate the analytical calculations.

  18. Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

    Directory of Open Access Journals (Sweden)

    Kai Yang

    2016-01-01

    Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

  19. Solution for state constrained optimal control problems applied to power split control for hybrid vehicles

    NARCIS (Netherlands)

    Keulen, van T.A.C.; Gillot, J.; Jager, de A.G.; Steinbuch, M.

    2014-01-01

    This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution

  20. A New Interpolation Approach for Linearly Constrained Convex Optimization

    KAUST Repository

    Espinoza, Francisco

    2012-08-01

    In this thesis we propose a new class of Linearly Constrained Convex Optimization methods based on the use of a generalization of Shepard\\'s interpolation formula. We prove the properties of the surface such as the interpolation property at the boundary of the feasible region and the convergence of the gradient to the null space of the constraints at the boundary. We explore several descent techniques such as steepest descent, two quasi-Newton methods and the Newton\\'s method. Moreover, we implement in the Matlab language several versions of the method, particularly for the case of Quadratic Programming with bounded variables. Finally, we carry out performance tests against Matab Optimization Toolbox methods for convex optimization and implementations of the standard log-barrier and active-set methods. We conclude that the steepest descent technique seems to be the best choice so far for our method and that it is competitive with other standard methods both in performance and empirical growth order.

  1. New Exact Penalty Functions for Nonlinear Constrained Optimization Problems

    Directory of Open Access Journals (Sweden)

    Bingzhuang Liu

    2014-01-01

    Full Text Available For two kinds of nonlinear constrained optimization problems, we propose two simple penalty functions, respectively, by augmenting the dimension of the primal problem with a variable that controls the weight of the penalty terms. Both of the penalty functions enjoy improved smoothness. Under mild conditions, it can be proved that our penalty functions are both exact in the sense that local minimizers of the associated penalty problem are precisely the local minimizers of the original constrained problem.

  2. Constrained multi-objective optimization of storage ring lattices

    Science.gov (United States)

    Husain, Riyasat; Ghodke, A. D.

    2018-03-01

    The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

  3. Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid

    Directory of Open Access Journals (Sweden)

    Xiaogang Guo

    2018-01-01

    Full Text Available In this paper, an adaptively constrained stochastic model predictive control (MPC is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs in a microgrid (MG. Besides the economic objective of MG operation, the limits of state-of-charge (SOC and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.

  4. Adaptive Multi-Agent Systems for Constrained Optimization

    Science.gov (United States)

    Macready, William; Bieniawski, Stefan; Wolpert, David H.

    2004-01-01

    Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

  5. Stress-constrained topology optimization for compliant mechanism design

    DEFF Research Database (Denmark)

    de Leon, Daniel M.; Alexandersen, Joe; Jun, Jun S.

    2015-01-01

    This article presents an application of stress-constrained topology optimization to compliant mechanism design. An output displacement maximization formulation is used, together with the SIMP approach and a projection method to ensure convergence to nearly discrete designs. The maximum stress...... is approximated using a normalized version of the commonly-used p-norm of the effective von Mises stresses. The usual problems associated with topology optimization for compliant mechanism design: one-node and/or intermediate density hinges are alleviated by the stress constraint. However, it is also shown...

  6. Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems

    National Research Council Canada - National Science Library

    Abramson, Mark A; Audet, Charles; Dennis, Jr, J. E

    2004-01-01

    .... This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints...

  7. A constrained optimization algorithm for total energy minimization in electronic structure calculations

    International Nuclear Information System (INIS)

    Yang Chao; Meza, Juan C.; Wang Linwang

    2006-01-01

    A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequence of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal 'step-length' to move along this search direction. Numerical examples are provided to demonstrate that this new direct constrained optimization algorithm can be more efficient than the self-consistent field (SCF) iteration

  8. JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

    Science.gov (United States)

    Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

    2017-01-25

    Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open

  9. Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes

    Directory of Open Access Journals (Sweden)

    Xi Wu

    2017-08-01

    Full Text Available The successful application of the unified power flow controller (UPFC provides a new control method for the secure and economic operation of power system. In order to make the full use of UPFC and improve the economic efficiency and static security of a power system, a preventive security-constrained power flow optimization method considering UPFC control modes is proposed in this paper. Firstly, an iterative method considering UPFC control modes is deduced for power flow calculation. Taking into account the influence of different UPFC control modes on the distribution of power flow after N-1 contingency, the optimization model is then constructed by setting a minimal system operation cost and a maximum static security margin as the objective. Based on this model, the particle swarm optimization (PSO algorithm is utilized to optimize power system operating parameters and UPFC control modes simultaneously. Finally, a standard IEEE 30-bus system is utilized to demonstrate that the proposed method fully exploits the potential of static control of UPFC and significantly increases the economic efficiency and static security of the power system.

  10. A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [Universite catholique de Louvain

    2017-07-25

    This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.

  11. Generation and reserve dispatch in a competitive market using constrained particle swarm optimization

    International Nuclear Information System (INIS)

    Azadani, E. Nasr; Hosseinian, S.H.; Moradzadeh, B.

    2010-01-01

    Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm. (author)

  12. On meeting capital requirements with a chance-constrained optimization model.

    Science.gov (United States)

    Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

    2016-01-01

    This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

  13. Topology Optimization of Constrained Layer Damping on Plates Using Method of Moving Asymptote (MMA Approach

    Directory of Open Access Journals (Sweden)

    Zheng Ling

    2011-01-01

    Full Text Available Damping treatments have been extensively used as a powerful means to damp out structural resonant vibrations. Usually, damping materials are fully covered on the surface of plates. The drawbacks of this conventional treatment are also obvious due to an added mass and excess material consumption. Therefore, it is not always economical and effective from an optimization design view. In this paper, a topology optimization approach is presented to maximize the modal damping ratio of the plate with constrained layer damping treatment. The governing equation of motion of the plate is derived on the basis of energy approach. A finite element model to describe dynamic performances of the plate is developed and used along with an optimization algorithm in order to determine the optimal topologies of constrained layer damping layout on the plate. The damping of visco-elastic layer is modeled by the complex modulus formula. Considering the vibration and energy dissipation mode of the plate with constrained layer damping treatment, damping material density and volume factor are considered as design variable and constraint respectively. Meantime, the modal damping ratio of the plate is assigned as the objective function in the topology optimization approach. The sensitivity of modal damping ratio to design variable is further derived and Method of Moving Asymptote (MMA is adopted to search the optimized topologies of constrained layer damping layout on the plate. Numerical examples are used to demonstrate the effectiveness of the proposed topology optimization approach. The results show that vibration energy dissipation of the plates can be enhanced by the optimal constrained layer damping layout. This optimal technology can be further extended to vibration attenuation of sandwich cylindrical shells which constitute the major building block of many critical structures such as cabins of aircrafts, hulls of submarines and bodies of rockets and missiles as an

  14. Constrained convex minimization via model-based excessive gap

    OpenAIRE

    Tran Dinh, Quoc; Cevher, Volkan

    2014-01-01

    We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal feasibility gap of their iterates separately. Through a dual smoothing and prox-function selection strategy, our framework subsumes the augmented Lagrangian, and alternating methods as special cases, where our rates apply.

  15. A simple two stage optimization algorithm for constrained power economic dispatch

    International Nuclear Information System (INIS)

    Huang, G.; Song, K.

    1994-01-01

    A simple two stage optimization algorithm is proposed and investigated for fast computation of constrained power economic dispatch control problems. The method is a simple demonstration of the hierarchical aggregation-disaggregation (HAD) concept. The algorithm first solves an aggregated problem to obtain an initial solution. This aggregated problem turns out to be classical economic dispatch formulation, and it can be solved in 1% of overall computation time. In the second stage, linear programming method finds optimal solution which satisfies power balance constraints, generation and transmission inequality constraints and security constraints. Implementation of the algorithm for IEEE systems and EPRI Scenario systems shows that the two stage method obtains average speedup ratio 10.64 as compared to classical LP-based method

  16. Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

    Directory of Open Access Journals (Sweden)

    Kiran Teeparthi

    2017-04-01

    Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

  17. Fast optimization of statistical potentials for structurally constrained phylogenetic models

    Directory of Open Access Journals (Sweden)

    Rodrigue Nicolas

    2009-09-01

    Full Text Available Abstract Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure. Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.

  18. Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

    Directory of Open Access Journals (Sweden)

    Xi Wang

    2016-01-01

    Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.

  19. Order-constrained linear optimization.

    Science.gov (United States)

    Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P

    2017-11-01

    Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data. © 2017 The British Psychological Society.

  20. Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

    Science.gov (United States)

    Ono, Masahiro; Kuwata, Yoshiaki

    2013-01-01

    A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

  1. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

    Science.gov (United States)

    Xu, Y; Li, N

    2014-09-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

  2. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

    International Nuclear Information System (INIS)

    Xu, Y; Li, N

    2014-01-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

  3. Nonlinear Chance Constrained Problems: Optimality Conditions, Regularization and Solvers

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin

    2016-01-01

    Roč. 170, č. 2 (2016), s. 419-436 ISSN 0022-3239 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Chance constrained programming * Optimality conditions * Regularization * Algorithms * Free MATLAB codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.289, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0460909.pdf

  4. Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

    Science.gov (United States)

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

    2015-11-01

    The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    KAUST Repository

    Reyes, Juan Carlos De los

    2013-11-01

    We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

  6. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    KAUST Repository

    Reyes, Juan Carlos De los; Schö nlieb, Carola-Bibiane

    2013-01-01

    We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

  7. Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

    International Nuclear Information System (INIS)

    Xiao Yunhai; Hu Qingjie

    2008-01-01

    An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

  8. SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wei, J [City College of New York, New York, NY (United States); Chao, M [The Mount Sinai Medical Center, New York, NY (United States)

    2016-06-15

    Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

  9. SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

    International Nuclear Information System (INIS)

    Wei, J; Chao, M

    2016-01-01

    Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

  10. Multi-Objective Design Optimization of an Over-Constrained Flexure-Based Amplifier

    Directory of Open Access Journals (Sweden)

    Yuan Ni

    2015-07-01

    Full Text Available The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated.  Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed.

  11. PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

    Energy Technology Data Exchange (ETDEWEB)

    Sen, Satyabrata [ORNL

    2014-01-01

    We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratio (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.

  12. Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal

    Science.gov (United States)

    Steinley, Douglas; Hubert, Lawrence

    2008-01-01

    This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…

  13. Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

    Directory of Open Access Journals (Sweden)

    Zunaira Nadeem

    2018-04-01

    Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

  14. Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

    Directory of Open Access Journals (Sweden)

    R. Manam

    2017-12-01

    Full Text Available In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

  15. A first-order multigrid method for bound-constrained convex optimization

    Czech Academy of Sciences Publication Activity Database

    Kočvara, Michal; Mohammed, S.

    2016-01-01

    Roč. 31, č. 3 (2016), s. 622-644 ISSN 1055-6788 R&D Projects: GA ČR(CZ) GAP201/12/0671 Grant - others:European Commission - EC(XE) 313781 Institutional support: RVO:67985556 Keywords : bound-constrained optimization * multigrid methods * linear complementarity problems Subject RIV: BA - General Mathematics Impact factor: 1.023, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/kocvara-0460326.pdf

  16. Pareto-optimal estimates that constrain mean California precipitation change

    Science.gov (United States)

    Langenbrunner, B.; Neelin, J. D.

    2017-12-01

    Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

  17. GA based CNC turning center exploitation process parameters optimization

    Directory of Open Access Journals (Sweden)

    Z. Car

    2009-01-01

    Full Text Available This paper presents machining parameters (turning process optimization based on the use of artificial intelligence. To obtain greater efficiency and productivity of the machine tool, optimal cutting parameters have to be obtained. In order to find optimal cutting parameters, the genetic algorithm (GA has been used as an optimal solution finder. Optimization has to yield minimum machining time and minimum production cost, while considering technological and material constrains.

  18. Electrochemomechanical constrained multiobjective optimization of PPy/MWCNT actuators

    International Nuclear Information System (INIS)

    Khalili, N; Naguib, H E; Kwon, R H

    2014-01-01

    Polypyrrole (PPy) conducting polymers have shown a great potential for the fabrication of conjugated polymer-based actuating devices. Consequently, they have been a key point in developing many advanced emerging applications such as biomedical devices and biomimetic robotics. When designing an actuator, taking all of the related decision variables, their roles and relationships into consideration is of pivotal importance to determine the actuator’s final performance. Therefore, the central focus of this study is to develop an electrochemomechanical constrained multiobjective optimization model of a PPy/MWCNTs trilayer actuator. For this purpose, the objective functions are designed to capture the three main characteristics of these actuators, namely their tip vertical displacement, blocking force and response time. To obtain the optimum range of the designated decision variables within the feasible domain, a multiobjective optimization algorithm is applied while appropriate constraints are imposed. The optimum points form a Pareto surface on which they are consistently spread. The numerical results are presented; these results enable one to design an actuator with consideration to the desired output performances. For the experimental analysis, a multilayer bending-type actuator is fabricated, which is composed of a PVDF layer and two layers of PPy with an incorporated layer of multi-walled carbon nanotubes deposited on each side of the PVDF membrane. The numerical results are experimentally verified; in order to determine the performance of the fabricated actuator, its outputs are compared with a neat PPy actuator’s experimental and numerical counterparts. (paper)

  19. Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

    Science.gov (United States)

    Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

    2002-06-01

    This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

  20. Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

    International Nuclear Information System (INIS)

    Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

    2017-01-01

    Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

  1. Mixed Integer PDE Constrained Optimization for the Control of a Wildfire Hazard

    Science.gov (United States)

    2017-01-01

    Constrained Optimization for the Control of a Wildfire Hazard Herausgegeben von der Professor fur Angewandte Mathematik Professor Dr. rer. nat. Armin...and H.H. Tan . Finite difference methods for solving the two-dimensional advection-diffusion equation. Int. J. Numer. Meth. Fluids, 9:75-98, 1989. 6

  2. A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

    Science.gov (United States)

    Liu, Qingshan; Guo, Zhishan; Wang, Jun

    2012-02-01

    In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

    International Nuclear Information System (INIS)

    Weir, V; Zhang, J

    2015-01-01

    Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols

  4. SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Weir, V; Zhang, J [University of Kentucky, Lexington, KY (United States)

    2015-06-15

    Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols.

  5. Block-triangular preconditioners for PDE-constrained optimization

    KAUST Repository

    Rees, Tyrone

    2010-11-26

    In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

  6. Block-triangular preconditioners for PDE-constrained optimization

    KAUST Repository

    Rees, Tyrone; Stoll, Martin

    2010-01-01

    In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

  7. An L∞/L1-Constrained Quadratic Optimization Problem with Applications to Neural Networks

    International Nuclear Information System (INIS)

    Leizarowitz, Arie; Rubinstein, Jacob

    2003-01-01

    Pattern formation in associative neural networks is related to a quadratic optimization problem. Biological considerations imply that the functional is constrained in the L ∞ norm and in the L 1 norm. We consider such optimization problems. We derive the Euler-Lagrange equations, and construct basic properties of the maximizers. We study in some detail the case where the kernel of the quadratic functional is finite-dimensional. In this case the optimization problem can be fully characterized by the geometry of a certain convex and compact finite-dimensional set

  8. Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

    Czech Academy of Sciences Publication Activity Database

    Axelsson, Owe; Farouq, S.; Neytcheva, M.

    2017-01-01

    Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

  9. Linearly constrained minimax optimization

    DEFF Research Database (Denmark)

    Madsen, Kaj; Schjær-Jacobsen, Hans

    1978-01-01

    We present an algorithm for nonlinear minimax optimization subject to linear equality and inequality constraints which requires first order partial derivatives. The algorithm is based on successive linear approximations to the functions defining the problem. The resulting linear subproblems...

  10. Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Hsiang-Hsi Huang

    2015-01-01

    Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

  11. Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

    NARCIS (Netherlands)

    F. Cong (Fei); C.W. Oosterlee (Kees)

    2016-01-01

    htmlabstractWe propose a simulation-based approach for solving the constrained dynamic mean– variance portfolio managemen tproblem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then,

  12. Genetic algorithm based separation cascade optimization

    International Nuclear Information System (INIS)

    Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

    2008-01-01

    The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

  13. Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2016-01-01

    Full Text Available The teaching-learning-based optimization (TLBO algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.

  14. Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

    Czech Academy of Sciences Publication Activity Database

    Axelsson, Owe; Farouq, S.; Neytcheva, M.

    2017-01-01

    Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution method s * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

  15. Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

    Science.gov (United States)

    Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

    2018-05-01

    Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

  16. Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

    Science.gov (United States)

    2014-01-01

    Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

  17. Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

    Science.gov (United States)

    Bacanin, Nebojsa; Tuba, Milan

    2014-01-01

    Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

  18. OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

    Directory of Open Access Journals (Sweden)

    Dhananjay Kumar

    2016-01-01

    Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

  19. Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems

    Directory of Open Access Journals (Sweden)

    Man Hong

    2013-01-01

    Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.

  20. Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

    Science.gov (United States)

    Crown, William; Buyukkaramikli, Nasuh; Thokala, Praveen; Morton, Alec; Sir, Mustafa Y; Marshall, Deborah A; Tosh, Jon; Padula, William V; Ijzerman, Maarten J; Wong, Peter K; Pasupathy, Kalyan S

    2017-03-01

    Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  1. Optimal dispatch in dynamic security constrained open power market

    International Nuclear Information System (INIS)

    Singh, S.N.; David, A.K.

    2002-01-01

    Power system security is a new concern in the competitive power market operation, because the integration of the system controller and the generation owner has been broken. This paper presents an approach for dynamic security constrained optimal dispatch in restructured power market environment. The transient energy margin using transient energy function (TEF) approach has been used to calculate the stability margin of the system and a hybrid method is applied to calculate the approximate unstable equilibrium point (UEP) that is used to calculate the exact UEP and thus, the energy margin using TEF. The case study results illustrated on two systems shows that the operating mechanisms are compatible with the new business environment. (author)

  2. Path-Constrained Motion Planning for Robotics Based on Kinematic Constraints

    NARCIS (Netherlands)

    Dijk, van N.J.M.; Wouw, van de N.; Pancras, W.C.M.; Nijmeijer, H.

    2007-01-01

    Common robotic tracking tasks consist of motions along predefined paths. The design of time-optimal path-constrained trajectories for robotic applications is discussed in this paper. To increase industrial applicability, the proposed method accounts for robot kinematics together with actuator

  3. SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

    International Nuclear Information System (INIS)

    An, Y; Liang, J; Liu, W

    2015-01-01

    Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with

  4. Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

    Directory of Open Access Journals (Sweden)

    Sengupta Raghu Nandan

    2017-02-01

    Full Text Available We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

  5. Loading pattern optimization with maximum utilization of discharging fuel employing adaptively constrained discontinuous penalty function

    International Nuclear Information System (INIS)

    Park, T. K.; Joo, H. G.; Kim, C. H.

    2010-01-01

    In order to find the most economical loading pattern (LP) considering multi-cycle fuel loading, multi-objective fuel LP optimization problems are examined by employing an adaptively constrained discontinuous penalty function (ACDPF) method. This is an improved method to simplify the complicated acceptance logic of the original DPF method in that the stochastic effects caused by the different random number sequence can be reduced. The effectiveness of the multi-objective simulated annealing (SA) algorithm employing ACDPF is examined for the reload core LP of Cycle 4 of Yonggwang Nuclear Unit 4. Several optimization runs are performed with different numbers of objectives consisting of cycle length and average burnup of fuels to be discharged or reloaded. The candidate LPs obtained from the multi-objective optimization runs turn out to be better than the reference LP in the aspects of cycle length and utilization of given fuels. It is note that the proposed ACDPF based MOSA algorithm can be a practical method to obtain an economical LP considering multi-cycle fuel loading. (authors)

  6. Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

    Directory of Open Access Journals (Sweden)

    Dayong Zhou

    2008-12-01

    Full Text Available Tsatsanis and Xu have applied the constrained minimum output variance (CMOV principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

  7. SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

    Directory of Open Access Journals (Sweden)

    Xiaoliang Wang

    2017-01-01

    Full Text Available Indoor localization technology based on Wi-Fi has long been a hot research topic in the past decade. Despite numerous solutions, new challenges have arisen along with the trend of smart home and wearable computing. For example, power efficiency needs to be significantly improved for resource-constrained wearable devices, such as smart watch and wristband. For a Wi-Fi-based locating system, most of the energy consumption can be attributed to real-time radio scan; however, simply reducing radio data collection will cause a serious loss of locating accuracy because of unstable Wi-Fi signals. In this paper, we present SmartFix, an optimization algorithm for indoor locating based on Wi-Fi RSS. SmartFix utilizes user motion features, extracts characteristic value from history trajectory, and corrects deviation caused by unstable Wi-Fi signals. We implemented a prototype of SmartFix both on Moto 360 2nd-generation Smartwatch and on HTC One Smartphone. We conducted experiments both in a large open area and in an office hall. Experiment results demonstrate that average locating error is less than 2 meters for more than 80% cases, and energy consumption is only 30% of Wi-Fi fingerprinting method under the same experiment circumstances.

  8. 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...

  9. WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

    Energy Technology Data Exchange (ETDEWEB)

    Watkins, W.T.; Siebers, J.V. [University of Virginia, Charlottesville, VA (United States)

    2016-06-15

    Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

  10. WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

    International Nuclear Information System (INIS)

    Watkins, W.T.; Siebers, J.V.

    2016-01-01

    Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

  11. Optimal Power Allocation Algorithm for Radar Network Systems Based on Low Probability of Intercept Optimization(in English

    Directory of Open Access Journals (Sweden)

    Shi Chen-guang

    2014-08-01

    Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.

  12. Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

    Science.gov (United States)

    Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

    2017-06-01

    Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

  13. Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2011-04-01

    This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

  14. Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

    Directory of Open Access Journals (Sweden)

    Xing Liu

    2014-12-01

    Full Text Available Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes.

  15. Statistical mechanics of budget-constrained auctions

    OpenAIRE

    Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

    2009-01-01

    Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). Based on the cavity method of statistical mechanics, we introduce a message passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution,...

  16. A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

    Science.gov (United States)

    Zhang, Chenglong; Guo, Ping

    2017-10-01

    The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

  17. Topology Optimization for Minimizing the Resonant Response of Plates with Constrained Layer Damping Treatment

    Directory of Open Access Journals (Sweden)

    Zhanpeng Fang

    2015-01-01

    Full Text Available A topology optimization method is proposed to minimize the resonant response of plates with constrained layer damping (CLD treatment under specified broadband harmonic excitations. The topology optimization problem is formulated and the square of displacement resonant response in frequency domain at the specified point is considered as the objective function. Two sensitivity analysis methods are investigated and discussed. The derivative of modal damp ratio is not considered in the conventional sensitivity analysis method. An improved sensitivity analysis method considering the derivative of modal damp ratio is developed to improve the computational accuracy of the sensitivity. The evolutionary structural optimization (ESO method is used to search the optimal layout of CLD material on plates. Numerical examples and experimental results show that the optimal layout of CLD treatment on the plate from the proposed topology optimization using the conventional sensitivity analysis or the improved sensitivity analysis can reduce the displacement resonant response. However, the optimization method using the improved sensitivity analysis can produce a higher modal damping ratio than that using the conventional sensitivity analysis and develop a smaller displacement resonant response.

  18. Modeling Oil Exploration and Production: Resource-Constrained and Agent-Based Approaches

    International Nuclear Information System (INIS)

    Jakobsson, Kristofer

    2010-05-01

    Energy is essential to the functioning of society, and oil is the single largest commercial energy source. Some analysts have concluded that the peak in oil production is soon about to happen on the global scale, while others disagree. Such incompatible views can persist because the issue of 'peak oil' cuts through the established scientific disciplines. The question is: what characterizes the modeling approaches that are available today, and how can they be further developed to improve a trans-disciplinary understanding of oil depletion? The objective of this thesis is to present long-term scenarios of oil production (Paper I) using a resource-constrained model; and an agent-based model of the oil exploration process (Paper II). It is also an objective to assess the strengths, limitations, and future development potentials of resource-constrained modeling, analytical economic modeling, and agent-based modeling. Resource-constrained models are only suitable when the time frame is measured in decades, but they can give a rough indication of which production scenarios are reasonable given the size of the resource. However, the models are comprehensible, transparent and the only feasible long-term forecasting tools at present. It is certainly possible to distinguish between reasonable scenarios, based on historically observed parameter values, and unreasonable scenarios with parameter values obtained through flawed analogy. The economic subfield of optimal depletion theory is founded on the notion of rational economic agents, and there is a causal relation between decisions made at the micro-level and the macro-result. In terms of future improvements, however, the analytical form considerably restricts the versatility of the approach. Agent-based modeling makes it feasible to combine economically motivated agents with a physical environment. An example relating to oil exploration is given in Paper II, where it is shown that the exploratory activities of individual

  19. The Regularized Fast Hartley Transform Optimal Formulation of Real-Data Fast Fourier Transform for Silicon-Based Implementation in Resource-Constrained Environments

    CERN Document Server

    Jones, Keith

    2010-01-01

    The Regularized Fast Hartley Transform provides the reader with the tools necessary to both understand the proposed new formulation and to implement simple design variations that offer clear implementational advantages, both practical and theoretical, over more conventional complex-data solutions to the problem. The highly-parallel formulation described is shown to lead to scalable and device-independent solutions to the latency-constrained version of the problem which are able to optimize the use of the available silicon resources, and thus to maximize the achievable computational density, th

  20. A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn [Institute of Natural Sciences, Department of Mathematics, and MOE Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240 (China); Lin, Guang, E-mail: lin491@purdue.edu [Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States); Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Yang, Xu, E-mail: xuyang@math.ucsb.edu [Department of Mathematics, University of California, Santa Barbara, CA 93106 (United States)

    2015-09-01

    In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

  1. Mini-batch optimized full waveform inversion with geological constrained gradient filtering

    Science.gov (United States)

    Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

    2018-05-01

    High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

  2. Dynamic Optimization of Constrained Layer Damping Structure for the Headstock of Machine Tools with Modal Strain Energy Method

    Directory of Open Access Journals (Sweden)

    Yakai Xu

    2017-01-01

    Full Text Available Dynamic stiffness and damping of the headstock, which is a critical component of precision horizontal machining center, are two main factors that influence machining accuracy and surface finish quality. Constrained Layer Damping (CLD structure is proved to be effective in raising damping capacity for the thin plate and shell structures. In this paper, one kind of high damping material is utilized on the headstock to improve damping capacity. The dynamic characteristic of the hybrid headstock is investigated analytically and experimentally. The results demonstrate that the resonant response amplitudes of the headstock with damping material can decrease significantly compared to original cast structure. To obtain the optimal configuration of damping material, a topology optimization method based on the Evolutionary Structural Optimization (ESO is implemented. Modal Strain Energy (MSE method is employed to analyze the damping and to derive the sensitivity of the modal loss factor. The optimization results indicate that the added weight of damping material decreases by 50%; meanwhile the first two orders of modal loss factor decrease by less than 23.5% compared to the original structure.

  3. Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

    Science.gov (United States)

    Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

    2017-10-01

    Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

  4. Pricing and lot sizing optimization in a two-echelon supply chain with a constrained Logit demand function

    Directory of Open Access Journals (Sweden)

    Yeison Díaz-Mateus

    2017-07-01

    Full Text Available Decision making in supply chains is influenced by demand variations, and hence sales, purchase orders and inventory levels are therefore concerned. This paper presents a non-linear optimization model for a two-echelon supply chain, for a unique product. In addition, the model includes the consumers’ maximum willingness to pay, taking socioeconomic differences into account. To do so, the constrained multinomial logit for discrete choices is used to estimate demand levels. Then, a metaheuristic approach based on particle swarm optimization is proposed to determine the optimal product sales price and inventory coordination variables. To validate the proposed model, a supply chain of a technological product was chosen and three scenarios are analyzed: discounts, demand segmentation and demand overestimation. Results are analyzed on the basis of profits, lotsizing and inventory turnover and market share. It can be concluded that the maximum willingness to pay must be taken into consideration, otherwise fictitious profits may mislead decision making, and although the market share would seem to improve, overall profits are not in fact necessarily better.

  5. On the optimal identification of tag sets in time-constrained RFID configurations.

    Science.gov (United States)

    Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel

    2011-01-01

    In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.

  6. Constrained Optimal Transport

    Science.gov (United States)

    Ekren, Ibrahim; Soner, H. Mete

    2018-03-01

    The classical duality theory of Kantorovich (C R (Doklady) Acad Sci URSS (NS) 37:199-201, 1942) and Kellerer (Z Wahrsch Verw Gebiete 67(4):399-432, 1984) for classical optimal transport is generalized to an abstract framework and a characterization of the dual elements is provided. This abstract generalization is set in a Banach lattice X with an order unit. The problem is given as the supremum over a convex subset of the positive unit sphere of the topological dual of X and the dual problem is defined on the bi-dual of X. These results are then applied to several extensions of the classical optimal transport.

  7. Oil Reservoir Production Optimization using Optimal Control

    DEFF Research Database (Denmark)

    Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan

    2011-01-01

    Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...

  8. Design of a Circularly Polarized Galileo E6-Band Textile Antenna by Dedicated Multiobjective Constrained Pareto Optimization

    Directory of Open Access Journals (Sweden)

    Arnaut Dierck

    2015-01-01

    Full Text Available Designing textile antennas for real-life applications requires a design strategy that is able to produce antennas that are optimized over a wide bandwidth for often conflicting characteristics, such as impedance matching, axial ratio, efficiency, and gain, and, moreover, that is able to account for the variations that apply for the characteristics of the unconventional materials used in smart textile systems. In this paper, such a strategy, incorporating a multiobjective constrained Pareto optimization, is presented and applied to the design of a Galileo E6-band antenna with optimal return loss and wide-band axial ratio characteristics. Subsequently, different prototypes of the optimized antenna are fabricated and measured to validate the proposed design strategy.

  9. Optimization of offshore wind turbine support structures using analytical gradient-based method

    OpenAIRE

    Chew, Kok Hon; Tai, Kang; Ng, E.Y.K.; Muskulus, Michael

    2015-01-01

    Design optimization of the offshore wind turbine support structure is an expensive task; due to the highly-constrained, non-convex and non-linear nature of the design problem. This report presents an analytical gradient-based method to solve this problem in an efficient and effective way. The design sensitivities of the objective and constraint functions are evaluated analytically while the optimization of the structure is performed, subject to sizing, eigenfrequency, extreme load an...

  10. Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

    OpenAIRE

    He, Longfei; Xu, Zhaoguang; Niu, Zhanwen

    2014-01-01

    We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optim...

  11. Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

    International Nuclear Information System (INIS)

    Rao, R.V.; More, K.C.

    2015-01-01

    Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence

  12. Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

    Science.gov (United States)

    Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

    2014-11-01

    In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

  13. Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization

    OpenAIRE

    Deming Yuan

    2014-01-01

    This paper considers the constrained multiagent optimization problem. The objective function of the problem is a sum of convex functions, each of which is known by a specific agent only. For solving this problem, we propose an asynchronous distributed method that is based on gradient-free oracles and gossip algorithm. In contrast to the existing work, we do not require that agents be capable of computing the subgradients of their objective functions and coordinating their...

  14. Robust optimization methods for chance constrained, simulation-based, and bilevel problems

    NARCIS (Netherlands)

    Yanikoglu, I.

    2014-01-01

    The objective of robust optimization is to find solutions that are immune to the uncertainty of the parameters in a mathematical optimization problem. It requires that the constraints of a given problem should be satisfied for all realizations of the uncertain parameters in a so-called uncertainty

  15. Workshop on Computational Optimization

    CERN Document Server

    2015-01-01

    Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2013. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, resource constrained project scheduling, problems arising in transport services, error correcting codes, optimal system performance and energy consumption and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others.

  16. Modified Covariance Matrix Adaptation – Evolution Strategy algorithm for constrained optimization under uncertainty, application to rocket design

    Directory of Open Access Journals (Sweden)

    Chocat Rudy

    2015-01-01

    Full Text Available The design of complex systems often induces a constrained optimization problem under uncertainty. An adaptation of CMA-ES(λ, μ optimization algorithm is proposed in order to efficiently handle the constraints in the presence of noise. The update mechanisms of the parametrized distribution used to generate the candidate solutions are modified. The constraint handling method allows to reduce the semi-principal axes of the probable research ellipsoid in the directions violating the constraints. The proposed approach is compared to existing approaches on three analytic optimization problems to highlight the efficiency and the robustness of the algorithm. The proposed method is used to design a two stage solid propulsion launch vehicle.

  17. An algorithm for mass matrix calculation of internally constrained molecular geometries

    International Nuclear Information System (INIS)

    Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

    2008-01-01

    Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model

  18. An algorithm for mass matrix calculation of internally constrained molecular geometries.

    Science.gov (United States)

    Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

    2008-01-28

    Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model.

  19. Exact methods for time constrained routing and related scheduling problems

    DEFF Research Database (Denmark)

    Kohl, Niklas

    1995-01-01

    of customers. In the VRPTW customers must be serviced within a given time period - a so called time window. The objective can be to minimize operating costs (e.g. distance travelled), fixed costs (e.g. the number of vehicles needed) or a combination of these component costs. During the last decade optimization......This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set...... of J?rnsten, Madsen and S?rensen (1986), which has been tested computationally by Halse (1992). Both methods decompose the problem into a series of time and capacity constrained shotest path problems. This yields a tight lower bound on the optimal objective, and the dual gap can often be closed...

  20. 3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

    Directory of Open Access Journals (Sweden)

    Jiekun Song

    2016-01-01

    Full Text Available Harmonious development of 3Es (economy-energy-environment system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, and NOX and CO2 emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

  1. Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems - Poisson and convection-diffusion control

    Czech Academy of Sciences Publication Activity Database

    Axelsson, Owe; Farouq, S.; Neytcheva, M.

    2016-01-01

    Roč. 73, č. 3 (2016), s. 631-633 ISSN 1017-1398 R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods Subject RIV: BA - General Mathematics Impact factor: 1.241, year: 2016 http://link.springer.com/article/10.1007%2Fs11075-016-0111-1

  2. Workshop on Computational Optimization

    CERN Document Server

    2016-01-01

    This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.

  3. A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality

    Science.gov (United States)

    Cheung, KW; So, HC; Ma, W.-K.; Chan, YT

    2006-12-01

    The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time-of-arrival (TOA), received signal strength (RSS), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the mobile station. In this paper, we present a constrained weighted least squares (CWLS) mobile positioning approach that encompasses all the above described measurement cases. The advantages of CWLS include performance optimality and capability of extension to hybrid measurement cases (e.g., mobile positioning using TDOA and AOA measurements jointly). Assuming zero-mean uncorrelated measurement errors, we show by mean and variance analysis that all the developed CWLS location estimators achieve zero bias and the Cramér-Rao lower bound approximately when measurement error variances are small. The asymptotic optimum performance is also confirmed by simulation results.

  4. Chance constrained uncertain classification via robust optimization

    NARCIS (Netherlands)

    Ben-Tal, A.; Bhadra, S.; Bhattacharayya, C.; Saketha Nat, J.

    2011-01-01

    This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out

  5. Depletion mapping and constrained optimization to support managing groundwater extraction

    Science.gov (United States)

    Fienen, Michael N.; Bradbury, Kenneth R.; Kniffin, Maribeth; Barlow, Paul M.

    2018-01-01

    Groundwater models often serve as management tools to evaluate competing water uses including ecosystems, irrigated agriculture, industry, municipal supply, and others. Depletion potential mapping—showing the model-calculated potential impacts that wells have on stream baseflow—can form the basis for multiple potential management approaches in an oversubscribed basin. Specific management approaches can include scenarios proposed by stakeholders, systematic changes in well pumping based on depletion potential, and formal constrained optimization, which can be used to quantify the tradeoff between water use and stream baseflow. Variables such as the maximum amount of reduction allowed in each well and various groupings of wells using, for example, K-means clustering considering spatial proximity and depletion potential are considered. These approaches provide a potential starting point and guidance for resource managers and stakeholders to make decisions about groundwater management in a basin, spreading responsibility in different ways. We illustrate these approaches in the Little Plover River basin in central Wisconsin, United States—home to a rich agricultural tradition, with farmland and urban areas both in close proximity to a groundwater-dependent trout stream. Groundwater withdrawals have reduced baseflow supplying the Little Plover River below a legally established minimum. The techniques in this work were developed in response to engaged stakeholders with various interests and goals for the basin. They sought to develop a collaborative management plan at a watershed scale that restores the flow rate in the river in a manner that incorporates principles of shared governance and results in effective and minimally disruptive changes in groundwater extraction practices.

  6. Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

    International Nuclear Information System (INIS)

    Qi Pei-Han; Zheng Shi-Lian; Yang Xiao-Niu; Zhao Zhi-Jin

    2016-01-01

    Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications. (paper)

  7. A model for optimal constrained adaptive testing

    NARCIS (Netherlands)

    van der Linden, Willem J.; Reese, Lynda M.

    2001-01-01

    A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

  8. A model for optimal constrained adaptive testing

    NARCIS (Netherlands)

    van der Linden, Willem J.; Reese, Lynda M.

    1997-01-01

    A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

  9. Online constrained model-based reinforcement learning

    CSIR Research Space (South Africa)

    Van Niekerk, B

    2017-08-01

    Full Text Available Constrained Model-based Reinforcement Learning Benjamin van Niekerk School of Computer Science University of the Witwatersrand South Africa Andreas Damianou∗ Amazon.com Cambridge, UK Benjamin Rosman Council for Scientific and Industrial Research, and School... MULTIPLE SHOOTING Using direct multiple shooting (Bock and Plitt, 1984), problem (1) can be transformed into a structured non- linear program (NLP). First, the time horizon [t0, t0 + T ] is partitioned into N equal subintervals [tk, tk+1] for k = 0...

  10. An ensemble-based method for constrained reservoir life-cycle optimization

    NARCIS (Netherlands)

    Leeuwenburgh, O.; Egberts, P.J.P.; Chitu, A.G.

    2015-01-01

    We consider the problem of finding optimal long-term (life-cycle) recovery strategies for hydrocarbon reservoirs by use of simulation models. In such problems the presence of operating constraints, such as for example a maximum rate limit for a group of wells, may strongly influence the range of

  11. An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

    Science.gov (United States)

    Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

    2018-05-01

    In this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.

  12. Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

    Science.gov (United States)

    Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

    2013-10-01

    This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

  13. Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

    Science.gov (United States)

    Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

    2013-11-01

    Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

  14. Thermodynamic optimization of mixed refrigerant Joule- Thomson systems constrained by heat transfer considerations

    International Nuclear Information System (INIS)

    Hinze, J F; Klein, S A; Nellis, G F

    2015-01-01

    Mixed refrigerant (MR) working fluids can significantly increase the cooling capacity of a Joule-Thomson (JT) cycle. The optimization of MRJT systems has been the subject of substantial research. However, most optimization techniques do not model the recuperator in sufficient detail. For example, the recuperator is usually assumed to have a heat transfer coefficient that does not vary with the mixture. Ongoing work at the University of Wisconsin-Madison has shown that the heat transfer coefficients for two-phase flow are approximately three times greater than for a single phase mixture when the mixture quality is between 15% and 85%. As a result, a system that optimizes a MR without also requiring that the flow be in this quality range may require an extremely large recuperator or not achieve the performance predicted by the model. To ensure optimal performance of the JT cycle, the MR should be selected such that it is entirely two-phase within the recuperator. To determine the optimal MR composition, a parametric study was conducted assuming a thermodynamically ideal cycle. The results of the parametric study are graphically presented on a contour plot in the parameter space consisting of the extremes of the qualities that exist within the recuperator. The contours show constant values of the normalized refrigeration power. This ‘map’ shows the effect of MR composition on the cycle performance and it can be used to select the MR that provides a high cooling load while also constraining the recuperator to be two phase. The predicted best MR composition can be used as a starting point for experimentally determining the best MR. (paper)

  15. Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products

    Directory of Open Access Journals (Sweden)

    Yuting Li

    2016-04-01

    Full Text Available In recent years; financing difficulties have been obsessed small and medium enterprises (SMEs; especially emerging SMEs. Inter-members’ joint financing within a supply chain is one of solutions for SMEs. How about members’ joint financing of inter-supply chains? In order to answer the question, we firstly employ the Stackelberg game to propose three kinds of financing decision models of two cash-constrained supply chains with complementary products. Secondly, we analyze qualitatively these models and find the joint financing decision of the two supply chains is the most optimal one. Lastly, we conduct some numerical simulations not only to illustrate above results but also to find that the larger are cross-price sensitivity coefficients; the higher is the motivation for participants to make joint financing decisions; and the more are profits for them to gain.

  16. Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Oussama Ait Sahed

    2015-01-01

    Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

  17. Constrained Burn Optimization for the International Space Station

    Science.gov (United States)

    Brown, Aaron J.; Jones, Brandon A.

    2017-01-01

    In long-term trajectory planning for the International Space Station (ISS), translational burns are currently targeted sequentially to meet the immediate trajectory constraints, rather than simultaneously to meet all constraints, do not employ gradient-based search techniques, and are not optimized for a minimum total deltav (v) solution. An analytic formulation of the constraint gradients is developed and used in an optimization solver to overcome these obstacles. Two trajectory examples are explored, highlighting the advantage of the proposed method over the current approach, as well as the potential v and propellant savings in the event of propellant shortages.

  18. A Variant of the Topkis-Veinott Method for Solving Inequality Constrained Optimization Problems

    International Nuclear Information System (INIS)

    Birge, J. R.; Qi, L.; Wei, Z.

    2000-01-01

    In this paper we give a variant of the Topkis-Veinott method for solving inequality constrained optimization problems. This method uses a linearly constrained positive semidefinite quadratic problem to generate a feasible descent direction at each iteration. Under mild assumptions, the algorithm is shown to be globally convergent in the sense that every accumulation point of the sequence generated by the algorithm is a Fritz-John point of the problem. We introduce a Fritz-John (FJ) function, an FJ1 strong second-order sufficiency condition (FJ1-SSOSC), and an FJ2 strong second-order sufficiency condition (FJ2-SSOSC), and then show, without any constraint qualification (CQ), that (i) if an FJ point z satisfies the FJ1-SSOSC, then there exists a neighborhood N(z) of z such that, for any FJ point y element of N(z) {z } , f 0 (y) ≠ f 0 (z) , where f 0 is the objective function of the problem; (ii) if an FJ point z satisfies the FJ2-SSOSC, then z is a strict local minimum of the problem. The result (i) implies that the entire iteration point sequence generated by the method converges to an FJ point. We also show that if the parameters are chosen large enough, a unit step length can be accepted by the proposed algorithm

  19. Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

    Science.gov (United States)

    Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

    with information to improve their crop's vigor has been a major topic of interest. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, the efficiency of farming must increase to meet future food requirements and to make farming a sustainable occupation for the farmer. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The goal is to increase farm revenue by increasing crop yield and decreasing applications of costly chemical and water treatments. In addition, this methodology will decrease the environmental costs of farming, i.e., reduce air, soil, and water pollution. Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now available. Commercial satellite systems can image (multi-spectral) the Earth with a resolution of approximately 2.5 m. Variable precision dispensing systems using GPS are available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been formulated. Personal computers and internet access are in place in most farm homes and can provide a mechanism to periodically disseminate, e.g. bi-weekly, advice on what quantities of water and chemicals are needed in individual regions of the field. What is missing is a model that fuses the disparate sources of information on the current states of the crop and soil, and the remaining resource levels available with the decisions farmers are required to make. This must be a product that is easy for the farmer to understand and to implement. A "Constrained Optimization Feed-back Control Model" to fill this void will be presented. The objective function of the model will be used to maximize the farmer's profit by increasing yields while decreasing environmental costs and decreasing

  20. Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

    Science.gov (United States)

    Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

    2003-08-01

    For two decades now, the use of Remote Sensing/Precision Agriculture to improve farm yields while reducing the use of polluting chemicals and the limited water supply has been a major goal. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, farm efficiency must increase to meet future food requirements and to make farming a sustainable, profitable occupation. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The real goal is to increase farm profitability by identifying the additional treatments of chemicals and water that increase revenues more than they increase costs and do no exceed pollution standards (constrained optimization). Even though the economic and environmental benefits appear to be great, Remote Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now in place, but other needed factors have been missing. Commercial satellite systems can now image the Earth (multi-spectrally) with a resolution as fine as 2.5 m. Precision variable dispensing systems using GPS are now available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been developed. Personal computers and internet access are now in place in most farm homes and can provide a mechanism for periodically disseminating advice on what quantities of water and chemicals are needed in specific regions of each field. Several processes have been selected that fuse the disparate sources of information on the current and historic states of the crop and soil, and the remaining resource levels available, with the critical decisions that farmers are required to make. These are done in a way that is easy for the farmer to understand and profitable to implement. A "Constrained

  1. Slope constrained Topology Optimization

    DEFF Research Database (Denmark)

    Petersson, J.; Sigmund, Ole

    1998-01-01

    The problem of minimum compliance topology optimization of an elastic continuum is considered. A general continuous density-energy relation is assumed, including variable thickness sheet models and artificial power laws. To ensure existence of solutions, the design set is restricted by enforcing...

  2. Constraint-Based Local Search for Constrained Optimum Paths Problems

    Science.gov (United States)

    Pham, Quang Dung; Deville, Yves; van Hentenryck, Pascal

    Constrained Optimum Path (COP) problems arise in many real-life applications and are ubiquitous in communication networks. They have been traditionally approached by dedicated algorithms, which are often hard to extend with side constraints and to apply widely. This paper proposes a constraint-based local search (CBLS) framework for COP applications, bringing the compositionality, reuse, and extensibility at the core of CBLS and CP systems. The modeling contribution is the ability to express compositional models for various COP applications at a high level of abstraction, while cleanly separating the model and the search procedure. The main technical contribution is a connected neighborhood based on rooted spanning trees to find high-quality solutions to COP problems. The framework, implemented in COMET, is applied to Resource Constrained Shortest Path (RCSP) problems (with and without side constraints) and to the edge-disjoint paths problem (EDP). Computational results show the potential significance of the approach.

  3. An Optimization-Based Impedance Approach for Robot Force Regulation with Prescribed Force Limits

    Directory of Open Access Journals (Sweden)

    R. de J. Portillo-Vélez

    2015-01-01

    Full Text Available An optimization based approach for the regulation of excessive or insufficient forces at the end-effector level is introduced. The objective is to minimize the interaction force error at the robot end effector, while constraining undesired interaction forces. To that end, a dynamic optimization problem (DOP is formulated considering a dynamic robot impedance model. Penalty functions are considered in the DOP to handle the constraints on the interaction force. The optimization problem is online solved through the gradient flow approach. Convergence properties are presented and the stability is drawn when the force limits are considered in the analysis. The effectiveness of our proposal is validated via experimental results for a robotic grasping task.

  4. Improved helicopter aeromechanical stability analysis using segmented constrained layer damping and hybrid optimization

    Science.gov (United States)

    Liu, Qiang; Chattopadhyay, Aditi

    2000-06-01

    Aeromechanical stability plays a critical role in helicopter design and lead-lag damping is crucial to this design. In this paper, the use of segmented constrained damping layer (SCL) treatment and composite tailoring is investigated for improved rotor aeromechanical stability using formal optimization technique. The principal load-carrying member in the rotor blade is represented by a composite box beam, of arbitrary thickness, with surface bonded SCLs. A comprehensive theory is used to model the smart box beam. A ground resonance analysis model and an air resonance analysis model are implemented in the rotor blade built around the composite box beam with SCLs. The Pitt-Peters dynamic inflow model is used in air resonance analysis under hover condition. A hybrid optimization technique is used to investigate the optimum design of the composite box beam with surface bonded SCLs for improved damping characteristics. Parameters such as stacking sequence of the composite laminates and placement of SCLs are used as design variables. Detailed numerical studies are presented for aeromechanical stability analysis. It is shown that optimum blade design yields significant increase in rotor lead-lag regressive modal damping compared to the initial system.

  5. A novel constrained H2 optimization algorithm for mechatronics design in flexure-linked biaxial gantry.

    Science.gov (United States)

    Ma, Jun; Chen, Si-Lu; Kamaldin, Nazir; Teo, Chek Sing; Tay, Arthur; Mamun, Abdullah Al; Tan, Kok Kiong

    2017-11-01

    The biaxial gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers from breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a flexure-linked biaxial gantry is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end-effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-to-point reference trajectories with same dynamics but different steps. From here, an H 2 optimization problem with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal step. Comparative experimental results obtained on the testbed are presented to verify the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Optimizing color reproduction of natural images

    NARCIS (Netherlands)

    Yendrikhovskij, S.N.; Blommaert, F.J.J.; Ridder, de H.

    1998-01-01

    The paper elaborates on understanding, measuring and optimizing perceived color quality of natural images. We introduce a model for optimal color reproduction of natural scenes which is based on the assumption that color quality of natural images is constrained by perceived naturalness and

  7. Security constrained optimal power flow by modern optimization tools

    African Journals Online (AJOL)

    The main objective of an optimal power flow (OPF) functions is to optimize .... It is characterized as propagation of plants and this happens by gametes union. ... ss and different variables, for example, wind, nearby fertilization can have a critic.

  8. Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

    International Nuclear Information System (INIS)

    Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

    2014-01-01

    Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

  9. Point-based warping with optimized weighting factors of displacement vectors

    Science.gov (United States)

    Pielot, Ranier; Scholz, Michael; Obermayer, Klaus; Gundelfinger, Eckart D.; Hess, Andreas

    2000-06-01

    The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.

  10. Constrained Optimization of MIMO Training Sequences

    Directory of Open Access Journals (Sweden)

    Coon Justin P

    2007-01-01

    Full Text Available Multiple-input multiple-output (MIMO systems have shown a huge potential for increased spectral efficiency and throughput. With an increasing number of transmitting antennas comes the burden of providing training for channel estimation for coherent detection. In some special cases optimal, in the sense of mean-squared error (MSE, training sequences have been designed. However, in many practical systems it is not feasible to analytically find optimal solutions and numerical techniques must be used. In this paper, two systems (unique word (UW single carrier and OFDM with nulled subcarriers are considered and a method of designing near-optimal training sequences using nonlinear optimization techniques is proposed. In particular, interior-point (IP algorithms such as the barrier method are discussed. Although the two systems seem unrelated, the cost function, which is the MSE of the channel estimate, is shown to be effectively the same for each scenario. Also, additional constraints, such as peak-to-average power ratio (PAPR, are considered and shown to be easily included in the optimization process. Numerical examples illustrate the effectiveness of the designed training sequences, both in terms of MSE and bit-error rate (BER.

  11. A brief introduction to continuous evolutionary optimization

    CERN Document Server

    Kramer, Oliver

    2014-01-01

    Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

  12. Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

    Science.gov (United States)

    Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

    2016-07-15

    Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Reliability based topology optimization for continuum structures with local failure constraints

    DEFF Research Database (Denmark)

    Luo, Yangjun; Zhou, Mingdong; Wang, Michael Yu

    2014-01-01

    This paper presents an effective method for stress constrained topology optimization problems under load and material uncertainties. Based on the Performance Measure Approach (PMA), the optimization problem is formulated as to minimize the objective function under a large number of (stress......-related) target performance constraints. In order to overcome the stress singularity phenomenon caused by the combined stress and reliability constraints, a reduction strategy on target reliability index is proposed and utilized together with the ε-relaxation approach. Meanwhile, an enhanced aggregation method...... is employed to aggregate the selected active constraints using a general K–S function, which avoids expensive computational cost from the large-scale nature of local failure constraints. Several numerical examples are given to demonstrate the validity of the present method....

  14. Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

    International Nuclear Information System (INIS)

    Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

    2010-01-01

    The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

  15. Deployment-based lifetime optimization model for homogeneous Wireless Sensor Network under retransmission.

    Science.gov (United States)

    Li, Ruiying; Liu, Xiaoxi; Xie, Wei; Huang, Ning

    2014-12-10

    Sensor-deployment-based lifetime optimization is one of the most effective methods used to prolong the lifetime of Wireless Sensor Network (WSN) by reducing the distance-sensitive energy consumption. In this paper, data retransmission, a major consumption factor that is usually neglected in the previous work, is considered. For a homogeneous WSN, monitoring a circular target area with a centered base station, a sensor deployment model based on regular hexagonal grids is analyzed. To maximize the WSN lifetime, optimization models for both uniform and non-uniform deployment schemes are proposed by constraining on coverage, connectivity and success transmission rate. Based on the data transmission analysis in a data gathering cycle, the WSN lifetime in the model can be obtained through quantifying the energy consumption at each sensor location. The results of case studies show that it is meaningful to consider data retransmission in the lifetime optimization. In particular, our investigations indicate that, with the same lifetime requirement, the number of sensors needed in a non-uniform topology is much less than that in a uniform one. Finally, compared with a random scheme, simulation results further verify the advantage of our deployment model.

  16. Pole shifting with constrained output feedback

    International Nuclear Information System (INIS)

    Hamel, D.; Mensah, S.; Boisvert, J.

    1984-03-01

    The concept of pole placement plays an important role in linear, multi-variable, control theory. It has received much attention since its introduction, and several pole shifting algorithms are now available. This work presents a new method which allows practical and engineering constraints such as gain limitation and controller structure to be introduced right into the pole shifting design strategy. This is achieved by formulating the pole placement problem as a constrained optimization problem. Explicit constraints (controller structure and gain limits) are defined to identify an admissible region for the feedback gain matrix. The desired pole configuration is translated into an appropriate cost function which must be closed-loop minimized. The resulting constrained optimization problem can thus be solved with optimization algorithms. The method has been implemented as an algorithmic interactive module in a computer-aided control system design package, MVPACK. The application of the method is illustrated to design controllers for an aircraft and an evaporator. The results illustrate the importance of controller structure on overall performance of a control system

  17. Application of pattern search method to power system security constrained economic dispatch with non-smooth cost function

    International Nuclear Information System (INIS)

    Al-Othman, A.K.; El-Naggar, K.M.

    2008-01-01

    Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED) with non-smooth cost function. Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using three different test systems. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED). In addition, valve-point effect loading and total system losses are considered to further investigate the potential of the PS technique. Based on the results, it can be concluded that the PS has demonstrated ability in handling highly nonlinear discontinuous non-smooth cost function of the SCED. (author)

  18. Evaluating potentialities and constrains of Problem Based Learning curriculum

    DEFF Research Database (Denmark)

    Guerra, Aida

    2013-01-01

    This paper presents a research design to evaluate Problem Based Learning (PBL) curriculum potentialities and constrains for future changes. PBL literature lacks examples of how to evaluate and analyse established PBL learning environments to address new challenges posed. The research design......) in the curriculum and a mean to choose cases for further case study (third phase)....

  19. MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

    Energy Technology Data Exchange (ETDEWEB)

    Liu, X; Belcher, AH; Wiersma, R [The University of Chicago, Chicago, IL (United States)

    2016-06-15

    Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimization and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also

  20. Constrained Dynamic Optimality and Binomial Terminal Wealth

    DEFF Research Database (Denmark)

    Pedersen, J. L.; Peskir, G.

    2018-01-01

    with interest rate $r \\in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\\inf_u\\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \\ge e...... a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast...... with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem....

  1. Minimum Time Trajectory Optimization of CNC Machining with Tracking Error Constraints

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2014-01-01

    Full Text Available An off-line optimization approach of high precision minimum time feedrate for CNC machining is proposed. Besides the ordinary considered velocity, acceleration, and jerk constraints, dynamic performance constraint of each servo drive is also considered in this optimization problem to improve the tracking precision along the optimized feedrate trajectory. Tracking error is applied to indicate the servo dynamic performance of each axis. By using variable substitution, the tracking error constrained minimum time trajectory planning problem is formulated as a nonlinear path constrained optimal control problem. Bang-bang constraints structure of the optimal trajectory is proved in this paper; then a novel constraint handling method is proposed to realize a convex optimization based solution of the nonlinear constrained optimal control problem. A simple ellipse feedrate planning test is presented to demonstrate the effectiveness of the approach. Then the practicability and robustness of the trajectory generated by the proposed approach are demonstrated by a butterfly contour machining example.

  2. A Quasi-Dynamic Optimal Control Strategy for Non-Linear Multivariable Processes Based upon Non-Quadratic Objective Functions

    Directory of Open Access Journals (Sweden)

    Jens G. Balchen

    1984-10-01

    Full Text Available The problem of systematic derivation of a quasi-dynamic optimal control strategy for a non-linear dynamic process based upon a non-quadratic objective function is investigated. The wellknown LQG-control algorithm does not lead to an optimal solution when the process disturbances have non-zero mean. The relationships between the proposed control algorithm and LQG-control are presented. The problem of how to constrain process variables by means of 'penalty' - terms in the objective function is dealt with separately.

  3. Assessment of electricity demand-supply in health facilities in resource-constrained settings : optimization and evaluation of energy systems for a case in Rwanda

    NARCIS (Netherlands)

    Palacios, S.G.

    2015-01-01

    In health facilities in resource-constrained settings, a lack of access to sustainable and reliable electricity can result on a sub-optimal delivery of healthcare services, as they do not have lighting for medical procedures and power to run essential equipment and devices to treat their patients.

  4. A RSSI-based parameter tracking strategy for constrained position localization

    Science.gov (United States)

    Du, Jinze; Diouris, Jean-François; Wang, Yide

    2017-12-01

    In this paper, a received signal strength indicator (RSSI)-based parameter tracking strategy for constrained position localization is proposed. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. In the context of applications where the positions are constrained on a grid, a novel tracking strategy is proposed to determine the real position and obtain the actual parameters in the monitored region. Based on practical data acquired from a real localization system, an experimental channel model is constructed to provide RSSI values and verify the proposed tracking strategy. Quantitative criteria are given to guarantee the efficiency of the proposed tracking strategy by providing a trade-off between the grid resolution and parameter variation. The simulation results show a good behavior of the proposed tracking strategy in the presence of space-time variation of the propagation channel. Compared with the existing RSSI-based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. In addition, a tracking test is performed to validate the effectiveness of the proposed tracking strategy.

  5. Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

    Science.gov (United States)

    Regis, Rommel G.

    2014-02-01

    This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

  6. A one-layer recurrent neural network for constrained nonconvex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2015-01-01

    In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

  7. Constrained optimal motion planning for autonomous vehicles using PRONTO

    NARCIS (Netherlands)

    Aguiar, A.P.; Bayer, F.A.; Hauser, J.; Häusler, A.J.; Notarstefano, G.; Pascoal, A.M.; Rucco, A.; Saccon, A.

    2017-01-01

    This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO, a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references

  8. Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

    Science.gov (United States)

    Bistra Dilkina; Rachel Houtman; Carla P. Gomes; Claire A. Montgomery; Kevin S. McKelvey; Katherine Kendall; Tabitha A. Graves; Richard Bernstein; Michael K. Schwartz

    2016-01-01

    Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a...

  9. Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

    Science.gov (United States)

    Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

    2011-02-01

    This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

  10. Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

    Directory of Open Access Journals (Sweden)

    Tae-Hyoung Kim

    2017-01-01

    Full Text Available This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

  11. Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

    Science.gov (United States)

    Massioni, Paolo; Massari, Mauro

    2018-05-01

    This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as "Sum Of Squares" (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.

  12. Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

    Directory of Open Access Journals (Sweden)

    Longfei He

    2014-01-01

    Full Text Available We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optimization algorithm to obtain joint optimal production quantities combination for maximizing overall profit under regulatory policies, respectively. Furthermore, numerical studies by featuring exponentially distributed demand compare systemwide performances in various scenarios. We build the “carbon emission elasticity of profit (CEEP” index as a metric to evaluate the impact of regulatory policies on both chainwide emissions and profit. Our results manifest that by facilitating the mandatory emission cap in proper installation within the network one can balance well effective emission reduction and associated acceptable profit loss. The outcome that CEEP index when implementing Carbon emission tax is elastic implies that the scale of profit loss is greater than that of emission reduction, which shows that this policy is less effective than mandatory cap from industry standpoint at least.

  13. Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

    Directory of Open Access Journals (Sweden)

    Dongshuang Li

    2018-05-01

    Full Text Available The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China’s road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.

  14. SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

    Energy Technology Data Exchange (ETDEWEB)

    Chen, T; Yue, N; Jabbour, S; Zhang, M [Rutgers University, New Brunswick, NJ (United States)

    2016-06-15

    Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components that were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy

  15. SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

    International Nuclear Information System (INIS)

    Chen, T; Yue, N; Jabbour, S; Zhang, M

    2016-01-01

    Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components that were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy

  16. Kinetic Constrained Optimization of the Golf Swing Hub Path

    Directory of Open Access Journals (Sweden)

    Steven M. Nesbit

    2014-12-01

    Full Text Available This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study.

  17. The equivalence of multi-criteria methods for radiotherapy plan optimization

    International Nuclear Information System (INIS)

    Breedveld, Sebastiaan; Storchi, Pascal R M; Heijmen, Ben J M

    2009-01-01

    Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2pεc (2-phase ε-constraint) method is based on the ε-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.

  18. Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model

    Directory of Open Access Journals (Sweden)

    Zhenggang Du

    2015-03-01

    Full Text Available To improve models for accurate projections, data assimilation, an emerging statistical approach to combine models with data, have recently been developed to probe initial conditions, parameters, data content, response functions and model uncertainties. Quantifying how many information contents are contained in different data streams is essential to predict future states of ecosystems and the climate. This study uses a data assimilation approach to examine the information contents contained in flux- and biometric-based data to constrain parameters in a terrestrial carbon (C model, which includes canopy photosynthesis and vegetation–soil C transfer submodels. Three assimilation experiments were constructed with either net ecosystem exchange (NEE data only or biometric data only [including foliage and woody biomass, litterfall, soil organic C (SOC and soil respiration], or both NEE and biometric data to constrain model parameters by a probabilistic inversion application. The results showed that NEE data mainly constrained parameters associated with gross primary production (GPP and ecosystem respiration (RE but were almost invalid for C transfer coefficients, while biometric data were more effective in constraining C transfer coefficients than other parameters. NEE and biometric data constrained about 26% (6 and 30% (7 of a total of 23 parameters, respectively, but their combined application constrained about 61% (14 of all parameters. The complementarity of NEE and biometric data was obvious in constraining most of parameters. The poor constraint by only NEE or biometric data was probably attributable to either the lack of long-term C dynamic data or errors from measurements. Overall, our results suggest that flux- and biometric-based data, containing different processes in ecosystem C dynamics, have different capacities to constrain parameters related to photosynthesis and C transfer coefficients, respectively. Multiple data sources could also

  19. An Experimental Comparison of Similarity Assessment Measures for 3D Models on Constrained Surface Deformation

    Science.gov (United States)

    Quan, Lulin; Yang, Zhixin

    2010-05-01

    To address the issues in the area of design customization, this paper expressed the specification and application of the constrained surface deformation, and reported the experimental performance comparison of three prevail effective similarity assessment algorithms on constrained surface deformation domain. Constrained surface deformation becomes a promising method that supports for various downstream applications of customized design. Similarity assessment is regarded as the key technology for inspecting the success of new design via measuring the difference level between the deformed new design and the initial sample model, and indicating whether the difference level is within the limitation. According to our theoretical analysis and pre-experiments, three similarity assessment algorithms are suitable for this domain, including shape histogram based method, skeleton based method, and U system moment based method. We analyze their basic functions and implementation methodologies in detail, and do a series of experiments on various situations to test their accuracy and efficiency using precision-recall diagram. Shoe model is chosen as an industrial example for the experiments. It shows that shape histogram based method gained an optimal performance in comparison. Based on the result, we proposed a novel approach that integrating surface constrains and shape histogram description with adaptive weighting method, which emphasize the role of constrains during the assessment. The limited initial experimental result demonstrated that our algorithm outperforms other three algorithms. A clear direction for future development is also drawn at the end of the paper.

  20. Evolutionary optimization methods for accelerator design

    Science.gov (United States)

    Poklonskiy, Alexey A.

    Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

  1. Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

    Science.gov (United States)

    Omagari, Hiroki; Higashino, Shin-Ichiro

    2018-04-01

    In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

  2. Distributed Constrained Stochastic Subgradient Algorithms Based on Random Projection and Asynchronous Broadcast over Networks

    Directory of Open Access Journals (Sweden)

    Junlong Zhu

    2017-01-01

    Full Text Available We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.

  3. 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.

  4. Transmission tariffs based on optimal power flow

    International Nuclear Information System (INIS)

    Wangensteen, Ivar; Gjelsvik, Anders

    1998-01-01

    This report discusses transmission pricing as a means of obtaining optimal scheduling and dispatch in a power system. This optimality includes consumption as well as generation. The report concentrates on how prices can be used as signals towards operational decisions of market participants (generators, consumers). The main focus is on deregulated systems with open access to the network. The optimal power flow theory, with demand side modelling included, is briefly reviewed. It turns out that the marginal costs obtained from the optimal power flow gives the optimal transmission tariff for the particular load flow in case. There is also a correspondence between losses and optimal prices. Emphasis is on simple examples that demonstrate the connection between optimal power flow results and tariffs. Various cases, such as open access and single owner are discussed. A key result is that the location of the ''marketplace'' in the open access case does not influence the net economical result for any of the parties involved (generators, network owner, consumer). The optimal power flow is instantaneous, and in its standard form cannot deal with energy constrained systems that are coupled in time, such as hydropower systems with reservoirs. A simplified example of how the theory can be extended to such a system is discussed. An example of the influence of security constraints on prices is also given. 4 refs., 24 figs., 7 tabs

  5. Optimal design and selection of magneto-rheological brake types based on braking torque and mass

    International Nuclear Information System (INIS)

    Nguyen, Q H; Lang, V T; Choi, S B

    2015-01-01

    In developing magnetorheological brakes (MRBs), it is well known that the braking torque and the mass of the MRBs are important factors that should be considered in the product’s design. This research focuses on the optimal design of different types of MRBs, from which we identify an optimal selection of MRB types, considering braking torque and mass. In the optimization, common types of MRBs such as disc-type, drum-type, hybrid-type, and T-shape types are considered. The optimization problem is to find an optimal MRB structure that can produce the required braking torque while minimizing its mass. After a brief description of the configuration of the MRBs, the MRBs’ braking torque is derived based on the Herschel-Bulkley rheological model of the magnetorheological fluid. Then, the optimal designs of the MRBs are analyzed. The optimization objective is to minimize the mass of the brake while the braking torque is constrained to be greater than a required value. In addition, the power consumption of the MRBs is also considered as a reference parameter in the optimization. A finite element analysis integrated with an optimization tool is used to obtain optimal solutions for the MRBs. Optimal solutions of MRBs with different required braking torque values are obtained based on the proposed optimization procedure. From the results, we discuss the optimal selection of MRB types, considering braking torque and mass. (technical note)

  6. A non-penalty recurrent neural network for solving a class of constrained optimization problems.

    Science.gov (United States)

    Hosseini, Alireza

    2016-01-01

    In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Constraining neutron guide optimizations with phase-space considerations

    Energy Technology Data Exchange (ETDEWEB)

    Bertelsen, Mads, E-mail: mads.bertelsen@gmail.com; Lefmann, Kim

    2016-09-11

    We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

  8. TH-CD-209-06: LET-Based Adjustment of IMPT Plans Using Prioritized Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Unkelbach, J; Giantsoudi, D; Paganetti, H [Massachusetts General Hospital, Boston, MA (United States); Botas, P [Massachusetts General Hospital, Boston, MA (United States); Heidelberg University, Heidelberg, DE (Germany); Qin, N; Jia, X [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)

    2016-06-15

    Purpose: In-vitro experiments suggest an increase in proton relative biological effectiveness (RBE) towards the end of range. However, proton treatment planning and dose reporting for clinical outcome assessment has been based on physical dose and constant RBE. Therefore, treatment planning for intensity-modulated proton therapy (IMPT) is unlikely to transition radically to pure RBE-based planning. We suggest a hybrid approach where treatment plans are initially created based on physical dose constraints and prescriptions, and are subsequently altered to avoid high linear energy transfer (LET) in critical structures while limiting the degradation of the physical dose distribution. Methods: To allow fast optimization based on dose and LET we extended a GPU-based Monte-Carlo code towards providing dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of physical dose and LET (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Regarding optimization techniques, LETxD has the advantage of being a linear function of the pencil beam intensities. Results: The method is applicable to treatments where serial critical structures with maximum dose constraint are located in or near the target. We studied intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas) where the target (CTV) overlaps with the brainstem and optic structures. Often, high LETxD in critical structures can be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based re-optimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based

  9. Improved solution for ill-posed linear systems using a constrained optimization ruled by a penalty: evaluation in nuclear medicine tomography

    International Nuclear Information System (INIS)

    Walrand, Stephan; Jamar, François; Pauwels, Stanislas

    2009-01-01

    Ill-posed linear systems occur in many different fields. A class of regularization methods, called constrained optimization, aims to determine the extremum of a penalty function whilst constraining an objective function to a likely value. We propose here a novel heuristic way to screen the local extrema satisfying the discrepancy principle. A modified version of the Landweber algorithm is used for the iteration process. After finding a local extremum, a bound is performed to the 'farthest' estimate in the data space still satisfying the discrepancy principle. Afterwards, the modified Landweber algorithm is again applied to find a new local extremum. This bound-iteration process is repeated until a satisfying solution is reached. For evaluation in nuclear medicine tomography, a novel penalty function that preserves the edge steps in the reconstructed solution was evaluated on Monte Carlo simulations and using real SPECT acquisitions as well. Surprisingly, the first bound always provided a significantly better solution in a wide range of statistics

  10. Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe

    2010-01-01

    determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output...... limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...... setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system....

  11. 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.

  12. On the sensitivity of IMRT dose optimization to the mathematical form of a biological imaging-based prescription function

    International Nuclear Information System (INIS)

    Bowen, Stephen R; Bentzen, Soeren M; Jeraj, Robert; Flynn, Ryan T

    2009-01-01

    Voxel-based prescriptions of deliberately non-uniform dose distributions based on molecular imaging, so-called dose painting or theragnostic radiation therapy, require specification of a transformation that maps the image data intensities to prescribed doses. However, the functional form of this transformation is currently unknown. An investigation into the sensitivity of optimized dose distributions resulting from several possible prescription functions was conducted. Transformations between the radiotracer activity concentrations from Cu-ATSM PET images, as a surrogate of tumour hypoxia, and dose prescriptions were implemented to yield weighted distributions of prescribed dose boosts in high uptake regions. Dose escalation was constrained to reflect clinically realistic whole tumour doses and constant normal tissue doses. Optimized heterogeneous dose distributions were found by minimizing a voxel-by-voxel quadratic objective function in which all tumour voxels were given equal weight. Prescriptions based on a polynomial mapping function were found to be least constraining on their optimized plans, while prescriptions based on a sigmoid mapping function were the most demanding to deliver. A prescription formalism that fixed integral dose was less sensitive to errors in the choice of the mapping function than one that boosted integral dose. Integral doses to normal tissue and critical structures were insensitive to the shape of the prescription function. Planned target dose conformity improved with smaller beamlet dimensions until the inherent spatial resolution of the functional image was matched. Clinical implementation of dose painting depends on advances in absolute quantification of functional images and improvements in delivery techniques over smaller spatial scales.

  13. Chance-constrained programming approach to natural-gas curtailment decisions

    Energy Technology Data Exchange (ETDEWEB)

    Guldmann, J M

    1981-10-01

    This paper presents a modeling methodology for the determination of optimal-curtailment decisions by a gas-distribution utility during a chronic gas-shortage situation. Based on the end-use priority approach, a linear-programming model is formulated, that reallocates the available gas supply among the utility's customers while minimizing fuel switching, unemployment, and utility operating costs. This model is then transformed into a chance-constrained program in order to account for the weather-related variability of the gas requirements. The methodology is applied to the East Ohio Gas Company. 16 references, 2 figures, 3 tables.

  14. Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

    Science.gov (United States)

    Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

    2018-06-01

    Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

  15. The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Denis Pinha

    2016-11-01

    Full Text Available This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.

  16. Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco

    OpenAIRE

    Kerschke, Pascal

    2017-01-01

    Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) - a sophisticated and effective approach for characterizing the landscapes of such problems by means of numeric...

  17. Optimization and optimal control in automotive systems

    CERN Document Server

    Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi

    2014-01-01

    This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier  approaches, based on some degree of heuristics, to the use of  more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...

  18. Analysis in nuclear power accident emergency based on random network and particle swarm optimization

    International Nuclear Information System (INIS)

    Gong Dichen; Fang Fang; Ding Weicheng; Chen Zhi

    2014-01-01

    The GERT random network model of nuclear power accident emergency was built in this paper, and the intelligent computation was combined with the random network based on the analysis of Fukushima nuclear accident in Japan. The emergency process was divided into the series link and parallel link, and the parallel link was the part of series link. The overall allocation of resources was firstly optimized, and then the parallel link was analyzed. The effect of the resources for emergency used in different links was analyzed, and it was put forward that the corresponding particle velocity vector was limited under the condition of limited emergency resources. The resource-constrained particle swarm optimization was obtained by using velocity projection matrix to correct the motion of particles. The optimized allocation of resources in emergency process was obtained and the time consumption of nuclear power accident emergency was reduced. (authors)

  19. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  20. On energy efficient power allocation for power-constrained systems

    KAUST Repository

    Sboui, Lokman

    2014-09-01

    Recently, the energy efficiency (EE) has become an important factor when designing new wireless communication systems. Due to economic and environmental challenges, new trends and efforts are oriented toward “green” communication especially for energy-constrained applications such as wireless sensors network and cognitive radio. To this end, we analyze the power allocation scheme that maximizes the EE defined as rate over the total power including circuit power. We derive an explicit expression of the optimal power with instantaneous channel gain based on EE criterion. We show that the relation between the EE and the spectral efficiency (SE) when the optimal power is adopted is strictly increasing in contrast with the SE-EE trade-off discussed in the literature. We also solve a non-convex problem and compute explicitly the optimal power for ergodic EE under either a peak or an average power constraint. When the instantaneous channel is not available, we provide the optimal power equation and compute simple sub-optimal power. In the numerical results, we show that the sup-optimal solution is very close to the optimal solution. In addition, we show that the absence of the channel state information (CSI) only affects the EE and the SE performances at high power regime compared to the full CSI case.

  1. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

    Science.gov (United States)

    Lin, Yu-Hsiu; Hu, Yu-Chen

    2018-04-27

    The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved

  2. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

    Science.gov (United States)

    Hu, Yu-Chen

    2018-01-01

    The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved

  3. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

    Directory of Open Access Journals (Sweden)

    Yu-Hsiu Lin

    2018-04-01

    Full Text Available The emergence of smart Internet of Things (IoT devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power

  4. An analytical method for optimal design of MR valve structures

    International Nuclear Information System (INIS)

    Nguyen, Q H; Choi, S B; Lee, Y S; Han, M S

    2009-01-01

    This paper proposes an analytical methodology for the optimal design of a magnetorheological (MR) valve structure. The MR valve structure is constrained in a specific volume and the optimization problem identifies geometric dimensions of the valve structure that maximize the yield stress pressure drop of a MR valve or the yield stress damping force of a MR damper. In this paper, the single-coil and two-coil annular MR valve structures are considered. After describing the schematic configuration and operating principle of a typical MR valve and damper, a quasi-static model is derived based on the Bingham model of a MR fluid. The magnetic circuit of the valve and damper is then analyzed by applying Kirchoff's law and the magnetic flux conservation rule. Based on quasi-static modeling and magnetic circuit analysis, the optimization problem of the MR valve and damper is built. In order to reduce the computation load, the optimization problem is simplified and a procedure to obtain the optimal solution of the simplified optimization problem is presented. The optimal solution of the simplified optimization problem of the MR valve structure constrained in a specific volume is then obtained and compared with the solution of the original optimization problem and the optimal solution obtained from the finite element method

  5. Production Optimization for Two-Phase Flow in an Oil Reservoir

    DEFF Research Database (Denmark)

    Völcker, Carsten; Jørgensen, John Bagterp; Thomsen, Per Grove

    2012-01-01

    framework to increase the production and economic value of an oil reservoir. Wether the objective is to maximize recovery or some financial measure like Net Present Value, the increased production is achieved by manipulation of the well rates and bottom-hole pressures of the injection and production wells....... The optimal water injection rates and production well bottom-hole pressures are computed by solution of a large-scale constrained optimal control problem. The objective is to maximize production by manipulating the well rates and bottom hole pressures of injection and production wells. Optimal control...... settings of injection and production wells are computed by solution of a large scale constrained optimal control problem. We describe a gradient based method to compute the optimal control strategy of the water flooding process. An explicit singly diagonally implicit Runge-Kutta (ESDIRK) method...

  6. Efficient non-negative constrained model-based inversion in optoacoustic tomography

    International Nuclear Information System (INIS)

    Ding, Lu; Luís Deán-Ben, X; Lutzweiler, Christian; Razansky, Daniel; Ntziachristos, Vasilis

    2015-01-01

    The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency. (paper)

  7. Sequential unconstrained minimization algorithms for constrained optimization

    International Nuclear Information System (INIS)

    Byrne, Charles

    2008-01-01

    The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results

  8. Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

    Science.gov (United States)

    Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

    2018-02-01

    The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

  9. Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept.

    Science.gov (United States)

    Shi, Chenguang; Wang, Fei; Salous, Sana; Zhou, Jianjiang

    2017-11-25

    In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme.

  10. Design Optimization Toolkit: Users' Manual

    Energy Technology Data Exchange (ETDEWEB)

    Aguilo Valentin, Miguel Alejandro [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Solid Mechanics and Structural Dynamics

    2014-07-01

    The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLAB command window.

  11. Application of a Double-Sided Chance-Constrained Integer Linear Program for Optimization of the Incremental Value of Ecosystem Services in Jilin Province, China

    Directory of Open Access Journals (Sweden)

    Baofeng Cai

    2017-08-01

    Full Text Available The Interconnected River System Network Project (IRSNP is a significant water supply engineering project, which is capable of effectively utilizing flood resources to generate ecological value, by connecting 198 lakes and ponds in western Jilin, northeast China. In this article, an optimization research approach has been proposed to maximize the incremental value of IRSNP ecosystem services. A double-sided chance-constrained integer linear program (DCCILP method has been proposed to support the optimization, which can deal with uncertainties presented as integers or random parameters that appear on both sides of the decision variable at the same time. The optimal scheme indicates that after rational optimization, the total incremental value of ecosystem services from the interconnected river system network project increased 22.25%, providing an increase in benefits of 3.26 × 109 ¥ compared to the original scheme. Most of the functional area is swamp wetland, which provides the greatest ecological benefits. Adjustment services increased obviously, implying that the optimization scheme prioritizes ecological benefits rather than supply and production services.

  12. Optimal design of MR shock absorber and application to vehicle suspension

    International Nuclear Information System (INIS)

    Nguyen, Quoc-Hung; Choi, Seung-Bok

    2009-01-01

    This paper presents an optimal design of a magnetorheological (MR) shock absorber based on finite element analysis. The MR shock absorber is constrained in a specific volume and the optimization problem identifies geometric dimensions of the shock absorber that minimize a multi-objective function. The objective function is proposed by considering the damping force, dynamic range and the inductive time constant of the shock absorber. After describing the configuration of the MR shock absorber, a quasi-static modeling of the shock absorber is performed based on the Bingham model of an MR fluid. The initial geometric dimensions of the shock absorber are then determined based on the assumption of constant magnetic flux density throughout the magnetic circuit. The objective function of the optimization problem is derived based on the solution of the initial shock absorber. An optimization procedure using a golden-section algorithm and a local quadratic fitting technique is constructed via a commercial finite element method parametric design language. Using the developed optimization tool, optimal solutions of the MR shock absorber, which is constrained in a specific cylindrical volume defined by its radius and height, are determined. Subsequently, a quarter-car suspension model with the optimized MR shock absorber is formulated and the vibration control performance of the suspension is evaluated under bump and sinusoidal road conditions

  13. Particle Swarm Optimization

    Science.gov (United States)

    Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

    2002-01-01

    The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

  14. Multivariable controller for discrete stochastic amplitude-constrained systems

    Directory of Open Access Journals (Sweden)

    Hannu T. Toivonen

    1983-04-01

    Full Text Available A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.

  15. PSO Based Optimization of Testing and Maintenance Cost in NPPs

    Directory of Open Access Journals (Sweden)

    Qiang Chou

    2014-01-01

    Full Text Available Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT, maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.

  16. Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid

    Directory of Open Access Journals (Sweden)

    Kai Ma

    2017-10-01

    Full Text Available Demand-side energy management is used for regulating the consumers’ energy usage in smart grid. With the guidance of the grid’s price policy, the consumers can change their energy consumption in response. The objective of this study is jointly optimizing the load status and electric supply, in order to make a tradeoff between the electric cost and the thermal comfort. The problem is formulated into a nonconvex optimization model. The multiplier method is used to solve the constrained optimization, and the objective function is transformed to the augmented Lagrangian function without constraints. Hence, the Powell direction acceleration method with advance and retreat is applied to solve the unconstrained optimization. Numerical results show that the proposed algorithm can achieve the balance between the electric supply and demand, and the optimization variables converge to the optimum.

  17. Constraining neutrinoless double beta decay

    International Nuclear Information System (INIS)

    Dorame, L.; Meloni, D.; Morisi, S.; Peinado, E.; Valle, J.W.F.

    2012-01-01

    A class of discrete flavor-symmetry-based models predicts constrained neutrino mass matrix schemes that lead to specific neutrino mass sum-rules (MSR). We show how these theories may constrain the absolute scale of neutrino mass, leading in most of the cases to a lower bound on the neutrinoless double beta decay effective amplitude.

  18. Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

    Directory of Open Access Journals (Sweden)

    Yongpeng Shen

    2016-02-01

    Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

  19. Binary classification posed as a quadratically constrained quadratic ...

    Indian Academy of Sciences (India)

    Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or ...

  20. Price-based Optimal Control of Electrical Power Systems

    Energy Technology Data Exchange (ETDEWEB)

    Jokic, A.

    2007-09-10

    The research presented in this thesis is motivated by the following issue of concern for the operation of future power systems: Future power systems will be characterized by significantly increased uncertainties at all time scales and, consequently, their behavior in time will be difficult to predict. In Chapter 2 we will present a novel explicit, dynamic, distributed feedback control scheme that utilizes nodal-prices for real-time optimal power balance and network congestion control. The term explicit means that the controller is not based on solving an optimization problem on-line. Instead, the nodal prices updates are based on simple, explicitly defined and easily comprehensible rules. We prove that the developed control scheme, which acts on the measurements from the current state of the system, always provide the correct nodal prices. In Chapter 3 we will develop a novel, robust, hybrid MPC control (model predictive controller) scheme for power balance control with hard constraints on line power flows and network frequency deviations. The developed MPC controller acts in parallel with the explicit controller from Chapter 2, and its task is to enforce the constraints during the transient periods following suddenly occurring power imbalances in the system. In Chapter 4 the concept of autonomous power networks will be presented as a concise formulation to deal with economic, technical and reliability issues in power systems with a large penetration of distributed generating units. With autonomous power networks as new market entities, we propose a novel operational structure of ancillary service markets. In Chapter 5 we will consider the problem of controlling a general linear time-invariant dynamical system to an economically optimal operating point, which is defined by a multiparametric constrained convex optimization problem related with the steady-state operation of the system. The parameters in the optimization problem are values of the exogenous inputs to

  1. Clustering Using Boosted Constrained k-Means Algorithm

    Directory of Open Access Journals (Sweden)

    Masayuki Okabe

    2018-03-01

    Full Text Available This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learning-based methods. Since it simply adds a function into the data assignment process of the k-means algorithm to check for constraint violations, it often exploits only a small number of constraints. Metric learning-based methods, which exploit constraints to create a new metric for data similarity, have shown promising results although the methods proposed so far are often slow depending on the amount of data or number of feature dimensions. We present a method that exploits the advantages of the constrained k-means and metric learning approaches. It incorporates a mechanism for accepting constraint priorities and a metric learning framework based on the boosting principle into a constrained k-means algorithm. In the framework, a metric is learned in the form of a kernel matrix that integrates weak cluster hypotheses produced by the constrained k-means algorithm, which works as a weak learner under the boosting principle. Experimental results for 12 data sets from 3 data sources demonstrated that our method has performance competitive to those of state-of-the-art constrained clustering methods for most data sets and that it takes much less computation time. Experimental evaluation demonstrated the effectiveness of controlling the constraint priorities by using the boosting principle and that our constrained k-means algorithm functions correctly as a weak learner of boosting.

  2. Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

    Directory of Open Access Journals (Sweden)

    Igor Stojanović

    2017-01-01

    Full Text Available The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

  3. Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhao, Changhong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zamzam, Admed S. [University of Minnesota; Sidiropoulos, Nicholas D. [University of Minnesota; Taylor, Josh A. [University of Toronto

    2018-01-12

    This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successive convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.

  4. Kinetic constrained optimization of the golf swing hub path.

    Science.gov (United States)

    Nesbit, Steven M; McGinnis, Ryan S

    2014-12-01

    This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key PointsThe hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer.It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer.It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories.Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact.The hand path trajectory has important influences over the club swing trajectory.

  5. Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

    Science.gov (United States)

    Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

    2017-08-01

    The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

  6. Statistical mechanics of budget-constrained auctions

    International Nuclear Information System (INIS)

    Altarelli, F; Braunstein, A; Realpe-Gomez, J; Zecchina, R

    2009-01-01

    Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise

  7. Statistical mechanics of budget-constrained auctions

    Science.gov (United States)

    Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

    2009-07-01

    Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

  8. Constrained variational calculus for higher order classical field theories

    Energy Technology Data Exchange (ETDEWEB)

    Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn, E-mail: cedricmc@icmat.e, E-mail: mdeleon@icmat.e, E-mail: david.martin@icmat.e [Instituto de Ciencias Matematicas, CSIC-UAM-UC3M-UCM, Serrano 123, 28006 Madrid (Spain)

    2010-11-12

    We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

  9. Constrained variational calculus for higher order classical field theories

    International Nuclear Information System (INIS)

    Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn

    2010-01-01

    We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

  10. Torque-Matched Aerodynamic Shape Optimization of HAWT Rotor

    International Nuclear Information System (INIS)

    Al-Abadi, Ali; Ertunç, Özgür; Beyer, Florian; Delgado, Antonio

    2014-01-01

    Schmitz and Blade Element Momentum (BEM) theories are integrated to a gradient based optimization algorithm to optimize the blade shape of a horizontal axis wind turbine (HAWT). The Schmitz theory is used to generate an initial blade design. BEM theory is used to calculate the forces, torque and power extracted by the turbine. The airfoil shape (NREL S809) is kept the same, so that the shape optimization comprises only the chord and the pitch angle distribution. The gradient based optimization of the blade shape is constrained to the torque-rotational speed characteristic of the generator, which is going to be a part of the experimental set-up used to validate the results of the optimization study. Hence, the objective of the optimization is the maximization of the turbines power coefficient C p while keeping the torque matched to that of the generator. The wind velocities and the rotational speeds are limited to those achievable in the wind tunnel and by the generator, respectively. After finding the optimum blade shape with the maximum C p within the given range of parameters, the C p of the turbine is evaluated at wind-speeds deviating from the optimum operating condition. For this purpose, a second optimization algorithm is used to find out the correct rotational speed for a given wind-speed, which is again constrained to the generator's torque rotational speed characteristic. The design and optimization procedures are later validated by high-fidelity numerical simulations. The agreement between the design and the numerical simulations is very satisfactory

  11. Affording and Constraining Local Moral Orders in Teacher-Led Ability-Based Mathematics Groups

    Science.gov (United States)

    Tait-McCutcheon, Sandi; Shuker, Mary Jane; Higgins, Joanna; Loveridge, Judith

    2015-01-01

    How teachers position themselves and their students can influence the development of afforded or constrained local moral orders in ability-based teacher-led mathematics lessons. Local moral orders are the negotiated discursive practices and interactions of participants in the group. In this article, the developing local moral orders of 12 teachers…

  12. 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%

  13. A hybrid guided neighborhood search for the disjunctively constrained knapsack problem

    Directory of Open Access Journals (Sweden)

    Mhand Hifi

    2015-12-01

    Full Text Available In this paper, we investigate the use of a hybrid guided neighborhood search for solving the disjunctively constrained knapsack problem. The studied problem may be viewed as a combination of two NP-hard combinatorial optimization problems: the weighted-independent set and the classical binary knapsack. The proposed algorithm is a hybrid approach that combines both deterministic and random local searches. The deterministic local search is based on a descent method, where both building and exploring procedures are alternatively used for improving the solution at hand. In order to escape from a local optima, a random local search strategy is introduced which is based on a modified ant colony optimization system. During the search process, the ant colony optimization system tries to diversify and to enhance the solutions using some informations collected from the previous iterations. Finally, the proposed algorithm is computationally analyzed on a set of benchmark instances available in the literature. The provided results are compared to those realized by both the Cplex solver and a recent algorithm of the literature. The computational part shows that the obtained results improve most existing solution values.

  14. Joint Chance-Constrained Dynamic Programming

    Science.gov (United States)

    Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

    2012-01-01

    This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

  15. Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

    Science.gov (United States)

    Zhang, Chenglong; Zhang, Fan; Guo, Shanshan; Liu, Xiao; Guo, Ping

    2018-01-01

    An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.

  16. Improved Sensitivity Relations in State Constrained Optimal Control

    International Nuclear Information System (INIS)

    Bettiol, Piernicola; Frankowska, Hélène; Vinter, Richard B.

    2015-01-01

    Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because

  17. Lightweight cryptography for constrained devices

    DEFF Research Database (Denmark)

    Alippi, Cesare; Bogdanov, Andrey; Regazzoni, Francesco

    2014-01-01

    Lightweight cryptography is a rapidly evolving research field that responds to the request for security in resource constrained devices. This need arises from crucial pervasive IT applications, such as those based on RFID tags where cost and energy constraints drastically limit the solution...... complexity, with the consequence that traditional cryptography solutions become too costly to be implemented. In this paper, we survey design strategies and techniques suitable for implementing security primitives in constrained devices....

  18. Phase-based Planning for Railway Infrastructure Projects

    DEFF Research Database (Denmark)

    Li, Rui

    2017-01-01

    is important in terms of economy and rail operations. This study presents two types of Phase-based Decision Support System (PDSS), i.e. Functional Phase-Based Planning Approach (F-PBPA) and Process-Oriented Phase-Based Planning Approach (PO-PBPA). They are used for decision support for the planning...... of the railway infrastructure maintenance activities at the strategic planning level. The objective is to achieve better economy, as well as improve cost efficiency. F-PBPA consists of ve main phases: Data Collection, Technical Optimization (TeO), Economic Optimization (EcO), Constrained Optimization (Co...... the costs. Finally, constrained optimization (CoO) includes additional constraints and it allows the railway expert to adjust input parameters, thereby to obtain alternative maintenance plans. PO-PBPA contains another systematic phase based process. With a focus on Life Cycle Cost (LCC), PO-PBPA can guide...

  19. Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems

    KAUST Repository

    Xinyu Tang,

    2010-01-25

    Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end-effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot\\'s degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robot\\'s degrees of freedom. In addition to supporting efficient sampling of configurations, we show that the RD-space formulation naturally supports planning and, in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end-effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1,000 links in time comparable to open chain sampling, and we can generate samples for 1,000-link multi-loop systems of varying topologies in less than a second. © 2010 The Author(s).

  20. Locality constrained joint dynamic sparse representation for local matching based face recognition.

    Science.gov (United States)

    Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

    2014-01-01

    Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

  1. Locality constrained joint dynamic sparse representation for local matching based face recognition.

    Directory of Open Access Journals (Sweden)

    Jianzhong Wang

    Full Text Available Recently, Sparse Representation-based Classification (SRC has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW demonstrate the effectiveness of LCJDSRC.

  2. Optimal truss and frame design from projected homogenization-based topology optimization

    DEFF Research Database (Denmark)

    Larsen, S. D.; Sigmund, O.; Groen, J. P.

    2018-01-01

    In this article, we propose a novel method to obtain a near-optimal frame structure, based on the solution of a homogenization-based topology optimization model. The presented approach exploits the equivalence between Michell’s problem of least-weight trusses and a compliance minimization problem...... using optimal rank-2 laminates in the low volume fraction limit. In a fully automated procedure, a discrete structure is extracted from the homogenization-based continuum model. This near-optimal structure is post-optimized as a frame, where the bending stiffness is continuously decreased, to allow...

  3. Optimal design of a vehicle magnetorheological damper considering the damping force and dynamic range

    International Nuclear Information System (INIS)

    Nguyen, Quoc-Hung; Choi, Seung-Bok

    2009-01-01

    This paper presents an optimal design of a passenger vehicle magnetorheological (MR) damper based on finite element analysis. The MR damper is constrained in a specific volume and the optimization problem identifies the geometric dimensions of the damper that minimize an objective function. The objective function consists of the damping force, the dynamic range, and the inductive time constant of the damper. After describing the configuration of the MR damper, the damping force and dynamic range are obtained on the basis of the Bingham model of an MR fluid. Then, the control energy (power consumption of the damper coil) and the inductive time constant are derived. The objective function for the optimization problem is determined based on the solution of the magnetic circuit of the initial damper. Subsequently, the optimization procedure, using a golden-section algorithm and a local quadratic fitting technique, is constructed via commercial finite element method parametric design language. Using the developed optimization tool, optimal solutions of the MR damper, which are constrained in a specific cylindrical volume defined by its radius and height, are determined and a comparative work on damping force and inductive time constant between the initial and optimal design is undertaken

  4. Optimization with PDE constraints ESF networking program 'OPTPDE'

    CERN Document Server

    2014-01-01

    This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).

  5. Recent advances in computational optimization

    CERN Document Server

    2013-01-01

    Optimization is part of our everyday life. We try to organize our work in a better way and optimization occurs in minimizing time and cost or the maximization of the profit, quality and efficiency. Also many real world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization. This book presents recent advances in computational optimization. The volume includes important real world problems like parameter settings for con- trolling processes in bioreactor, robot skin wiring, strip packing, project scheduling, tuning of PID controller and so on. Some of them can be solved by applying traditional numerical methods, but others need a huge amount of computational resources. For them it is shown that is appropriate to develop algorithms based on metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming etc...

  6. Scheduling Aircraft Landings under Constrained Position Shifting

    Science.gov (United States)

    Balakrishnan, Hamsa; Chandran, Bala

    2006-01-01

    Optimal scheduling of airport runway operations can play an important role in improving the safety and efficiency of the National Airspace System (NAS). Methods that compute the optimal landing sequence and landing times of aircraft must accommodate practical issues that affect the implementation of the schedule. One such practical consideration, known as Constrained Position Shifting (CPS), is the restriction that each aircraft must land within a pre-specified number of positions of its place in the First-Come-First-Served (FCFS) sequence. We consider the problem of scheduling landings of aircraft in a CPS environment in order to maximize runway throughput (minimize the completion time of the landing sequence), subject to operational constraints such as FAA-specified minimum inter-arrival spacing restrictions, precedence relationships among aircraft that arise either from airline preferences or air traffic control procedures that prevent overtaking, and time windows (representing possible control actions) during which each aircraft landing can occur. We present a Dynamic Programming-based approach that scales linearly in the number of aircraft, and describe our computational experience with a prototype implementation on realistic data for Denver International Airport.

  7. Phase-Field Relaxation of Topology Optimization with Local Stress Constraints

    DEFF Research Database (Denmark)

    Stainko, Roman; Burger, Martin

    2006-01-01

    inequality constraints. We discretize the problem by finite elements and solve the arising finite-dimensional programming problems by a primal-dual interior point method. Numerical experiments for problems with local stress constraints based on different criteria indicate the success and robustness......We introduce a new relaxation scheme for structural topology optimization problems with local stress constraints based on a phase-field method. In the basic formulation we have a PDE-constrained optimization problem, where the finite element and design analysis are solved simultaneously...

  8. Automatic analog IC sizing and optimization constrained with PVT corners and layout effects

    CERN Document Server

    Lourenço, Nuno; Horta, Nuno

    2017-01-01

    This book introduces readers to a variety of tools for automatic analog integrated circuit (IC) sizing and optimization. The authors provide a historical perspective on the early methods proposed to tackle automatic analog circuit sizing, with emphasis on the methodologies to size and optimize the circuit, and on the methodologies to estimate the circuit’s performance. The discussion also includes robust circuit design and optimization and the most recent advances in layout-aware analog sizing approaches. The authors describe a methodology for an automatic flow for analog IC design, including details of the inputs and interfaces, multi-objective optimization techniques, and the enhancements made in the base implementation by using machine leaning techniques. The Gradient model is discussed in detail, along with the methods to include layout effects in the circuit sizing. The concepts and algorithms of all the modules are thoroughly described, enabling readers to reproduce the methodologies, improve the qual...

  9. A Dynamic Optimization Method of Indoor Fire Evacuation Route Based on Real-time Situation Awareness

    Directory of Open Access Journals (Sweden)

    DING Yulin

    2016-12-01

    Full Text Available How to provide safe and effective evacuation routes is an important safeguard to correctly guide evacuation and reduce the casualties during the fire situation rapidly evolving in complex indoor environment. The traditional static path finding method is difficult to adjust the path adaptively according to the changing fire situation, which lead to the evacuation decision-making blindness and hysteresis. This paper proposes a dynamic method which can dynamically optimize the indoor evacuation routes based on the real-time situation awareness. According to the real-time perception of fire situation parameters and the changing indoor environment information, the evacuation route is optimized dynamically. The integrated representation of multisource indoor fire monitoring sensor observations oriented fire emergency evacuation is presented at first, real-time fire threat situation information inside building is then extracted from the observation data of multi-source sensors, which is used to constrain the dynamical optimization of the topology of the evacuation route. Finally, the simulation experiments prove that this method can improve the accuracy and efficiency of indoor evacuation routing.

  10. GPS-based ionospheric tomography with a constrained adaptive ...

    Indian Academy of Sciences (India)

    Gauss weighted function is introduced to constrain the tomography system in the new method. It can resolve the ... the research focus in the fields of space geodesy and ... ment of GNSS such as GPS, Glonass, Galileo and. Compass, as these ...

  11. Reliability-based optimization of engineering structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2008-01-01

    The theoretical basis for reliability-based structural optimization within the framework of Bayesian statistical decision theory is briefly described. Reliability-based cost benefit problems are formulated and exemplitied with structural optimization. The basic reliability-based optimization...... problems are generalized to the following extensions: interactive optimization, inspection and repair costs, systematic reconstruction, re-assessment of existing structures. Illustrative examples are presented including a simple introductory example, a decision problem related to bridge re...

  12. 8th Workshop on Computational Optimization

    CERN Document Server

    2016-01-01

    This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2015. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, control of ethanol production, minimal convex hill with application in routing algorithms, graph coloring, flow design in photonic data transport system, predicting indoor temperature, crisis control center monitoring, fuel consumption of helicopters, portfolio selection, GPS surveying and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization problems. .

  13. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

    2016-10-01

    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  14. Modeling and query the uncertainty of network constrained moving objects based on RFID data

    Science.gov (United States)

    Han, Liang; Xie, Kunqing; Ma, Xiujun; Song, Guojie

    2007-06-01

    The management of network constrained moving objects is more and more practical, especially in intelligent transportation system. In the past, the location information of moving objects on network is collected by GPS, which cost high and has the problem of frequent update and privacy. The RFID (Radio Frequency IDentification) devices are used more and more widely to collect the location information. They are cheaper and have less update. And they interfere in the privacy less. They detect the id of the object and the time when moving object passed by the node of the network. They don't detect the objects' exact movement in side the edge, which lead to a problem of uncertainty. How to modeling and query the uncertainty of the network constrained moving objects based on RFID data becomes a research issue. In this paper, a model is proposed to describe the uncertainty of network constrained moving objects. A two level index is presented to provide efficient access to the network and the data of movement. The processing of imprecise time-slice query and spatio-temporal range query are studied in this paper. The processing includes four steps: spatial filter, spatial refinement, temporal filter and probability calculation. Finally, some experiments are done based on the simulated data. In the experiments the performance of the index is studied. The precision and recall of the result set are defined. And how the query arguments affect the precision and recall of the result set is also discussed.

  15. Discrete Material and Thickness Optimization of laminated composite structures including failure criteria

    DEFF Research Database (Denmark)

    Lund, Erik

    2017-01-01

    This work extends the Discrete Material and Thickness Optimization approach to structural optimization problems where strength considerations in the form of failure criteria are taken into account for laminated composite structures. It takes offset in the density approaches applied for stress...... constrained topology optimization of single-material problems and develops formulations for multi-material topology optimization problems applied for laminated composite structures. The method can be applied for both stress- and strain-based failure criteria. The large number of local constraints is reduced...

  16. Lifecycle-Based Swarm Optimization Method for Numerical Optimization

    Directory of Open Access Journals (Sweden)

    Hai Shen

    2014-01-01

    Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

  17. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    Science.gov (United States)

    Janardhanan, S.; Datta, B.

    2011-12-01

    saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

  18. Onomatopoeia characters extraction from comic images using constrained Delaunay triangulation

    Science.gov (United States)

    Liu, Xiangping; Shoji, Kenji; Mori, Hiroshi; Toyama, Fubito

    2014-02-01

    A method for extracting onomatopoeia characters from comic images was developed based on stroke width feature of characters, since they nearly have a constant stroke width in a number of cases. An image was segmented with a constrained Delaunay triangulation. Connected component grouping was performed based on the triangles generated by the constrained Delaunay triangulation. Stroke width calculation of the connected components was conducted based on the altitude of the triangles generated with the constrained Delaunay triangulation. The experimental results proved the effectiveness of the proposed method.

  19. Fitness Estimation Based Particle Swarm Optimization Algorithm for Layout Design of Truss Structures

    Directory of Open Access Journals (Sweden)

    Ayang Xiao

    2014-01-01

    Full Text Available Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational cost of truss analysis is often quite expensive. In this paper, a novel fitness estimation based particle swarm optimization algorithm with an adaptive penalty function approach (FEPSO-AP is proposed to handle this problem. FEPSO-AP adopts a special fitness estimate strategy to evaluate the similar particles in the current population, with the purpose to reduce the computational cost. Further more, a laconic adaptive penalty function is employed by FEPSO-AP, which can handle multiple constraints effectively by making good use of historical iteration information. Four benchmark examples with fixed topologies and up to 44 design dimensions were studied to verify the generality and efficiency of the proposed algorithm. Numerical results of the present work compared with results of other state-of-the-art hybrid algorithms shown in the literature demonstrate that the convergence rate and the solution quality of FEPSO-AP are essentially competitive.

  20. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

    Science.gov (United States)

    Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

    2017-05-01

    In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

  1. Conditions for the Solvability of the Linear Programming Formulation for Constrained Discounted Markov Decision Processes

    Energy Technology Data Exchange (ETDEWEB)

    Dufour, F., E-mail: dufour@math.u-bordeaux1.fr [Institut de Mathématiques de Bordeaux, INRIA Bordeaux Sud Ouest, Team: CQFD, and IMB (France); Prieto-Rumeau, T., E-mail: tprieto@ccia.uned.es [UNED, Department of Statistics and Operations Research (Spain)

    2016-08-15

    We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state and action spaces, compact action sets, and lower semi-continuous cost functions. We introduce a set of hypotheses related to a positive weight function which allow us to consider cost functions that might not be bounded below by a constant, and which imply the solvability of the linear programming formulation of the constrained MDP. In particular, we establish the existence of a constrained optimal stationary policy. Our results are illustrated with an application to a fishery management problem.

  2. Resource Constrained Planning of Multiple Projects with Separable Activities

    Science.gov (United States)

    Fujii, Susumu; Morita, Hiroshi; Kanawa, Takuya

    In this study we consider a resource constrained planning problem of multiple projects with separable activities. This problem provides a plan to process the activities considering a resource availability with time window. We propose a solution algorithm based on the branch and bound method to obtain the optimal solution minimizing the completion time of all projects. We develop three methods for improvement of computational efficiency, that is, to obtain initial solution with minimum slack time rule, to estimate lower bound considering both time and resource constraints and to introduce an equivalence relation for bounding operation. The effectiveness of the proposed methods is demonstrated by numerical examples. Especially as the number of planning projects increases, the average computational time and the number of searched nodes are reduced.

  3. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  4. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  5. Measurement model and calibration experiment of over-constrained parallel six-dimensional force sensor based on stiffness characteristics analysis

    International Nuclear Information System (INIS)

    Niu, Zhi; Zhao, Yanzhi; Zhao, Tieshi; Cao, Yachao; Liu, Menghua

    2017-01-01

    An over-constrained, parallel six-dimensional force sensor has various advantages, including its ability to bear heavy loads and provide redundant force measurement information. These advantages render the sensor valuable in important applications in the field of aerospace (space docking tests, etc). The stiffness of each component in the over-constrained structure has a considerable influence on the internal force distribution of the structure. Thus, the measurement model changes when the measurement branches of the sensor are under tensile or compressive force. This study establishes a general measurement model for an over-constrained parallel six-dimensional force sensor considering the different branch tensions and compression stiffness values. Numerical calculations and analyses are performed using practical examples. Based on the parallel mechanism, an over-constrained, orthogonal structure is proposed for a six-dimensional force sensor. Hence, a prototype is designed and developed, and a calibration experiment is conducted. The measurement accuracy of the sensor is improved based on the measurement model under different branch tensions and compression stiffness values. Moreover, the largest class I error is reduced from 5.81 to 2.23% full scale (FS), and the largest class II error is reduced from 3.425 to 1.871% FS. (paper)

  6. Model-based dynamic control and optimization of gas networks

    Energy Technology Data Exchange (ETDEWEB)

    Hofsten, Kai

    2001-07-01

    This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum

  7. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    Science.gov (United States)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  8. Topology Optimization Design of 3D Continuum Structure with Reserved Hole Based on Variable Density Method

    Directory of Open Access Journals (Sweden)

    Bai Shiye

    2016-05-01

    Full Text Available An objective function defined by minimum compliance of topology optimization for 3D continuum structure was established to search optimal material distribution constrained by the predetermined volume restriction. Based on the improved SIMP (solid isotropic microstructures with penalization model and the new sensitivity filtering technique, basic iteration equations of 3D finite element analysis were deduced and solved by optimization criterion method. All the above procedures were written in MATLAB programming language, and the topology optimization design examples of 3D continuum structure with reserved hole were examined repeatedly by observing various indexes, including compliance, maximum displacement, and density index. The influence of mesh, penalty factors, and filter radius on the topology results was analyzed. Computational results showed that the finer or coarser the mesh number was, the larger the compliance, maximum displacement, and density index would be. When the filtering radius was larger than 1.0, the topology shape no longer appeared as a chessboard problem, thus suggesting that the presented sensitivity filtering method was valid. The penalty factor should be an integer because iteration steps increased greatly when it is a noninteger. The above modified variable density method could provide technical routes for topology optimization design of more complex 3D continuum structures in the future.

  9. Agent-Based Optimization

    CERN Document Server

    Jędrzejowicz, Piotr; Kacprzyk, Janusz

    2013-01-01

    This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

  10. Performance-based shape optimization of continuum structures

    International Nuclear Information System (INIS)

    Liang Qingquan

    2010-01-01

    This paper presents a performance-based optimization (PBO) method for optimal shape design of continuum structures with stiffness constraints. Performance-based design concepts are incorporated in the shape optimization theory to achieve optimal designs. In the PBO method, the traditional shape optimization problem of minimizing the weight of a continuum structure with displacement or mean compliance constraints is transformed to the problem of maximizing the performance of the structure. The optimal shape of a continuum structure is obtained by gradually eliminating inefficient finite elements from the structure until its performance is maximized. Performance indices are employed to monitor the performance of optimized shapes in an optimization process. Performance-based optimality criteria are incorporated in the PBO method to identify the optimum from the optimization process. The PBO method is used to produce optimal shapes of plane stress continuum structures and plates in bending. Benchmark numerical results are provided to demonstrate the effectiveness of the PBO method for generating the maximum stiffness shape design of continuum structures. It is shown that the PBO method developed overcomes the limitations of traditional shape optimization methods in optimal design of continuum structures. Performance-based optimality criteria presented can be incorporated in any shape and topology optimization methods to obtain optimal designs of continuum structures.

  11. The bounds of feasible space on constrained nonconvex quadratic programming

    Science.gov (United States)

    Zhu, Jinghao

    2008-03-01

    This paper presents a method to estimate the bounds of the radius of the feasible space for a class of constrained nonconvex quadratic programmingsE Results show that one may compute a bound of the radius of the feasible space by a linear programming which is known to be a P-problem [N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4 (1984) 373-395]. It is proposed that one applies this method for using the canonical dual transformation [D.Y. Gao, Canonical duality theory and solutions to constrained nonconvex quadratic programming, J. Global Optimization 29 (2004) 377-399] for solving a standard quadratic programming problem.

  12. High-Fidelity Multidisciplinary Design Optimization of Aircraft Configurations

    Science.gov (United States)

    Martins, Joaquim R. R. A.; Kenway, Gaetan K. W.; Burdette, David; Jonsson, Eirikur; Kennedy, Graeme J.

    2017-01-01

    To evaluate new airframe technologies we need design tools based on high-fidelity models that consider multidisciplinary interactions early in the design process. The overarching goal of this NRA is to develop tools that enable high-fidelity multidisciplinary design optimization of aircraft configurations, and to apply these tools to the design of high aspect ratio flexible wings. We develop a geometry engine that is capable of quickly generating conventional and unconventional aircraft configurations including the internal structure. This geometry engine features adjoint derivative computation for efficient gradient-based optimization. We also added overset capability to a computational fluid dynamics solver, complete with an adjoint implementation and semiautomatic mesh generation. We also developed an approach to constraining buffet and started the development of an approach for constraining utter. On the applications side, we developed a new common high-fidelity model for aeroelastic studies of high aspect ratio wings. We performed optimal design trade-o s between fuel burn and aircraft weight for metal, conventional composite, and carbon nanotube composite wings. We also assessed a continuous morphing trailing edge technology applied to high aspect ratio wings. This research resulted in the publication of 26 manuscripts so far, and the developed methodologies were used in two other NRAs. 1

  13. Practical mathematical optimization basic optimization theory and gradient-based algorithms

    CERN Document Server

    Snyman, Jan A

    2018-01-01

    This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...

  14. GPS-based ionospheric tomography with a constrained adaptive ...

    Indian Academy of Sciences (India)

    According to the continuous smoothness of the variations of ionospheric electron density (IED) among neighbouring voxels, Gauss weighted function is introduced to constrain the tomography system in the new method. It can resolve the dependence on the initial values for those voxels without any GPS rays traversing them ...

  15. Dynamic Convex Duality in Constrained Utility Maximization

    OpenAIRE

    Li, Yusong; Zheng, Harry

    2016-01-01

    In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint process coming from the dual FBSDEs in a dynamic fashion and vice versa. Moreover, we also...

  16. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    International Nuclear Information System (INIS)

    Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

    2006-01-01

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

  17. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

    2006-09-15

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

  18. Risk Based Optimal Fatigue Testing

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

    1992-01-01

    Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

  19. On the design of compliant mechanisms using topology optimization

    DEFF Research Database (Denmark)

    Sigmund, Ole

    1997-01-01

    This paper presents a method for optimal design of compliant mechanism topologies. The method is based on continuum-type topology optimization techniques and finds the optimal compliant mechanism topology within a given design domain and a given position and direction of input and output forces....... By constraining the allowed displacement at the input port, it is possible to control the maximum stress level in the compliant mechanism. The ability of the design method to find a mechanism with complex output behavior is demonstrated by several examples. Some of the optimal mechanism topologies have been...... manufactured, both in macroscale (hand-size) made in Nylon, and in microscale (

  20. A "Reverse-Schur" Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design.

    Science.gov (United States)

    Bardhan, Jaydeep P; Altman, Michael D; Tidor, B; White, Jacob K

    2009-01-01

    We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule's electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts-in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method.

  1. Development of optimization-based probabilistic earthquake scenarios for the city of Tehran

    Science.gov (United States)

    Zolfaghari, M. R.; Peyghaleh, E.

    2016-01-01

    This paper presents the methodology and practical example for the application of optimization process to select earthquake scenarios which best represent probabilistic earthquake hazard in a given region. The method is based on simulation of a large dataset of potential earthquakes, representing the long-term seismotectonic characteristics in a given region. The simulation process uses Monte-Carlo simulation and regional seismogenic source parameters to generate a synthetic earthquake catalogue consisting of a large number of earthquakes, each characterized with magnitude, location, focal depth and fault characteristics. Such catalogue provides full distributions of events in time, space and size; however, demands large computation power when is used for risk assessment, particularly when other sources of uncertainties are involved in the process. To reduce the number of selected earthquake scenarios, a mixed-integer linear program formulation is developed in this study. This approach results in reduced set of optimization-based probabilistic earthquake scenario, while maintaining shape of hazard curves and full probabilistic picture by minimizing the error between hazard curves driven by full and reduced sets of synthetic earthquake scenarios. To test the model, the regional seismotectonic and seismogenic characteristics of northern Iran are used to simulate a set of 10,000-year worth of events consisting of some 84,000 earthquakes. The optimization model is then performed multiple times with various input data, taking into account probabilistic seismic hazard for Tehran city as the main constrains. The sensitivity of the selected scenarios to the user-specified site/return period error-weight is also assessed. The methodology could enhance run time process for full probabilistic earthquake studies like seismic hazard and risk assessment. The reduced set is the representative of the contributions of all possible earthquakes; however, it requires far less

  2. Logic-based methods for optimization combining optimization and constraint satisfaction

    CERN Document Server

    Hooker, John

    2011-01-01

    A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible

  3. Reflected stochastic differential equation models for constrained animal movement

    Science.gov (United States)

    Hanks, Ephraim M.; Johnson, Devin S.; Hooten, Mevin B.

    2017-01-01

    Movement for many animal species is constrained in space by barriers such as rivers, shorelines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

  4. Optimization algorithms and applications

    CERN Document Server

    Arora, Rajesh Kumar

    2015-01-01

    Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc

  5. Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization

    Directory of Open Access Journals (Sweden)

    Maciej Malawski

    2015-01-01

    Full Text Available This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments with Montage, an astronomical application, executed on Amazon EC2 cloud. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.

  6. Optimization of a constrained linear monochromator design for neutral atom beams

    International Nuclear Information System (INIS)

    Kaltenbacher, Thomas

    2016-01-01

    A focused ground state, neutral atom beam, exploiting its de Broglie wavelength by means of atom optics, is used for neutral atom microscopy imaging. Employing Fresnel zone plates as a lens for these beams is a well established microscopy technique. To date, even for favorable beam source conditions a minimal focus spot size of slightly below 1 μm was reached. This limitation is essentially given by the intrinsic spectral purity of the beam in combination with the chromatic aberration of the diffraction based zone plate. Therefore, it is important to enhance the monochromaticity of the beam, enabling a higher spatial resolution, preferably below 100 nm. We propose to increase the monochromaticity of a neutral atom beam by means of a so-called linear monochromator set-up – a Fresnel zone plate in combination with a pinhole aperture – in order to gain more than one order of magnitude in spatial resolution. This configuration is known in X-ray microscopy and has proven to be useful, but has not been applied to neutral atom beams. The main result of this work is optimal design parameters based on models for this linear monochromator set-up followed by a second zone plate for focusing. The optimization was performed for minimizing the focal spot size and maximizing the centre line intensity at the detector position for an atom beam simultaneously. The results presented in this work are for, but not limited to, a neutral helium atom beam. - Highlights: • The presented results are essential for optimal operation conditions of a neutral atom microscope set-up. • The key parameters for the experimental arrangement of a neutral microscopy set-up are identified and their interplay is quantified. • Insights in the multidimensional problem provide deep and crucial understanding for pushing beyond the apparent focus limitations. • This work points out the trade-offs for high intensity and high spatial resolution indicating several use cases.

  7. Optimization of a constrained linear monochromator design for neutral atom beams

    Energy Technology Data Exchange (ETDEWEB)

    Kaltenbacher, Thomas

    2016-04-15

    A focused ground state, neutral atom beam, exploiting its de Broglie wavelength by means of atom optics, is used for neutral atom microscopy imaging. Employing Fresnel zone plates as a lens for these beams is a well established microscopy technique. To date, even for favorable beam source conditions a minimal focus spot size of slightly below 1 μm was reached. This limitation is essentially given by the intrinsic spectral purity of the beam in combination with the chromatic aberration of the diffraction based zone plate. Therefore, it is important to enhance the monochromaticity of the beam, enabling a higher spatial resolution, preferably below 100 nm. We propose to increase the monochromaticity of a neutral atom beam by means of a so-called linear monochromator set-up – a Fresnel zone plate in combination with a pinhole aperture – in order to gain more than one order of magnitude in spatial resolution. This configuration is known in X-ray microscopy and has proven to be useful, but has not been applied to neutral atom beams. The main result of this work is optimal design parameters based on models for this linear monochromator set-up followed by a second zone plate for focusing. The optimization was performed for minimizing the focal spot size and maximizing the centre line intensity at the detector position for an atom beam simultaneously. The results presented in this work are for, but not limited to, a neutral helium atom beam. - Highlights: • The presented results are essential for optimal operation conditions of a neutral atom microscope set-up. • The key parameters for the experimental arrangement of a neutral microscopy set-up are identified and their interplay is quantified. • Insights in the multidimensional problem provide deep and crucial understanding for pushing beyond the apparent focus limitations. • This work points out the trade-offs for high intensity and high spatial resolution indicating several use cases.

  8. A one-layer recurrent neural network for constrained nonsmooth optimization.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2011-10-01

    This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

  9. Assessment of oscillatory stability constrained available transfer capability

    International Nuclear Information System (INIS)

    Jain, T.; Singh, S.N.; Srivastava, S.C.

    2009-01-01

    This paper utilizes a bifurcation approach to compute oscillatory stability constrained available transfer capability (ATC) in an electricity market having bilateral as well as multilateral transactions. Oscillatory instability in non-linear systems can be related to Hopf bifurcation. At the Hopf bifurcation, one pair of the critical eigenvalues of the system Jacobian reaches imaginary axis. A new optimization formulation, including Hopf bifurcation conditions, has been developed in this paper to obtain the dynamic ATC. An oscillatory stability based contingency screening index, which takes into account the impact of transactions on severity of contingency, has been utilized to identify critical contingencies to be considered in determining ATC. The proposed method has been applied for dynamic ATC determination on a 39-bus New England system and a practical 75-bus Indian system considering composite static load as well as dynamic load models. (author)

  10. Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California.

    Science.gov (United States)

    Baker, Nameer R; Khalili, Banafshe; Martiny, Jennifer B H; Allison, Steven D

    2018-06-01

    Microbial decomposers mediate the return of CO 2 to the atmosphere by producing extracellular enzymes to degrade complex plant polymers, making plant carbon available for metabolism. Determining if and how these decomposer communities are constrained in their ability to degrade plant litter is necessary for predicting how carbon cycling will be affected by future climate change. We analyzed mass loss, litter chemistry, microbial biomass, extracellular enzyme activities, and enzyme temperature sensitivities in grassland litter transplanted along a Mediterranean climate gradient in southern California. Microbial community composition was manipulated by caging litter within bags made of nylon membrane that prevent microbial immigration. To test whether grassland microbes were constrained by climate history, half of the bags were inoculated with local microbial communities native to each gradient site. We determined that temperature and precipitation likely interact to limit microbial decomposition in the extreme sites along our gradient. Despite their unique climate history, grassland microbial communities were not restricted in their ability to decompose litter under different climate conditions across the gradient, although microbial communities across our gradient may be restricted in their ability to degrade different types of litter. We did find some evidence that local microbial communities were optimized based on climate, but local microbial taxa that proliferated after inoculation into litterbags did not enhance litter decomposition. Our results suggest that microbial community composition does not constrain C-cycling rates under climate change in our system, but optimization to particular resource environments may act as more general constraints on microbial communities. © 2018 by the Ecological Society of America.

  11. Designing equitable antiretroviral allocation strategies in resource-constrained countries.

    Directory of Open Access Journals (Sweden)

    David P Wilson

    2005-02-01

    Full Text Available Recently, a global commitment has been made to expand access to antiretrovirals (ARVs in the developing world. However, in many resource-constrained countries the number of individuals infected with HIV in need of treatment will far exceed the supply of ARVs, and only a limited number of health-care facilities (HCFs will be available for ARV distribution. Deciding how to allocate the limited supply of ARVs among HCFs will be extremely difficult. Resource allocation decisions can be made on the basis of many epidemiological, ethical, or preferential treatment priority criteria.Here we use operations research techniques, and we show how to determine the optimal strategy for allocating ARVs among HCFs in order to satisfy the equitable criterion that each individual infected with HIV has an equal chance of receiving ARVs. We present a novel spatial mathematical model that includes heterogeneity in treatment accessibility. We show how to use our theoretical framework, in conjunction with an equity objective function, to determine an optimal equitable allocation strategy (OEAS for ARVs in resource-constrained regions. Our equity objective function enables us to apply the egalitarian principle of equity with respect to access to health care. We use data from the detailed ARV rollout plan designed by the government of South Africa to determine an OEAS for the province of KwaZulu-Natal. We determine the OEAS for KwaZulu-Natal, and we then compare this OEAS with two other ARV allocation strategies: (i allocating ARVs only to Durban (the largest urban city in KwaZulu-Natal province and (ii allocating ARVs equally to all available HCFs. In addition, we compare the OEAS to the current allocation plan of the South African government (which is based upon allocating ARVs to 17 HCFs. We show that our OEAS significantly improves equity in treatment accessibility in comparison with these three ARV allocation strategies. We also quantify how the size of the

  12. Calibration of Water Supply Systems Based on Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Mahmoud Faghfoor Maghrebi

    2013-03-01

    Full Text Available Leakage is one of the main problems in the water supply systems and due to the limitations in water supply and its costly process, reduction of leak in water distribution networks can be considered as one of the main goals of the water supply authorities. One of the leak detection techniques in water distribution system is the usage of the recorded node pressures at some locations to calibrate the whole system node pressures. Calibration process is accomplished by the optimization of a constrained objective function. Therefore, in addition to performing a hydraulic analysis of the network, application of an optimization technique is needed. In the current paper, a comparsion between the ant colony and genetic algorithm methodes, in calibration of the node pressures and leak detections was investigated. To examine the workability and the way of leak detection, analysis of the network with an assumed leak was carried out. The results showed that the effectiveness of the ant colony optimization in the detection of the position and magnitude of leak in a water network.

  13. Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

    Energy Technology Data Exchange (ETDEWEB)

    Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria; Rice, Mark J.; Glaesemann, Kurt R.; Zhou, Ning

    2012-07-26

    Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.

  14. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

    Science.gov (United States)

    Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

    2017-05-01

    In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Resource Management in Constrained Dynamic Situations

    Science.gov (United States)

    Seok, Jinwoo

    Resource management is considered in this dissertation for systems with limited resources, possibly combined with other system constraints, in unpredictably dynamic environments. Resources may represent fuel, power, capabilities, energy, and so on. Resource management is important for many practical systems; usually, resources are limited, and their use must be optimized. Furthermore, systems are often constrained, and constraints must be satisfied for safe operation. Simplistic resource management can result in poor use of resources and failure of the system. Furthermore, many real-world situations involve dynamic environments. Many traditional problems are formulated based on the assumptions of given probabilities or perfect knowledge of future events. However, in many cases, the future is completely unknown, and information on or probabilities about future events are not available. In other words, we operate in unpredictably dynamic situations. Thus, a method is needed to handle dynamic situations without knowledge of the future, but few formal methods have been developed to address them. Thus, the goal is to design resource management methods for constrained systems, with limited resources, in unpredictably dynamic environments. To this end, resource management is organized hierarchically into two levels: 1) planning, and 2) control. In the planning level, the set of tasks to be performed is scheduled based on limited resources to maximize resource usage in unpredictably dynamic environments. In the control level, the system controller is designed to follow the schedule by considering all the system constraints for safe and efficient operation. Consequently, this dissertation is mainly divided into two parts: 1) planning level design, based on finite state machines, and 2) control level methods, based on model predictive control. We define a recomposable restricted finite state machine to handle limited resource situations and unpredictably dynamic environments

  16. A chance-constrained stochastic approach to intermodal container routing problems.

    Science.gov (United States)

    Zhao, Yi; Liu, Ronghui; Zhang, Xi; Whiteing, Anthony

    2018-01-01

    We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.

  17. A “Reverse-Schur” Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design

    Science.gov (United States)

    Bardhan, Jaydeep P.; Altman, Michael D.

    2009-01-01

    We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule’s electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts–in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method. PMID:23055839

  18. A new approach to nonlinear constrained Tikhonov regularization

    KAUST Repository

    Ito, Kazufumi

    2011-09-16

    We present a novel approach to nonlinear constrained Tikhonov regularization from the viewpoint of optimization theory. A second-order sufficient optimality condition is suggested as a nonlinearity condition to handle the nonlinearity of the forward operator. The approach is exploited to derive convergence rate results for a priori as well as a posteriori choice rules, e.g., discrepancy principle and balancing principle, for selecting the regularization parameter. The idea is further illustrated on a general class of parameter identification problems, for which (new) source and nonlinearity conditions are derived and the structural property of the nonlinearity term is revealed. A number of examples including identifying distributed parameters in elliptic differential equations are presented. © 2011 IOP Publishing Ltd.

  19. Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

    Science.gov (United States)

    Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun

    2017-08-07

    This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

  20. Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project

    Science.gov (United States)

    Xu, Jiuping

    2014-01-01

    This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708

  1. Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

    Science.gov (United States)

    Xu, Jiuping; Feng, Cuiying

    2014-01-01

    This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.

  2. Chance-Constrained Guidance With Non-Convex Constraints

    Science.gov (United States)

    Ono, Masahiro

    2011-01-01

    Missions to small bodies, such as comets or asteroids, require autonomous guidance for descent to these small bodies. Such guidance is made challenging by uncertainty in the position and velocity of the spacecraft, as well as the uncertainty in the gravitational field around the small body. In addition, the requirement to avoid collision with the asteroid represents a non-convex constraint that means finding the optimal guidance trajectory, in general, is intractable. In this innovation, a new approach is proposed for chance-constrained optimal guidance with non-convex constraints. Chance-constrained guidance takes into account uncertainty so that the probability of collision is below a specified threshold. In this approach, a new bounding method has been developed to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. Branch and bound enables non-convex problems to be solved efficiently to global optimality. Considering the problem of finite-horizon robust optimal control of dynamic systems under Gaussian-distributed stochastic uncertainty, with state and control constraints, a discrete-time, continuous-state linear dynamics model is assumed. Gaussian-distributed stochastic uncertainty is a more natural model for exogenous disturbances such as wind gusts and turbulence than the previously studied set-bounded models. However, with stochastic uncertainty, it is often impossible to guarantee that state constraints are satisfied, because there is typically a non-zero probability of having a disturbance that is large enough to push the state out of the feasible region. An effective framework to address robustness with stochastic uncertainty is optimization with chance constraints. These require that the probability of violating the state constraints (i.e., the probability of

  3. Particle Swarm Optimization Toolbox

    Science.gov (United States)

    Grant, Michael J.

    2010-01-01

    The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

  4. A CLASS OF NONMONOTONE TRUST REGION ALGORITHMS FOR LINEARLY CONSTRAINED OPTIMIZATION%线性约束优化的一类非单调信赖域算法

    Institute of Scientific and Technical Information of China (English)

    葛恒武; 陈中文

    2002-01-01

    We present a class of nonmonotone trust region algorithms for linearly constrained optimization in this paper.The algorithm may adjust automatically the scope of the monotonicity by the degree that the quadratic model is "trusted".Under the suitable conditions,it is proved that any limit point of the infinite sequence generated by the algorithm is the Kuhn-Tucker point of the primal problem.Finally,some numerical results show that the new algorithm is very effective.

  5. Reliability-Based Optimization in Structural Engineering

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1994-01-01

    In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability...

  6. Methods of mathematical optimization

    Science.gov (United States)

    Vanderplaats, G. N.

    The fundamental principles of numerical optimization methods are reviewed, with an emphasis on potential engineering applications. The basic optimization process is described; unconstrained and constrained minimization problems are defined; a general approach to the design of optimization software programs is outlined; and drawings and diagrams are shown for examples involving (1) the conceptual design of an aircraft, (2) the aerodynamic optimization of an airfoil, (3) the design of an automotive-engine connecting rod, and (4) the optimization of a 'ski-jump' to assist aircraft in taking off from a very short ship deck.

  7. Continuous nonlinear optimization for engineering applications in GAMS technology

    CERN Document Server

    Andrei, Neculai

    2017-01-01

    This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and graduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications. Beginning with an overview of constrained nonlinear optimization methods, this book moves on to illustrate key aspects of mathematical modeling through modeling technologies based on algebraically oriented modeling languages. Next, the main feature of GAMS, an algebraically oriented language that allows for high-level algebraic representation of mathematical opti...

  8. Optimization of a constrained linear monochromator design for neutral atom beams.

    Science.gov (United States)

    Kaltenbacher, Thomas

    2016-04-01

    A focused ground state, neutral atom beam, exploiting its de Broglie wavelength by means of atom optics, is used for neutral atom microscopy imaging. Employing Fresnel zone plates as a lens for these beams is a well established microscopy technique. To date, even for favorable beam source conditions a minimal focus spot size of slightly below 1μm was reached. This limitation is essentially given by the intrinsic spectral purity of the beam in combination with the chromatic aberration of the diffraction based zone plate. Therefore, it is important to enhance the monochromaticity of the beam, enabling a higher spatial resolution, preferably below 100nm. We propose to increase the monochromaticity of a neutral atom beam by means of a so-called linear monochromator set-up - a Fresnel zone plate in combination with a pinhole aperture - in order to gain more than one order of magnitude in spatial resolution. This configuration is known in X-ray microscopy and has proven to be useful, but has not been applied to neutral atom beams. The main result of this work is optimal design parameters based on models for this linear monochromator set-up followed by a second zone plate for focusing. The optimization was performed for minimizing the focal spot size and maximizing the centre line intensity at the detector position for an atom beam simultaneously. The results presented in this work are for, but not limited to, a neutral helium atom beam. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. A policy iteration approach to online optimal control of continuous-time constrained-input systems.

    Science.gov (United States)

    Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

    2013-09-01

    This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

  10. 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.

  11. Constrained Optimal Stochastic Control of Non-Linear Wave Energy Point Absorbers

    DEFF Research Database (Denmark)

    Sichani, Mahdi Teimouri; Chen, Jian-Bing; Kramer, Morten

    2014-01-01

    to extract energy. Constrains are enforced on the control force to prevent large structural stresses in the floater at specific hot spots with the risk of inducing fatigue damage, or because the demanded control force cannot be supplied by the actuator system due to saturation. Further, constraints...... are enforced on the motion of the floater to prevent it from hitting the bottom of the sea or to make unacceptable jumps out of the water. The applied control law, which is of the feedback type with feedback from the displacement, velocity, and acceleration of the floater, contains two unprovided gain...

  12. The Ontology of Knowledge Based Optimization

    OpenAIRE

    Nasution, Mahyuddin K. M.

    2012-01-01

    Optimization has been becoming a central of studies in mathematic and has many areas with different applications. However, many themes of optimization came from different area have not ties closing to origin concepts. This paper is to address some variants of optimization problems using ontology in order to building basic of knowledge about optimization, and then using it to enhance strategy to achieve knowledge based optimization.

  13. Optimizing CT radiation dose based on patient size and image quality: the size-specific dose estimate method

    Energy Technology Data Exchange (ETDEWEB)

    Larson, David B. [Stanford University School of Medicine, Department of Radiology, Stanford, CA (United States)

    2014-10-15

    The principle of ALARA (dose as low as reasonably achievable) calls for dose optimization rather than dose reduction, per se. Optimization of CT radiation dose is accomplished by producing images of acceptable diagnostic image quality using the lowest dose method available. Because it is image quality that constrains the dose, CT dose optimization is primarily a problem of image quality rather than radiation dose. Therefore, the primary focus in CT radiation dose optimization should be on image quality. However, no reliable direct measure of image quality has been developed for routine clinical practice. Until such measures become available, size-specific dose estimates (SSDE) can be used as a reasonable image-quality estimate. The SSDE method of radiation dose optimization for CT abdomen and pelvis consists of plotting SSDE for a sample of examinations as a function of patient size, establishing an SSDE threshold curve based on radiologists' assessment of image quality, and modifying protocols to consistently produce doses that are slightly above the threshold SSDE curve. Challenges in operationalizing CT radiation dose optimization include data gathering and monitoring, managing the complexities of the numerous protocols, scanners and operators, and understanding the relationship of the automated tube current modulation (ATCM) parameters to image quality. Because CT manufacturers currently maintain their ATCM algorithms as secret for proprietary reasons, prospective modeling of SSDE for patient populations is not possible without reverse engineering the ATCM algorithm and, hence, optimization by this method requires a trial-and-error approach. (orig.)

  14. PRODUCT OPTIMIZATION METHOD BASED ON ANALYSIS OF OPTIMAL VALUES OF THEIR CHARACTERISTICS

    Directory of Open Access Journals (Sweden)

    Constantin D. STANESCU

    2016-05-01

    Full Text Available The paper presents an original method of optimizing products based on the analysis of optimal values of their characteristics . Optimization method comprises statistical model and analytical model . With this original method can easily and quickly obtain optimal product or material .

  15. Towards weakly constrained double field theory

    Directory of Open Access Journals (Sweden)

    Kanghoon Lee

    2016-08-01

    Full Text Available We show that it is possible to construct a well-defined effective field theory incorporating string winding modes without using strong constraint in double field theory. We show that X-ray (Radon transform on a torus is well-suited for describing weakly constrained double fields, and any weakly constrained fields are represented as a sum of strongly constrained fields. Using inverse X-ray transform we define a novel binary operation which is compatible with the level matching constraint. Based on this formalism, we construct a consistent gauge transform and gauge invariant action without using strong constraint. We then discuss the relation of our result to the closed string field theory. Our construction suggests that there exists an effective field theory description for massless sector of closed string field theory on a torus in an associative truncation.

  16. Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

    Directory of Open Access Journals (Sweden)

    Charles Tatkeu

    2008-12-01

    Full Text Available We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

  17. Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

    Science.gov (United States)

    Zaouche, Abdelouahib; Dayoub, Iyad; Rouvaen, Jean Michel; Tatkeu, Charles

    2008-12-01

    We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR) strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA) are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

  18. A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation

    International Nuclear Information System (INIS)

    Sankaran, Sethuraman; Audet, Charles; Marsden, Alison L.

    2010-01-01

    Recent advances in coupling novel optimization methods to large-scale computing problems have opened the door to tackling a diverse set of physically realistic engineering design problems. A large computational overhead is associated with computing the cost function for most practical problems involving complex physical phenomena. Such problems are also plagued with uncertainties in a diverse set of parameters. We present a novel stochastic derivative-free optimization approach for tackling such problems. Our method extends the previously developed surrogate management framework (SMF) to allow for uncertainties in both simulation parameters and design variables. The stochastic collocation scheme is employed for stochastic variables whereas Kriging based surrogate functions are employed for the cost function. This approach is tested on four numerical optimization problems and is shown to have significant improvement in efficiency over traditional Monte-Carlo schemes. Problems with multiple probabilistic constraints are also discussed.

  19. Use of constrained mixture design for optimization of method for determination of zinc and manganese in tea leaves employing slurry sampling

    Energy Technology Data Exchange (ETDEWEB)

    Almeida Bezerra, Marcos, E-mail: mbezerra47@yahoo.com.br [Universidade Estadual do Sudoeste da Bahia, Laboratorio de Quimica Analitica, 45200-190, Jequie, Bahia (Brazil); Teixeira Castro, Jacira [Universidade Federal do Reconcavo da Bahia, Centro de Ciencias Exatas e Tecnologicas, 44380-000, Cruz das Almas, Bahia (Brazil); Coelho Macedo, Reinaldo; Goncalves da Silva, Douglas [Universidade Estadual do Sudoeste da Bahia, Laboratorio de Quimica Analitica, 45200-190, Jequie, Bahia (Brazil)

    2010-06-18

    A slurry suspension sampling technique has been developed for manganese and zinc determination in tea leaves by using flame atomic absorption spectrometry. The proportions of liquid-phase of the slurries composed by HCl, HNO{sub 3} and Triton X-100 solutions have been optimized applying a constrained mixture design. The optimized conditions were 200 mg of sample ground in a tungsten carbide balls mill (particle size < 100 {mu}m), dilution in a liquid-phase composed by 2.0 mol L{sup -1} nitric, 2.0 mol L{sup -1} hydrochloric acid and 2.5% Triton X-100 solutions (in the proportions of 50%, 12% and 38% respectively), sonication time of 10 min and final slurry volume of 50.0 mL. This method allowed the determination of manganese and zinc by FAAS, with detection limits of 0.46 and 0.66 {mu}g g{sup -1}, respectively. The precisions, expressed as relative standard deviation (RSD), are 6.9 and 5.5% (n = 10), for concentrations of manganese and zinc of 20 and 40 {mu}g g{sup -1}, respectively. The accuracy of the method was confirmed by analysis of the certified apple leaves (NIST 1515) and spinach leaves (NIST 1570a). The proposed method was applied for the determination of manganese and zinc in tea leaves used for the preparation of infusions. The obtained concentrations varied between 42 and 118 {mu}g g{sup -1} and 18.6 and 90 {mu}g g{sup -1}, respectively, for manganese and zinc. The results were compared with those obtained by an acid digestion procedure and determination of the elements by FAAS. There was no significant difference between the results obtained by the two methods based on a paired t-test (at 95% confidence level).

  20. Toward optimal X-ray flux utilization in breast CT

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Heide; Hansen, Per Christian; Sidky, Emil Y.

    2011-01-01

    A realistic computer-simulation of a breast computed tomography (CT) system and subject is constructed. The model is used to investigate the optimal number of views for the scan given a fixed total X-ray fluence. The reconstruction algorithm is based on accurate solution to a constrained......, TVminimization problem, which has received much interest recently for sparse-view CT data....

  1. Optimization of China's generating portfolio and policy implications based on portfolio theory

    International Nuclear Information System (INIS)

    Zhu, Lei; Fan, Ying

    2010-01-01

    This paper applies portfolio theory to evaluate China's 2020-medium-term plans for generating technologies and its generating portfolio. With reference to the risk of relevant generating-cost streams, the paper discusses China's future development of efficient (Pareto optimal) generating portfolios that enhance energy security in different scenarios, including CO 2 -emission-constrained scenarios. This research has found that the future adjustment of China's planned 2020 generating portfolio can reduce the portfolio's cost risk through appropriate diversification of generating technologies, but a price will be paid in the form of increased generating cost. In the CO 2 -emission-constrained scenarios, the generating-cost risk of China's planned 2020 portfolio is even greater than that of the 2005 portfolio, but increasing the proportion of nuclear power in the generating portfolio can reduce the cost risk effectively. For renewable-power generation, because of relatively high generating costs, it will be necessary to obtain stronger policy support to promote renewable-power development.

  2. Constrained superfields in supergravity

    Energy Technology Data Exchange (ETDEWEB)

    Dall’Agata, Gianguido; Farakos, Fotis [Dipartimento di Fisica ed Astronomia “Galileo Galilei”, Università di Padova,Via Marzolo 8, 35131 Padova (Italy); INFN, Sezione di Padova,Via Marzolo 8, 35131 Padova (Italy)

    2016-02-16

    We analyze constrained superfields in supergravity. We investigate the consistency and solve all known constraints, presenting a new class that may have interesting applications in the construction of inflationary models. We provide the superspace Lagrangians for minimal supergravity models based on them and write the corresponding theories in component form using a simplifying gauge for the goldstino couplings.

  3. Optimization of reconstruction algorithms using Monte Carlo simulation

    International Nuclear Information System (INIS)

    Hanson, K.M.

    1989-01-01

    A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. Task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by a iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a nonnegativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers. 11 refs., 6 figs., 2 tabs

  4. Optimization of reconstruction algorithms using Monte Carlo simulation

    International Nuclear Information System (INIS)

    Hanson, K.M.

    1989-01-01

    A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. Task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by an iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a non-negativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers. (author)

  5. A new hybrid optimization algorithm CRO-DE for optimal coordination of overcurrent relays in complex power systems

    Directory of Open Access Journals (Sweden)

    Mohamed Zellagui

    2017-09-01

    Full Text Available The paper presents a new hybrid global optimization algorithm based on Chemical Reaction based Optimization (CRO and Di¤erential evolution (DE algorithm for nonlinear constrained optimization problems. This approach proposed for the optimal coordination and setting relays of directional overcurrent relays in complex power systems. In protection coordination problem, the objective function to be minimized is the sum of the operating time of all main relays. The optimization problem is subject to a number of constraints which are mainly focused on the operation of the backup relay, which should operate if a primary relay fails to respond to the fault near to it, Time Dial Setting (TDS, Plug Setting (PS and the minimum operating time of a relay. The hybrid global proposed optimization algorithm aims to minimize the total operating time of each protection relay. Two systems are used as case study to check the effeciency of the optimization algorithm which are IEEE 4-bus and IEEE 6-bus models. Results are obtained and presented for CRO and DE and hybrid CRO-DE algorithms. The obtained results for the studied cases are compared with those results obtained when using other optimization algorithms which are Teaching Learning-Based Optimization (TLBO, Chaotic Differential Evolution Algorithm (CDEA and Modiffied Differential Evolution Algorithm (MDEA, and Hybrid optimization algorithms (PSO-DE, IA-PSO, and BFOA-PSO. From analysing the obtained results, it has been concluded that hybrid CRO-DO algorithm provides the most optimum solution with the best convergence rate.

  6. 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

  7. Optimality conditions for the numerical solution of optimization problems with PDE constraints :

    Energy Technology Data Exchange (ETDEWEB)

    Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

    2014-03-01

    A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

  8. Coverage-based constraints for IMRT optimization

    Science.gov (United States)

    Mescher, H.; Ulrich, S.; Bangert, M.

    2017-09-01

    Radiation therapy treatment planning requires an incorporation of uncertainties in order to guarantee an adequate irradiation of the tumor volumes. In current clinical practice, uncertainties are accounted for implicitly with an expansion of the target volume according to generic margin recipes. Alternatively, it is possible to account for uncertainties by explicit minimization of objectives that describe worst-case treatment scenarios, the expectation value of the treatment or the coverage probability of the target volumes during treatment planning. In this note we show that approaches relying on objectives to induce a specific coverage of the clinical target volumes are inevitably sensitive to variation of the relative weighting of the objectives. To address this issue, we introduce coverage-based constraints for intensity-modulated radiation therapy (IMRT) treatment planning. Our implementation follows the concept of coverage-optimized planning that considers explicit error scenarios to calculate and optimize patient-specific probabilities q(\\hat{d}, \\hat{v}) of covering a specific target volume fraction \\hat{v} with a certain dose \\hat{d} . Using a constraint-based reformulation of coverage-based objectives we eliminate the trade-off between coverage and competing objectives during treatment planning. In-depth convergence tests including 324 treatment plan optimizations demonstrate the reliability of coverage-based constraints for varying levels of probability, dose and volume. General clinical applicability of coverage-based constraints is demonstrated for two cases. A sensitivity analysis regarding penalty variations within this planing study based on IMRT treatment planning using (1) coverage-based constraints, (2) coverage-based objectives, (3) probabilistic optimization, (4) robust optimization and (5) conventional margins illustrates the potential benefit of coverage-based constraints that do not require tedious adjustment of target volume objectives.

  9. Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter

    International Nuclear Information System (INIS)

    Son, Junbo; Zhou, Shiyu; Sankavaram, Chaitanya; Du, Xinyu; Zhang, Yilu

    2016-01-01

    In this paper, a statistical prognostic method to predict the remaining useful life (RUL) of individual units based on noisy condition monitoring signals is proposed. The prediction accuracy of existing data-driven prognostic methods depends on the capability of accurately modeling the evolution of condition monitoring (CM) signals. Therefore, it is inevitable that the RUL prediction accuracy depends on the amount of random noise in CM signals. When signals are contaminated by a large amount of random noise, RUL prediction even becomes infeasible in some cases. To mitigate this issue, a robust RUL prediction method based on constrained Kalman filter is proposed. The proposed method models the CM signals subject to a set of inequality constraints so that satisfactory prediction accuracy can be achieved regardless of the noise level of signal evolution. The advantageous features of the proposed RUL prediction method is demonstrated by both numerical study and case study with real world data from automotive lead-acid batteries. - Highlights: • A computationally efficient constrained Kalman filter is proposed. • Proposed filter is integrated into an online failure prognosis framework. • A set of proper constraints significantly improves the failure prediction accuracy. • Promising results are reported in the application of battery failure prognosis.

  10. Optimal Orientation Planning and Control Deviation Estimation on FAST Cable-Driven Parallel Robot

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-03-01

    Full Text Available The paper is devoted theoretically to the optimal orientation planning and control deviation estimation of FAST cable-driven parallel robot. Regarding the robot characteristics, the solutions are obtained from two constrained optimizations, both of which are based on the equilibrium of the cabin and the attention on force allocation among 6 cable tensions. A kind of control algorithm is proposed based on the position and force feedbacks. The analysis proves that the orientation control depends on force feedback and the optimal tension solution corresponding to the planned orientation. Finally, the estimation of orientation deviation is given under the limit range of tension errors.

  11. Inexact Multistage Stochastic Chance Constrained Programming Model for Water Resources Management under Uncertainties

    Directory of Open Access Journals (Sweden)

    Hong Zhang

    2017-01-01

    Full Text Available In order to formulate water allocation schemes under uncertainties in the water resources management systems, an inexact multistage stochastic chance constrained programming (IMSCCP model is proposed. The model integrates stochastic chance constrained programming, multistage stochastic programming, and inexact stochastic programming within a general optimization framework to handle the uncertainties occurring in both constraints and objective. These uncertainties are expressed as probability distributions, interval with multiply distributed stochastic boundaries, dynamic features of the long-term water allocation plans, and so on. Compared with the existing inexact multistage stochastic programming, the IMSCCP can be used to assess more system risks and handle more complicated uncertainties in water resources management systems. The IMSCCP model is applied to a hypothetical case study of water resources management. In order to construct an approximate solution for the model, a hybrid algorithm, which incorporates stochastic simulation, back propagation neural network, and genetic algorithm, is proposed. The results show that the optimal value represents the maximal net system benefit achieved with a given confidence level under chance constraints, and the solutions provide optimal water allocation schemes to multiple users over a multiperiod planning horizon.

  12. Nonlinear optimization

    CERN Document Server

    Ruszczynski, Andrzej

    2011-01-01

    Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed and easy-to-follow proofs illustrated by numerous examples and figures. The book covers convex analysis, the theory of optimality conditions, duality theory, and numerical methods for solving unconstrained and constrained optimization problems. It addresses not only classical material but also modern top...

  13. Adaptive Parameter Optimization of a Grid-based Conceptual Hydrological Model

    Science.gov (United States)

    Samaniego, L.; Kumar, R.; Attinger, S.

    2007-12-01

    Any spatially explicit hydrological model at the mesoscale is a conceptual approximation of the hydrological cycle and its dominant process occurring at this scale. Manual-expert calibration of this type of models may become quite tedious---if not impossible---taking into account the enormous amount of data required by these kind of models and the intrinsic uncertainty of both the data (input-output) and the model structure. Additionally, the model should be able to reproduce well several process which are accounted by a number of predefined objectives. As a consequence, some degree of automatic calibration would be required to find "good" solutions, each one constituting a trade-off among all calibration criteria. In other words, it is very likely that a number of parameter sets fulfil the optimization criteria and thus can be considered a model solution. In this study, we dealt with two research questions: 1) How to assess the adequate level of model complexity so that model overparameterization is avoided? And, 2) How to find a good solution with a relatively low computational burden? In the present study, a grid-based conceptual hydrological model denoted as HBV-UFZ based on some of the original HBV concepts was employed. This model was driven by 12~h precipitation, temperature, and PET grids which are acquired either from satellite products or from data of meteorological stations. In the latter case, the data was interpolated with external drift Kriging. The first research question was addressed in this study with the implementation of nonlinear transfer functions that regionalize most model parameters as a function of other spatially distributed observables such as land cover (time dependent) and other time independent basin characteristics such as soil type, slope, aspect, geological formations among others. The second question was addressed with an adaptive constrained optimization algorithm based on a parallel implementation of simulated annealing (SA

  14. Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

    International Nuclear Information System (INIS)

    Martorell, S.; Sanchez, A.; Carlos, S.; Serradell, V.

    2002-01-01

    One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS and M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS and M-related parameters in a plant are associated with controlling risk or with satisfying requirements, and are candidate to be evaluated for their resource effectiveness in risk-informed applications. The resource versus risk-control effectiveness principles formally enter in optimization problems where the cost or the burden for the plant staff is to be minimized while the risk or the availability of the safety equipment is constrained to be at a given level, and vice versa. Optimization of TS and M has been found interesting from the very beginning. However, the resolution of such a kind of optimization problem has been limited to focus on only 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 growing interest in the last years to focus on the simultaneous and multi-criteria optimization of TS and M. In the simultaneous optimization of TS and M-related parameters based on risk (or unavailability) and cost, like in many other engineering optimization problems, one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. Genetic algorithms (GAs) have

  15. An historical survey of computational methods in optimal control.

    Science.gov (United States)

    Polak, E.

    1973-01-01

    Review of some of the salient theoretical developments in the specific area of optimal control algorithms. The first algorithms for optimal control were aimed at unconstrained problems and were derived by using first- and second-variation methods of the calculus of variations. These methods have subsequently been recognized as gradient, Newton-Raphson, or Gauss-Newton methods in function space. A much more recent addition to the arsenal of unconstrained optimal control algorithms are several variations of conjugate-gradient methods. At first, constrained optimal control problems could only be solved by exterior penalty function methods. Later algorithms specifically designed for constrained problems have appeared. Among these are methods for solving the unconstrained linear quadratic regulator problem, as well as certain constrained minimum-time and minimum-energy problems. Differential-dynamic programming was developed from dynamic programming considerations. The conditional-gradient method, the gradient-projection method, and a couple of feasible directions methods were obtained as extensions or adaptations of related algorithms for finite-dimensional problems. Finally, the so-called epsilon-methods combine the Ritz method with penalty function techniques.

  16. A New Self-Constrained Inversion Method of Potential Fields Based on Probability Tomography

    Science.gov (United States)

    Sun, S.; Chen, C.; WANG, H.; Wang, Q.

    2014-12-01

    The self-constrained inversion method of potential fields uses a priori information self-extracted from potential field data. Differing from external a priori information, the self-extracted information are generally parameters derived exclusively from the analysis of the gravity and magnetic data (Paoletti et al., 2013). Here we develop a new self-constrained inversion method based on probability tomography. Probability tomography doesn't need any priori information, as well as large inversion matrix operations. Moreover, its result can describe the sources, especially the distribution of which is complex and irregular, entirely and clearly. Therefore, we attempt to use the a priori information extracted from the probability tomography results to constrain the inversion for physical properties. The magnetic anomaly data was taken as an example in this work. The probability tomography result of magnetic total field anomaly(ΔΤ) shows a smoother distribution than the anomalous source and cannot display the source edges exactly. However, the gradients of ΔΤ are with higher resolution than ΔΤ in their own direction, and this characteristic is also presented in their probability tomography results. So we use some rules to combine the probability tomography results of ∂ΔΤ⁄∂x, ∂ΔΤ⁄∂y and ∂ΔΤ⁄∂z into a new result which is used for extracting a priori information, and then incorporate the information into the model objective function as spatial weighting functions to invert the final magnetic susceptibility. Some magnetic synthetic examples incorporated with and without a priori information extracted from the probability tomography results were made to do comparison, results of which show that the former are more concentrated and with higher resolution of the source body edges. This method is finally applied in an iron mine in China with field measured ΔΤ data and performs well. ReferencesPaoletti, V., Ialongo, S., Florio, G., Fedi, M

  17. Topology optimization problems with design-dependent sets of constraints

    DEFF Research Database (Denmark)

    Schou, Marie-Louise Højlund

    Topology optimization is a design tool which is used in numerous fields. It can be used whenever the design is driven by weight and strength considerations. The basic concept of topology optimization is the interpretation of partial differential equation coefficients as effective material...... properties and designing through changing these coefficients. For example, consider a continuous structure. Then the basic concept is to represent this structure by small pieces of material that are coinciding with the elements of a finite element model of the structure. This thesis treats stress constrained...... structural topology optimization problems. For such problems a stress constraint for an element should only be present in the optimization problem when the structural design variable corresponding to this element has a value greater than zero. We model the stress constrained topology optimization problem...

  18. Pareto optimality in infinite horizon linear quadratic differential games

    NARCIS (Netherlands)

    Reddy, P.V.; Engwerda, J.C.

    2013-01-01

    In this article we derive conditions for the existence of Pareto optimal solutions for linear quadratic infinite horizon cooperative differential games. First, we present a necessary and sufficient characterization for Pareto optimality which translates to solving a set of constrained optimal

  19. Splines and polynomial tools for flatness-based constrained motion planning

    Science.gov (United States)

    Suryawan, Fajar; De Doná, José; Seron, María

    2012-08-01

    This article addresses the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parametrisation for the flat output, the performance output, the states and the inputs. Using this parametrisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). Simulation examples are presented, involving the generation of rest-to-rest trajectories. In addition, an experimental result of the method is also presented, where two methods to generate trajectories for a magnetic-levitation (maglev) system in the presence of constraints are compared and each method's performance is discussed. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on a real maglev system reported here show that, in most scenarios, the nonlinear and linearised models produce almost similar, indistinguishable trajectories.

  20. 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.

  1. Pixel-based OPC optimization based on conjugate gradients.

    Science.gov (United States)

    Ma, Xu; Arce, Gonzalo R

    2011-01-31

    Optical proximity correction (OPC) methods are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the mask is divided into small pixels, each of which is modified during the optimization process. Two critical issues in PBOPC are the required computational complexity of the optimization process, and the manufacturability of the optimized mask. Most current OPC optimization methods apply the steepest descent (SD) algorithm to improve image fidelity augmented by regularization penalties to reduce the complexity of the mask. Although simple to implement, the SD algorithm converges slowly. The existing regularization penalties, however, fall short in meeting the mask rule check (MRC) requirements often used in semiconductor manufacturing. This paper focuses on developing OPC optimization algorithms based on the conjugate gradient (CG) method which exhibits much faster convergence than the SD algorithm. The imaging formation process is represented by the Fourier series expansion model which approximates the partially coherent system as a sum of coherent systems. In order to obtain more desirable manufacturability properties of the mask pattern, a MRC penalty is proposed to enlarge the linear size of the sub-resolution assistant features (SRAFs), as well as the distances between the SRAFs and the main body of the mask. Finally, a projection method is developed to further reduce the complexity of the optimized mask pattern.

  2. Development of GPT-based optimization algorithm

    International Nuclear Information System (INIS)

    White, J.R.; Chapman, D.M.; Biswas, D.

    1985-01-01

    The University of Lowell and Westinghouse Electric Corporation are involved in a joint effort to evaluate the potential benefits of generalized/depletion perturbation theory (GPT/DTP) methods for a variety of light water reactor (LWR) physics applications. One part of that work has focused on the development of a GPT-based optimization algorithm for the overall design, analysis, and optimization of LWR reload cores. The use of GPT sensitivity data in formulating the fuel management optimization problem is conceptually straightforward; it is the actual execution of the concept that is challenging. Thus, the purpose of this paper is to address some of the major difficulties, to outline our approach to these problems, and to present some illustrative examples of an efficient GTP-based optimization scheme

  3. Bare-Bones Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2014-01-01

    Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

  4. Design of an optimization algorithm for clinical use

    International Nuclear Information System (INIS)

    Gustafsson, Anders

    1995-01-01

    Radiation therapy optimization has received much attention in the past few years. In combination with biological objective functions, the different optimization schemes has shown a potential to considerably increase the treatment outcome. With improved radiobiological models and increased computer capacity, radiation therapy optimization has now reached a stage where implementation in a clinical treatment planning system is realistic. A radiation therapy optimization method has been investigated with respect to its feasibility as a tool in a clinical 3D treatment planning system. The optimization algorithm is a constrained iterative gradient method. Photon dose calculation is performed using the clinically validated pencil-beam based algorithm of the clinical treatment planning system. Dose calculation within the optimization scheme is very time consuming and measures are required to decrease the calculation time. Different methods for more effective dose calculation within the optimization scheme have been investigated. The optimization results for adaptive sampling of calculation points, and secondary effect approximations in the dose calculation algorithm are compared with the optimization result for accurate dose calculation in all voxels of interest

  5. Conformationally constrained farnesoid X receptor (FXR) agonists: alternative replacements of the stilbene.

    Science.gov (United States)

    Akwabi-Ameyaw, Adwoa; Caravella, Justin A; Chen, Lihong; Creech, Katrina L; Deaton, David N; Madauss, Kevin P; Marr, Harry B; Miller, Aaron B; Navas, Frank; Parks, Derek J; Spearing, Paul K; Todd, Dan; Williams, Shawn P; Wisely, G Bruce

    2011-10-15

    To further explore the optimum placement of the acid moiety in conformationally constrained analogs of GW 4064 1a, a series of stilbene replacements were prepared. The benzothiophene 1f and the indole 1g display the optimal orientation of the carboxylate for enhanced FXR agonist potency. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. A Digital Hysteresis Current Control for Half-Bridge Inverters with Constrained Switching Frequency

    Directory of Open Access Journals (Sweden)

    Triet Nguyen-Van

    2017-10-01

    Full Text Available This paper proposes a new robustly adaptive hysteresis current digital control algorithm for half-bridge inverters, which plays an important role in electric power, and in various applications in electronic systems. The proposed control algorithm is assumed to be implemented on a high-speed Field Programmable Gate Array (FPGA circuit, using measured data with high sampling frequency. The hysteresis current band is computed in each switching modulation period based on both the current error and the negative half switching period during the previous modulation period, in addition to the conventionally used voltages measured at computation instants. The proposed control algorithm is derived by solving the optimization problem—where the switching frequency is always constrained at below the desired constant frequency—which is not guaranteed by the conventional method. The optimization problem also keeps the output current stable around the reference, and minimizes power loss. Simulation results show good performances of the proposed algorithm compared with the conventional one.

  7. Minimum energy control and optimal-satisfactory control of Boolean control network

    International Nuclear Information System (INIS)

    Li, Fangfei; Lu, Xiwen

    2013-01-01

    In the literatures, to transfer the Boolean control network from the initial state to the desired state, the expenditure of energy has been rarely considered. Motivated by this, this Letter investigates the minimum energy control and optimal-satisfactory control of Boolean control network. Based on the semi-tensor product of matrices and Floyd's algorithm, minimum energy, constrained minimum energy and optimal-satisfactory control design for Boolean control network are given respectively. A numerical example is presented to illustrate the efficiency of the obtained results.

  8. A penalization approach to linear programming duality with application to capacity constrained transport

    OpenAIRE

    Korman, Jonathan; McCann, Robert J.; Seis, Christian

    2013-01-01

    A new approach to linear programming duality is proposed which relies on quadratic penalization, so that the relation between solutions to the penalized primal and dual problems becomes affine. This yields a new proof of Levin's duality theorem for capacity-constrained optimal transport as an infinite-dimensional application.

  9. Interactive Reliability-Based Optimal Design

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle; Siemaszko, A.

    1994-01-01

    Interactive design/optimization of large, complex structural systems is considered. The objective function is assumed to model the expected costs. The constraints are reliability-based and/or related to deterministic code requirements. Solution of this optimization problem is divided in four main...... tasks, namely finite element analyses, sensitivity analyses, reliability analyses and application of an optimization algorithm. In the paper it is shown how these four tasks can be linked effectively and how existing information on design variables, Lagrange multipliers and the Hessian matrix can...

  10. Exploring the Metabolic and Perceptual Correlates of Self-Selected Walking Speed under Constrained and Un-Constrained Conditions

    Directory of Open Access Journals (Sweden)

    David T Godsiff, Shelly Coe, Charlotte Elsworth-Edelsten, Johnny Collett, Ken Howells, Martyn Morris, Helen Dawes

    2018-03-01

    Full Text Available Mechanisms underpinning self-selected walking speed (SSWS are poorly understood. The present study investigated the extent to which SSWS is related to metabolism, energy cost, and/or perceptual parameters during both normal and artificially constrained walking. Fourteen participants with no pathology affecting gait were tested under standard conditions. Subjects walked on a motorized treadmill at speeds derived from their SSWS as a continuous protocol. RPE scores (CR10 and expired air to calculate energy cost (J.kg-1.m-1 and carbohydrate (CHO oxidation rate (J.kg-1.min-1 were collected during minutes 3-4 at each speed. Eight individuals were re-tested under the same conditions within one week with a hip and knee-brace to immobilize their right leg. Deflection in RPE scores (CR10 and CHO oxidation rate (J.kg-1.min-1 were not related to SSWS (five and three people had deflections in the defined range of SSWS in constrained and unconstrained conditions, respectively (p > 0.05. Constrained walking elicited a higher energy cost (J.kg-1.m-1 and slower SSWS (p 0.05. SSWS did not occur at a minimum energy cost (J.kg-1.m-1 in either condition, however, the size of the minimum energy cost to SSWS disparity was the same (Froude {Fr} = 0.09 in both conditions (p = 0.36. Perceptions of exertion can modify walking patterns and therefore SSWS and metabolism/ energy cost are not directly related. Strategies which minimize perceived exertion may enable faster walking in people with altered gait as our findings indicate they should self-optimize to the same extent under different conditions.

  11. Preconditioning for partial differential equation constrained optimization with control constraints

    KAUST Repository

    Stoll, Martin

    2011-10-18

    Optimal control problems with partial differential equations play an important role in many applications. The inclusion of bound constraints for the control poses a significant additional challenge for optimization methods. In this paper, we propose preconditioners for the saddle point problems that arise when a primal-dual active set method is used. We also show for this method that the same saddle point system can be derived when the method is considered as a semismooth Newton method. In addition, the projected gradient method can be employed to solve optimization problems with simple bounds, and we discuss the efficient solution of the linear systems in question. In the case when an acceleration technique is employed for the projected gradient method, this again yields a semismooth Newton method that is equivalent to the primal-dual active set method. We also consider the Moreau-Yosida regularization method for control constraints and efficient preconditioners for this technique. Numerical results illustrate the competitiveness of these approaches. © 2011 John Wiley & Sons, Ltd.

  12. Preconditioning for partial differential equation constrained optimization with control constraints

    KAUST Repository

    Stoll, Martin; Wathen, Andy

    2011-01-01

    Optimal control problems with partial differential equations play an important role in many applications. The inclusion of bound constraints for the control poses a significant additional challenge for optimization methods. In this paper, we propose preconditioners for the saddle point problems that arise when a primal-dual active set method is used. We also show for this method that the same saddle point system can be derived when the method is considered as a semismooth Newton method. In addition, the projected gradient method can be employed to solve optimization problems with simple bounds, and we discuss the efficient solution of the linear systems in question. In the case when an acceleration technique is employed for the projected gradient method, this again yields a semismooth Newton method that is equivalent to the primal-dual active set method. We also consider the Moreau-Yosida regularization method for control constraints and efficient preconditioners for this technique. Numerical results illustrate the competitiveness of these approaches. © 2011 John Wiley & Sons, Ltd.

  13. Space engineering modeling and optimization with case studies

    CERN Document Server

    Pintér, János

    2016-01-01

    This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...

  14. Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging through Chance Constrained Mixed-Integer Programming

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Oren, Shmuel S.

    2017-01-01

    This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic...

  15. A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2016-04-01

    Full Text Available With the increasing demands for better transmission speed and robust quality of service (QoS, the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users. A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives.

  16. Global optimization framework for solar building design

    Science.gov (United States)

    Silva, N.; Alves, N.; Pascoal-Faria, P.

    2017-07-01

    The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

  17. 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.

  18. Mesh dependence in PDE-constrained optimisation an application in tidal turbine array layouts

    CERN Document Server

    Schwedes, Tobias; Funke, Simon W; Piggott, Matthew D

    2017-01-01

    This book provides an introduction to PDE-constrained optimisation using finite elements and the adjoint approach. The practical impact of the mathematical insights presented here are demonstrated using the realistic scenario of the optimal placement of marine power turbines, thereby illustrating the real-world relevance of best-practice Hilbert space aware approaches to PDE-constrained optimisation problems. Many optimisation problems that arise in a real-world context are constrained by partial differential equations (PDEs). That is, the system whose configuration is to be optimised follows physical laws given by PDEs. This book describes general Hilbert space formulations of optimisation algorithms, thereby facilitating optimisations whose controls are functions of space. It demonstrates the importance of methods that respect the Hilbert space structure of the problem by analysing the mathematical drawbacks of failing to do so. The approaches considered are illustrated using the optimisation problem arisin...

  19. Maximum entropy production: Can it be used to constrain conceptual hydrological models?

    Science.gov (United States)

    M.C. Westhoff; E. Zehe

    2013-01-01

    In recent years, optimality principles have been proposed to constrain hydrological models. The principle of maximum entropy production (MEP) is one of the proposed principles and is subject of this study. It states that a steady state system is organized in such a way that entropy production is maximized. Although successful applications have been reported in...

  20. Constrained bidirectional propagation and stroke segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Mori, S; Gillespie, W; Suen, C Y

    1983-03-01

    A new method for decomposing a complex figure into its constituent strokes is described. This method, based on constrained bidirectional propagation, is suitable for parallel processing. Examples of its application to the segmentation of Chinese characters are presented. 9 references.

  1. Heuristic algorithm for single resource constrained project scheduling problem based on the dynamic programming

    Directory of Open Access Journals (Sweden)

    Stanimirović Ivan

    2009-01-01

    Full Text Available We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm, as well as with Microsoft Project.

  2. Rate-distortion optimization for compressive video sampling

    Science.gov (United States)

    Liu, Ying; Vijayanagar, Krishna R.; Kim, Joohee

    2014-05-01

    The recently introduced compressed sensing (CS) framework enables low complexity video acquisition via sub- Nyquist rate sampling. In practice, the resulting CS samples are quantized and indexed by finitely many bits (bit-depth) for transmission. In applications where the bit-budget for video transmission is constrained, rate- distortion optimization (RDO) is essential for quality video reconstruction. In this work, we develop a double-level RDO scheme for compressive video sampling, where frame-level RDO is performed by adaptively allocating the fixed bit-budget per frame to each video block based on block-sparsity, and block-level RDO is performed by modelling the block reconstruction peak-signal-to-noise ratio (PSNR) as a quadratic function of quantization bit-depth. The optimal bit-depth and the number of CS samples are then obtained by setting the first derivative of the function to zero. In the experimental studies the model parameters are initialized with a small set of training data, which are then updated with local information in the model testing stage. Simulation results presented herein show that the proposed double-level RDO significantly enhances the reconstruction quality for a bit-budget constrained CS video transmission system.

  3. Optimal control of hybrid vehicles

    CERN Document Server

    Jager, Bram; Kessels, John

    2013-01-01

    Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on Pontryagin’s maximum principle.   Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Two case studies are included in the book: ·        a control strategy for a micro-hybrid power train; and ·        experimental results obtained with a real-time strategy implemented in...

  4. CFD-based optimization in plastics extrusion

    Science.gov (United States)

    Eusterholz, Sebastian; Elgeti, Stefanie

    2018-05-01

    This paper presents novel ideas in numerical design of mixing elements in single-screw extruders. The actual design process is reformulated as a shape optimization problem, given some functional, but possibly inefficient initial design. Thereby automatic optimization can be incorporated and the design process is advanced, beyond the simulation-supported, but still experience-based approach. This paper proposes concepts to extend a method which has been developed and validated for die design to the design of mixing-elements. For simplicity, it focuses on single-phase flows only. The developed method conducts forward-simulations to predict the quasi-steady melt behavior in the relevant part of the extruder. The result of each simulation is used in a black-box optimization procedure based on an efficient low-order parameterization of the geometry. To minimize user interaction, an objective function is formulated that quantifies the products' quality based on the forward simulation. This paper covers two aspects: (1) It reviews the set-up of the optimization framework as discussed in [1], and (2) it details the necessary extensions for the optimization of mixing elements in single-screw extruders. It concludes with a presentation of first advances in the unsteady flow simulation of a metering and mixing section with the SSMUM [2] using the Carreau material model.

  5. Reliability Based Optimization of Fire Protection

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    fire protection (PFP) of firewalls and structural members. The paper is partly based on research performed within the EU supported research project B/E-4359 "Optimized Fire Safety of Offshore Structures" and partly on research supported by the Danish Technical Research Council (see Thoft-Christensen [1......]). Special emphasis is put on the optimization software developed within the project.......It is well known that fire is one of the major risks of serious damage or total loss of several types of structures such as nuclear installations, buildings, offshore platforms/topsides etc. This paper presents a methodology and software for reliability based optimization of the layout of passive...

  6. Constrained Vapor Bubble Experiment

    Science.gov (United States)

    Gokhale, Shripad; Plawsky, Joel; Wayner, Peter C., Jr.; Zheng, Ling; Wang, Ying-Xi

    2002-11-01

    Microgravity experiments on the Constrained Vapor Bubble Heat Exchanger, CVB, are being developed for the International Space Station. In particular, we present results of a precursory experimental and theoretical study of the vertical Constrained Vapor Bubble in the Earth's environment. A novel non-isothermal experimental setup was designed and built to study the transport processes in an ethanol/quartz vertical CVB system. Temperature profiles were measured using an in situ PC (personal computer)-based LabView data acquisition system via thermocouples. Film thickness profiles were measured using interferometry. A theoretical model was developed to predict the curvature profile of the stable film in the evaporator. The concept of the total amount of evaporation, which can be obtained directly by integrating the experimental temperature profile, was introduced. Experimentally measured curvature profiles are in good agreement with modeling results. For microgravity conditions, an analytical expression, which reveals an inherent relation between temperature and curvature profiles, was derived.

  7. Decision-Making Approach to Selecting Optimal Platform of Service Variants

    Directory of Open Access Journals (Sweden)

    Vladimir Modrak

    2016-01-01

    Full Text Available Nowadays, it is anticipated that service sector companies will be inspired to follow mass customization trends of industrial sector. However, services are more abstract than products and therefore concepts for mass customization in manufacturing domain cannot be transformed without a methodical change. This paper is focused on the development of a methodological framework to support decisions in a selection of optimal platform of service variants when compatibility problems between service options occurred. The approach is based on mutual relations between waste and constrained design space entropy. For this purpose, software for quantification of constrained and waste design space is developed. Practicability of the methodology is presented on a realistic case.

  8. The evolutionary value of recombination is constrained by genome modularity.

    Directory of Open Access Journals (Sweden)

    Darren P Martin

    2005-10-01

    Full Text Available Genetic recombination is a fundamental evolutionary mechanism promoting biological adaptation. Using engineered recombinants of the small single-stranded DNA plant virus, Maize streak virus (MSV, we experimentally demonstrate that fragments of genetic material only function optimally if they reside within genomes similar to those in which they evolved. The degree of similarity necessary for optimal functionality is correlated with the complexity of intragenomic interaction networks within which genome fragments must function. There is a striking correlation between our experimental results and the types of MSV recombinants that are detectable in nature, indicating that obligatory maintenance of intragenome interaction networks strongly constrains the evolutionary value of recombination for this virus and probably for genomes in general.

  9. Food processing optimization using evolutionary algorithms | Enitan ...

    African Journals Online (AJOL)

    Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the ...

  10. 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.

  11. Constrained ripple optimization of Tokamak bundle divertors

    International Nuclear Information System (INIS)

    Hively, L.M.; Rome, J.A.; Lynch, V.E.; Lyon, J.F.; Fowler, R.H.; Peng, Y-K.M.; Dory, R.A.

    1983-02-01

    Magnetic field ripple from a tokamak bundle divertor is localized to a small toroidal sector and must be treated differently from the usual (distributed) toroidal field (TF) coil ripple. Generally, in a tokamak with an unoptimized divertor design, all of the banana-trapped fast ions are quickly lost due to banana drift diffusion or to trapping between the 1/R variation in absolute value vector B ω B and local field maxima due to the divertor. A computer code has been written to optimize automatically on-axis ripple subject to these constraints, while varying up to nine design parameters. Optimum configurations have low on-axis ripple ( 0 ) are lost. However, because finite-sized TF coils have not been used in this study, the flux bundle is not expanded

  12. Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

    Directory of Open Access Journals (Sweden)

    Pan Pan

    2012-11-01

    Full Text Available This paper presents an optimization method for the structural design of horizontal-axis wind turbine (HAWT blades based on the particle swarm optimization algorithm (PSO combined with the finite element method (FEM. The main goal is to create an optimization tool and to demonstrate the potential improvements that could be brought to the structural design of HAWT blades. A multi-criteria constrained optimization design model pursued with respect to minimum mass of the blade is developed. The number and the location of layers in the spar cap and the positions of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining the above method and design model under ultimate (extreme flap-wise load conditions. The optimization results are described and compared with the original design. It shows that the method used in this study is efficient and produces improved designs.

  13. 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.

  14. Constrained non-linear optimization in 3D reflexion tomography; Problemes d'optimisation non-lineaire avec contraintes en tomographie de reflexion 3D

    Energy Technology Data Exchange (ETDEWEB)

    Delbos, F

    2004-11-01

    Reflexion tomography allows the determination of a subsurface velocity model from the travel times of seismic waves. The introduction of a priori information in this inverse problem can lead to the resolution of a constrained non-linear least-squares problem. The goal of the thesis is to improve the resolution techniques of this optimization problem, whose main difficulties are its ill-conditioning, its large scale and an expensive cost function in terms of CPU time. Thanks to a detailed study of the problem and to numerous numerical experiments, we justify the use of a sequential quadratic programming method, in which the tangential quadratic programs are solved by an original augmented Lagrangian method. We show the global linear convergence of the latter. The efficiency and robustness of the approach are demonstrated on several synthetic examples and on two real data cases. (author)

  15. A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    WANG Shaoyu

    2016-12-01

    Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.

  16. Optimization of tokamak plasma equilibrium shape using parallel genetic algorithms

    International Nuclear Information System (INIS)

    Zhulin An; Bin Wu; Lijian Qiu

    2006-01-01

    In the device of non-circular cross sectional tokamaks, the plasma equilibrium shape has a strong influence on the confinement and MHD stability. The plasma equilibrium shape is determined by the configuration of the poloidal field (PF) system. Usually there are many PF systems that could support the specified plasma equilibrium, the differences are the number of coils used, their positions, sizes and currents. It is necessary to find the optimal choice that meets the engineering constrains, which is often done by a constrained optimization. The Genetic Algorithms (GAs) based method has been used to solve the problem of the optimization, but the time complexity limits the algorithms to become widely used. Due to the large search space that the optimization has, it takes several hours to get a nice result. The inherent parallelism in GAs can be exploited to enhance their search efficiency. In this paper, we introduce a parallel genetic algorithms (PGAs) based approach which can reduce the computational time. The algorithm has a master-slave structure, the slave explore the search space separately and return the results to the master. A program is also developed, and it can be running on any computers which support massage passing interface. Both the algorithm and the program are detailed discussed in the paper. We also include an application that uses the program to determine the positions and currents of PF coils in EAST. The program reach the target value within half an hour and yield a speedup rate of 5.21 on 8 CPUs. (author)

  17. Distance Constrained Based Adaptive Flocking Control for Multiagent Networks with Time Delay

    Directory of Open Access Journals (Sweden)

    Qing Zhang

    2015-01-01

    Full Text Available The flocking control of multiagent system is a new type of decentralized control method, which has aroused great attention. The paper includes a detailed research in terms of distance constrained based adaptive flocking control for multiagent system with time delay. Firstly, the program on the adaptive flocking with time delay of multiagent is proposed. Secondly, a kind of adaptive controllers and updating laws are presented. According to the Lyapunov stability theory, it is proved that the distance between agents can be larger than a constant during the motion evolution. What is more, velocities of each agent come to the same asymptotically. Finally, the analytical results can be verified by a numerical example.

  18. Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements

    International Nuclear Information System (INIS)

    Liu, Fan; Bie, Zhaohong; Liu, Shiyu; Ding, Tao

    2017-01-01

    Highlights: • Analyzing zonal reserve requirements for wind integrated power system. • Modeling day-ahead optimal dispatch solved by chance constrained programming theory. • Determining optimal zonal reserve demand with minimum confidence interval. • Analyzing numerical results on test and large-scale real-life power systems. - Abstract: Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage.

  19. Data Science and Optimal Learning for Material Discovery and Design

    Science.gov (United States)

    ; Optimal Learning for Material Discovery & Design Data Science and Optimal Learning for Material inference and optimization methods that can constrain predictions using insights and results from theory directions in the application of information theoretic tools to materials problems related to learning from

  20. Optimal Control of a Fed-Batch Fermentation Involving Multiple Feeds

    Directory of Open Access Journals (Sweden)

    Chongyang Liu

    2012-01-01

    Full Text Available A nonlinear dynamical system, in which the feed rates of glycerol and alkali are taken as the control functions, is first proposed to formulate the fed-batch culture of 1,3-propanediol (1,3-PD production. To maximize the 1,3-PD concentration at the terminal time, a constrained optimal control model is then presented. A solution approach is developed to seek the optimal feed rates based on control vector parametrization method and improved differential evolution algorithm. The proposed methodology yielded an increase by 32.17% of 1,3-PD concentration at the terminal time.

  1. Small-kernel constrained-least-squares restoration of sampled image data

    Science.gov (United States)

    Hazra, Rajeeb; Park, Stephen K.

    1992-10-01

    Constrained least-squares image restoration, first proposed by Hunt twenty years ago, is a linear image restoration technique in which the restoration filter is derived by maximizing the smoothness of the restored image while satisfying a fidelity constraint related to how well the restored image matches the actual data. The traditional derivation and implementation of the constrained least-squares restoration filter is based on an incomplete discrete/discrete system model which does not account for the effects of spatial sampling and image reconstruction. For many imaging systems, these effects are significant and should not be ignored. In a recent paper Park demonstrated that a derivation of the Wiener filter based on the incomplete discrete/discrete model can be extended to a more comprehensive end-to-end, continuous/discrete/continuous model. In a similar way, in this paper, we show that a derivation of the constrained least-squares filter based on the discrete/discrete model can also be extended to this more comprehensive continuous/discrete/continuous model and, by so doing, an improved restoration filter is derived. Building on previous work by Reichenbach and Park for the Wiener filter, we also show that this improved constrained least-squares restoration filter can be efficiently implemented as a small-kernel convolution in the spatial domain.

  2. Canonical Duality Theory for Topology Optimization

    OpenAIRE

    Gao, David Yang

    2016-01-01

    This paper presents a canonical duality approach for solving a general topology optimization problem of nonlinear elastic structures. By using finite element method, this most challenging problem can be formulated as a mixed integer nonlinear programming problem (MINLP), i.e. for a given deformation, the first-level optimization is a typical linear constrained 0-1 programming problem, while for a given structure, the second-level optimization is a general nonlinear continuous minimization pro...

  3. Optimal control landscape for the generation of unitary transformations with constrained dynamics

    International Nuclear Information System (INIS)

    Hsieh, Michael; Wu, Rebing; Rabitz, Herschel; Lidar, Daniel

    2010-01-01

    The reliable and precise generation of quantum unitary transformations is essential for the realization of a number of fundamental objectives, such as quantum control and quantum information processing. Prior work has explored the optimal control problem of generating such unitary transformations as a surface-optimization problem over the quantum control landscape, defined as a metric for realizing a desired unitary transformation as a function of the control variables. It was found that under the assumption of nondissipative and controllable dynamics, the landscape topology is trap free, which implies that any reasonable optimization heuristic should be able to identify globally optimal solutions. The present work is a control landscape analysis, which incorporates specific constraints in the Hamiltonian that correspond to certain dynamical symmetries in the underlying physical system. It is found that the presence of such symmetries does not destroy the trap-free topology. These findings expand the class of quantum dynamical systems on which control problems are intrinsically amenable to a solution by optimal control.

  4. Reliability Based Optimization of Structural Systems

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    1987-01-01

    The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...

  5. Robust optimization-based DC optimal power flow for managing wind generation uncertainty

    Science.gov (United States)

    Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn

    2012-11-01

    Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

  6. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  7. Self-constrained inversion of potential fields

    Science.gov (United States)

    Paoletti, V.; Ialongo, S.; Florio, G.; Fedi, M.; Cella, F.

    2013-11-01

    We present a potential-field-constrained inversion procedure based on a priori information derived exclusively from the analysis of the gravity and magnetic data (self-constrained inversion). The procedure is designed to be applied to underdetermined problems and involves scenarios where the source distribution can be assumed to be of simple character. To set up effective constraints, we first estimate through the analysis of the gravity or magnetic field some or all of the following source parameters: the source depth-to-the-top, the structural index, the horizontal position of the source body edges and their dip. The second step is incorporating the information related to these constraints in the objective function as depth and spatial weighting functions. We show, through 2-D and 3-D synthetic and real data examples, that potential field-based constraints, for example, structural index, source boundaries and others, are usually enough to obtain substantial improvement in the density and magnetization models.

  8. Complementary Constrains on Component based Multiphase Flow Problems, Should It Be Implemented Locally or Globally?

    Science.gov (United States)

    Shao, H.; Huang, Y.; Kolditz, O.

    2015-12-01

    Multiphase flow problems are numerically difficult to solve, as it often contains nonlinear Phase transition phenomena A conventional technique is to introduce the complementarity constraints where fluid properties such as liquid saturations are confined within a physically reasonable range. Based on such constraints, the mathematical model can be reformulated into a system of nonlinear partial differential equations coupled with variational inequalities. They can be then numerically handled by optimization algorithms. In this work, two different approaches utilizing the complementarity constraints based on persistent primary variables formulation[4] are implemented and investigated. The first approach proposed by Marchand et.al[1] is using "local complementary constraints", i.e. coupling the constraints with the local constitutive equations. The second approach[2],[3] , namely the "global complementary constrains", applies the constraints globally with the mass conservation equation. We will discuss how these two approaches are applied to solve non-isothermal componential multiphase flow problem with the phase change phenomenon. Several benchmarks will be presented for investigating the overall numerical performance of different approaches. The advantages and disadvantages of different models will also be concluded. References[1] E.Marchand, T.Mueller and P.Knabner. Fully coupled generalized hybrid-mixed finite element approximation of two-phase two-component flow in porous media. Part I: formulation and properties of the mathematical model, Computational Geosciences 17(2): 431-442, (2013). [2] A. Lauser, C. Hager, R. Helmig, B. Wohlmuth. A new approach for phase transitions in miscible multi-phase flow in porous media. Water Resour., 34,(2011), 957-966. [3] J. Jaffré, and A. Sboui. Henry's Law and Gas Phase Disappearance. Transp. Porous Media. 82, (2010), 521-526. [4] A. Bourgeat, M. Jurak and F. Smaï. Two-phase partially miscible flow and transport modeling in

  9. Integrated Reliability-Based Optimal Design of Structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1987-01-01

    In conventional optimal design of structural systems the weight or the initial cost of the structure is usually used as objective function. Further, the constraints require that the stresses and/or strains at some critical points have to be less than some given values. Finally, all variables......-based optimal design is discussed. Next, an optimal inspection and repair strategy for existing structural systems is presented. An optimization problem is formulated , where the objective is to minimize the expected total future cost of inspection and repair subject to the constraint that the reliability...... value. The reliability can be measured from an element and/or a systems point of view. A number of methods to solve reliability-based optimization problems has been suggested, see e.g. Frangopol [I]. Murotsu et al. (2], Thoft-Christensen & Sørensen (3] and Sørensen (4). For structures where...

  10. Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

    Directory of Open Access Journals (Sweden)

    Massimiliano Kaucic

    2015-09-01

    Full Text Available In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

  11. Optimal time points sampling in pathway modelling.

    Science.gov (United States)

    Hu, Shiyan

    2004-01-01

    Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

  12. An Improved Real-Coded Population-Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems

    Directory of Open Access Journals (Sweden)

    Guo-Qiang Zeng

    2014-01-01

    Full Text Available As a novel evolutionary optimization method, extremal optimization (EO has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO in continuous optimization problems are relatively rare. This paper proposes an improved real-coded population-based EO method (IRPEO for continuous unconstrained optimization problems. The key operations of IRPEO include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally. The experimental results on 10 benchmark test functions with the dimension N=30 have shown that IRPEO is competitive or even better than the recently reported various genetic algorithm (GA versions with different mutation operations in terms of simplicity, effectiveness, and efficiency. Furthermore, the superiority of IRPEO to other evolutionary algorithms such as original population-based EO, particle swarm optimization (PSO, and the hybrid PSO-EO is also demonstrated by the experimental results on some benchmark functions.

  13. 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.

  14. Input-constrained model predictive control via the alternating direction method of multipliers

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Frison, Gianluca; Andersen, Martin S.

    2014-01-01

    This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP......) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly structured quadratic program. These quadratic programs are solved using a Riccati iteration procedure, and a structure-exploiting interior-point method, respectively. The computational cost per iteration...... is quadratic in the dimensions of the controlled system, and linear in the length of the prediction horizon. Simulations show that the approach proposed in this paper is more than an order of magnitude faster than several state-of-the-art quadratic programming algorithms, and that the difference in computation...

  15. Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems

    KAUST Repository

    Xinyu Tang,; Thomas, S.; Coleman, P.; Amato, N. M.

    2010-01-01

    reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robot's degrees of freedom

  16. The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

    Science.gov (United States)

    Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

    2018-03-01

    To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

  17. Thermodynamic optimization of geometry in engineering flow systems

    Energy Technology Data Exchange (ETDEWEB)

    Bejan, A.; Jones, J.A. [Duke Univ., Durham, NC (United States)

    2000-07-01

    This review draws attention to an emerging body of work that relies on global thermodynamic optimization in the pursuit of flow system architecture. Exergy analysis establishes the theoretical performance limit. Thermodynamic optimization (or entropy generation minimization) brings the design as closely as permissible to the theoretical limit. The design is destined to remain imperfect because of constraints (finite sizes, times, and costs). Improvements are registered by spreading the imperfection (e.g., flow resistances) through the system. Resistances compete against each other and must be optimized together. Optimal spreading means spatial distribution, geometric form, topology, and geography. System architecture springs out of constrained global optimization. The principle is illustrated by simple examples: the optimization of dimensions, spacings, and the distribution (allocation) of heat transfer surface to the two heat exchangers of a power plant. Similar opportunities for deducing flow architecture exist in more complex systems for power and refrigeration. Examples show that the complete structure of heat exchangers for environmental control systems of aircraft can be derived based on this principle. (authors)

  18. Coding for Two Dimensional Constrained Fields

    DEFF Research Database (Denmark)

    Laursen, Torben Vaarbye

    2006-01-01

    a first order model to model higher order constraints by the use of an alphabet extension. We present an iterative method that based on a set of conditional probabilities can help in choosing the large numbers of parameters of the model in order to obtain a stationary model. Explicit results are given...... for the No Isolated Bits constraint. Finally we present a variation of the encoding scheme of bit-stuffing that is applicable to the class of checkerboard constrained fields. It is possible to calculate the entropy of the coding scheme thus obtaining lower bounds on the entropy of the fields considered. These lower...... bounds are very tight for the Run-Length limited fields. Explicit bounds are given for the diamond constrained field as well....

  19. Multiple utility constrained multi-objective programs using Bayesian theory

    Science.gov (United States)

    Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

    2018-03-01

    A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

  20. Optimal Design of Gravity Pipeline Systems Using Genetic Algorithm and Mathematical Optimization

    Directory of Open Access Journals (Sweden)

    maryam rohani

    2015-03-01

    Full Text Available In recent years, the optimal design of pipeline systems has become increasingly important in the water industry. In this study, the two methods of genetic algorithm and mathematical optimization were employed for the optimal design of pipeline systems with the objective of avoiding the water hammer effect caused by valve closure. The problem of optimal design of a pipeline system is a constrained one which should be converted to an unconstrained optimization problem using an external penalty function approach in the mathematical programming method. The quality of the optimal solution greatly depends on the value of the penalty factor that is calculated by the iterative method during the optimization procedure such that the computational effort is simultaneously minimized. The results obtained were used to compare the GA and mathematical optimization methods employed to determine their efficiency and capabilities for the problem under consideration. It was found that the mathematical optimization method exhibited a slightly better performance compared to the GA method.

  1. An Equivalent Emission Minimization Strategy for Causal Optimal Control of Diesel Engines

    Directory of Open Access Journals (Sweden)

    Stephan Zentner

    2014-02-01

    Full Text Available One of the main challenges during the development of operating strategies for modern diesel engines is the reduction of the CO2 emissions, while complying with ever more stringent limits for the pollutant emissions. The inherent trade-off between the emissions of CO2 and pollutants renders a simultaneous reduction difficult. Therefore, an optimal operating strategy is sought that yields minimal CO2 emissions, while holding the cumulative pollutant emissions at the allowed level. Such an operating strategy can be obtained offline by solving a constrained optimal control problem. However, the final-value constraint on the cumulated pollutant emissions prevents this approach from being adopted for causal control. This paper proposes a framework for causal optimal control of diesel engines. The optimization problem can be solved online when the constrained minimization of the CO2 emissions is reformulated as an unconstrained minimization of the CO2 emissions and the weighted pollutant emissions (i.e., equivalent emissions. However, the weighting factors are not known a priori. A method for the online calculation of these weighting factors is proposed. It is based on the Hamilton–Jacobi–Bellman (HJB equation and a physically motivated approximation of the optimal cost-to-go. A case study shows that the causal control strategy defined by the online calculation of the equivalence factor and the minimization of the equivalent emissions is only slightly inferior to the non-causal offline optimization, while being applicable to online control.

  2. Optimal policy for value-based decision-making.

    Science.gov (United States)

    Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

    2016-08-18

    For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

  3. Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function

    Directory of Open Access Journals (Sweden)

    Yong Ma

    2013-01-01

    Full Text Available We present one algorithm based on particle swarm optimization (PSO with penalty function to determine the conflict-free path for mobile objects in four-dimension (three spatial and one-time dimensions with obstacles. The shortest path of the mobile object is set as goal function, which is constrained by conflict-free criterion, path smoothness, and velocity and acceleration requirements. This problem is formulated as a calculus of variation problem (CVP. With parametrization method, the CVP is converted to a time-varying nonlinear programming problem (TNLPP. Constraints of TNLPP are transformed to general TNLPP without any constraints through penalty functions. Then, by using a little calculations and applying the algorithm PSO, the solution of the CVP is consequently obtained. Approach efficiency is confirmed by numerical examples.

  4. Optimal Control of Evolution Mixed Variational Inclusions

    Energy Technology Data Exchange (ETDEWEB)

    Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx [Universidad Nacional Autónoma de México, Departamento de Recursos Naturales, Instituto de Geofísica (Mexico)

    2013-12-15

    Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.

  5. Optimal Control of Evolution Mixed Variational Inclusions

    International Nuclear Information System (INIS)

    Alduncin, Gonzalo

    2013-01-01

    Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory

  6. Preconditioners for state-constrained optimal control problems with Moreau-Yosida penalty function

    KAUST Repository

    Pearson, John W.; Stoll, Martin; Wathen, Andrew J.

    2012-01-01

    Optimal control problems with partial differential equations as constraints play an important role in many applications. The inclusion of bound constraints for the state variable poses a significant challenge for optimization methods. Our focus here

  7. Robust media processing on programmable power-constrained systems

    Science.gov (United States)

    McVeigh, Jeff

    2005-03-01

    To achieve consumer-level quality, media systems must process continuous streams of audio and video data while maintaining exacting tolerances on sampling rate, jitter, synchronization, and latency. While it is relatively straightforward to design fixed-function hardware implementations to satisfy worst-case conditions, there is a growing trend to utilize programmable multi-tasking solutions for media applications. The flexibility of these systems enables support for multiple current and future media formats, which can reduce design costs and time-to-market. This paper provides practical engineering solutions to achieve robust media processing on such systems, with specific attention given to power-constrained platforms. The techniques covered in this article utilize the fundamental concepts of algorithm and software optimization, software/hardware partitioning, stream buffering, hierarchical prioritization, and system resource and power management. A novel enhancement to dynamically adjust processor voltage and frequency based on buffer fullness to reduce system power consumption is examined in detail. The application of these techniques is provided in a case study of a portable video player implementation based on a general-purpose processor running a non real-time operating system that achieves robust playback of synchronized H.264 video and MP3 audio from local storage and streaming over 802.11.

  8. Optimization of the box-girder of overhead crane with constrained ...

    African Journals Online (AJOL)

    haroun

    Keywords: Overhead crane - Box-girder - New bat algorithm - level of ... much more efficiency and robustness compared to the genetic algorithm (GA) and PSO ...... optimization: developments, applications and resources," in Evolutionary.

  9. Constrained non-linear optimization in 3D reflexion tomography; Problemes d'optimisation non-lineaire avec contraintes en tomographie de reflexion 3D

    Energy Technology Data Exchange (ETDEWEB)

    Delbos, F.

    2004-11-01

    Reflexion tomography allows the determination of a subsurface velocity model from the travel times of seismic waves. The introduction of a priori information in this inverse problem can lead to the resolution of a constrained non-linear least-squares problem. The goal of the thesis is to improve the resolution techniques of this optimization problem, whose main difficulties are its ill-conditioning, its large scale and an expensive cost function in terms of CPU time. Thanks to a detailed study of the problem and to numerous numerical experiments, we justify the use of a sequential quadratic programming method, in which the tangential quadratic programs are solved by an original augmented Lagrangian method. We show the global linear convergence of the latter. The efficiency and robustness of the approach are demonstrated on several synthetic examples and on two real data cases. (author)

  10. Study on transfer optimization of urban rail transit and conventional public transport

    Science.gov (United States)

    Wang, Jie; Sun, Quan Xin; Mao, Bao Hua

    2018-04-01

    This paper mainly studies the time optimization of feeder connection between rail transit and conventional bus in a shopping center. In order to achieve the goal of connecting rail transportation effectively and optimizing the convergence between the two transportations, the things had to be done are optimizing the departure intervals, shorting the passenger transfer time and improving the service level of public transit. Based on the goal that has the minimum of total waiting time of passengers and the number of start of classes, establish the optimizing model of bus connecting of departure time. This model has some constrains such as transfer time, load factor, and the convergence of public transportation grid spacing. It solves the problems by using genetic algorithms.

  11. SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

    International Nuclear Information System (INIS)

    Qiu, J; Li, H. Harlod; Zhang, T; Yang, D; Ma, F

    2015-01-01

    Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools

  12. SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Taishan Medical University, Taian, Shandong (China); Washington University in St Louis, St Louis, MO (United States); Li, H. Harlod; Zhang, T; Yang, D [Washington University in St Louis, St Louis, MO (United States); Ma, F [Taishan Medical University, Taian, Shandong (China)

    2015-06-15

    Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.

  13. Constrained evolution in numerical relativity

    Science.gov (United States)

    Anderson, Matthew William

    The strongest potential source of gravitational radiation for current and future detectors is the merger of binary black holes. Full numerical simulation of such mergers can provide realistic signal predictions and enhance the probability of detection. Numerical simulation of the Einstein equations, however, is fraught with difficulty. Stability even in static test cases of single black holes has proven elusive. Common to unstable simulations is the growth of constraint violations. This work examines the effect of controlling the growth of constraint violations by solving the constraints periodically during a simulation, an approach called constrained evolution. The effects of constrained evolution are contrasted with the results of unconstrained evolution, evolution where the constraints are not solved during the course of a simulation. Two different formulations of the Einstein equations are examined: the standard ADM formulation and the generalized Frittelli-Reula formulation. In most cases constrained evolution vastly improves the stability of a simulation at minimal computational cost when compared with unconstrained evolution. However, in the more demanding test cases examined, constrained evolution fails to produce simulations with long-term stability in spite of producing improvements in simulation lifetime when compared with unconstrained evolution. Constrained evolution is also examined in conjunction with a wide variety of promising numerical techniques, including mesh refinement and overlapping Cartesian and spherical computational grids. Constrained evolution in boosted black hole spacetimes is investigated using overlapping grids. Constrained evolution proves to be central to the host of innovations required in carrying out such intensive simulations.

  14. GPU-Monte Carlo based fast IMRT plan optimization

    Directory of Open Access Journals (Sweden)

    Yongbao Li

    2014-03-01

    Full Text Available Purpose: Intensity-modulated radiation treatment (IMRT plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z

  15. Ring-constrained Join

    DEFF Research Database (Denmark)

    Yiu, Man Lung; Karras, Panagiotis; Mamoulis, Nikos

    2008-01-01

    . This new operation has important applications in decision support, e.g., placing recycling stations at fair locations between restaurants and residential complexes. Clearly, RCJ is defined based on a geometric constraint but not on distances between points. Thus, our operation is fundamentally different......We introduce a novel spatial join operator, the ring-constrained join (RCJ). Given two sets P and Q of spatial points, the result of RCJ consists of pairs (p, q) (where p ε P, q ε Q) satisfying an intuitive geometric constraint: the smallest circle enclosing p and q contains no other points in P, Q...

  16. Optimal power and performance trade-offs for dynamic voltage scaling in power management based wireless sensor node

    Directory of Open Access Journals (Sweden)

    Anuradha Pughat

    2016-09-01

    Full Text Available Dynamic voltage scaling contributes to a significant amount of power saving, especially in the energy constrained wireless sensor networks (WSNs. Existing dynamic voltage scaling techniques make the system slower and ignore the event miss rate. This results in degradation of the system performance when there is non-stationary workload at input. The overhead due to transition between voltage level and discrete voltage levels are also the limitations of available dynamic voltage scaling (DVS techniques at sensor node (SN. This paper proposes a workload dependent DVS based MSP430 controller model used for SN. An online gradient estimation technique has been used to optimize power and performance trade-offs. The analytical results are validated with the simulation results obtained using simulation tool “SimEvents” and compared with the available AT9OS8535 controller. Based on the stochastic workload, the controller's input voltage, operational frequency, utilization, and average wait time of events are obtained.

  17. Constrained Optimization via Stochastic approximation with a simultaneous perturbation gradient approximation

    DEFF Research Database (Denmark)

    Sadegh, Payman

    1997-01-01

    This paper deals with a projection algorithm for stochastic approximation using simultaneous perturbation gradient approximation for optimization under inequality constraints where no direct gradient of the loss function is available and the inequality constraints are given as explicit functions...... of the optimization parameters. It is shown that, under application of the projection algorithm, the parameter iterate converges almost surely to a Kuhn-Tucker point, The procedure is illustrated by a numerical example, (C) 1997 Elsevier Science Ltd....

  18. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...

  19. EUD-based biological optimization for carbon ion therapy

    International Nuclear Information System (INIS)

    Brüningk, Sarah C.; Kamp, Florian; Wilkens, Jan J.

    2015-01-01

    Purpose: Treatment planning for carbon ion therapy requires an accurate modeling of the biological response of each tissue to estimate the clinical outcome of a treatment. The relative biological effectiveness (RBE) accounts for this biological response on a cellular level but does not refer to the actual impact on the organ as a whole. For photon therapy, the concept of equivalent uniform dose (EUD) represents a simple model to take the organ response into account, yet so far no formulation of EUD has been reported that is suitable to carbon ion therapy. The authors introduce the concept of an equivalent uniform effect (EUE) that is directly applicable to both ion and photon therapies and exemplarily implemented it as a basis for biological treatment plan optimization for carbon ion therapy. Methods: In addition to a classical EUD concept, which calculates a generalized mean over the RBE-weighted dose distribution, the authors propose the EUE to simplify the optimization process of carbon ion therapy plans. The EUE is defined as the biologically equivalent uniform effect that yields the same probability of injury as the inhomogeneous effect distribution in an organ. Its mathematical formulation is based on the generalized mean effect using an effect-volume parameter to account for different organ architectures and is thus independent of a reference radiation. For both EUD concepts, quadratic and logistic objective functions are implemented into a research treatment planning system. A flexible implementation allows choosing for each structure between biological effect constraints per voxel and EUD constraints per structure. Exemplary treatment plans are calculated for a head-and-neck patient for multiple combinations of objective functions and optimization parameters. Results: Treatment plans optimized using an EUE-based objective function were comparable to those optimized with an RBE-weighted EUD-based approach. In agreement with previous results from photon

  20. Bounds on the Capacity of Weakly constrained two-dimensional Codes

    DEFF Research Database (Denmark)

    Forchhammer, Søren

    2002-01-01

    Upper and lower bounds are presented for the capacity of weakly constrained two-dimensional codes. The maximum entropy is calculated for two simple models of 2-D codes constraining the probability of neighboring 1s as an example. For given models of the coded data, upper and lower bounds...... on the capacity for 2-D channel models based on occurrences of neighboring 1s are considered....

  1. A superlinear interior points algorithm for engineering design optimization

    Science.gov (United States)

    Herskovits, J.; Asquier, J.

    1990-01-01

    We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.

  2. Biogeography-Based Optimization with Orthogonal Crossover

    Directory of Open Access Journals (Sweden)

    Quanxi Feng

    2013-01-01

    Full Text Available Biogeography-based optimization (BBO is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm.

  3. Extended Information Ratio for Portfolio Optimization Using Simulated Annealing with Constrained Neighborhood

    Science.gov (United States)

    Orito, Yukiko; Yamamoto, Hisashi; Tsujimura, Yasuhiro; Kambayashi, Yasushi

    The portfolio optimizations are to determine the proportion-weighted combination in the portfolio in order to achieve investment targets. This optimization is one of the multi-dimensional combinatorial optimizations and it is difficult for the portfolio constructed in the past period to keep its performance in the future period. In order to keep the good performances of portfolios, we propose the extended information ratio as an objective function, using the information ratio, beta, prime beta, or correlation coefficient in this paper. We apply the simulated annealing (SA) to optimize the portfolio employing the proposed ratio. For the SA, we make the neighbor by the operation that changes the structure of the weights in the portfolio. In the numerical experiments, we show that our portfolios keep the good performances when the market trend of the future period becomes different from that of the past period.

  4. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

    Directory of Open Access Journals (Sweden)

    Bo Chen

    2015-05-01

    Full Text Available Segmentation, which is usually the first step in object-based image analysis (OBIA, greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD and the edge penalty (EP. First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition’s multi-scale segmentation.

  5. Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

    Directory of Open Access Journals (Sweden)

    Nebojsa Bacanin

    2014-01-01

    portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

  6. Segment-based dose optimization using a genetic algorithm

    International Nuclear Information System (INIS)

    Cotrutz, Cristian; Xing Lei

    2003-01-01

    Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning

  7. Optimization of radiotherapy to target volumes with concave outlines: target-dose homogenization and selective sparing of critical structures by constrained matrix inversion

    Energy Technology Data Exchange (ETDEWEB)

    Colle, C; Van den Berge, D; De Wagter, C; Fortan, L; Van Duyse, B; De Neve, W

    1995-12-01

    The design of 3D-conformal dose distributions for targets with concave outlines is a technical challenge in conformal radiotherapy. For these targets, it is impossible to find beam incidences for which the target volume can be isolated from the tissues at risk. Commonly occurring examples are most thyroid cancers and the targets located at the lower neck and upper mediastinal levels related to some head and neck. A solution to this problem was developed, using beam intensity modulation executed with a multileaf collimator by applying a static beam-segmentation technique. The method includes the definition of beam incidences and beam segments of specific shape as well as the calculation of segment weights. Tests on Sherouse`s GRATISTM planning system allowed to escalate the dose to these targets to 65-70 Gy without exceeding spinal cord tolerance. Further optimization by constrained matrix inversion was investigated to explore the possibility of further dose escalation.

  8. Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

    Science.gov (United States)

    Lu, Weidang; Lin, Yuanrong; Peng, Hong; Nan, Tian; Liu, Xin

    2017-09-13

    Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN's achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

  9. Energy aware swarm optimization with intercluster search for wireless sensor network.

    Science.gov (United States)

    Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

    2015-01-01

    Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

  10. Tongue Images Classification Based on Constrained High Dispersal Network

    Directory of Open Access Journals (Sweden)

    Dan Meng

    2017-01-01

    Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

  11. Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    David Quintana

    2017-10-01

    Full Text Available Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix.

  12. Layered and Laterally Constrained 2D Inversion of Time Domain Induced Polarization Data

    DEFF Research Database (Denmark)

    Fiandaca, Gianluca; Ramm, James; Auken, Esben

    description of the transmitter waveform and of the receiver transfer function allowing for a quantitative interpretation of the parameters. The code has been optimized for parallel computation and the inversion time is comparable to codes inverting just for direct current resistivity. The new inversion......In a sedimentary environment, quasi-layered models often represent the actual geology more accurately than smooth minimum-structure models. We have developed a new layered and laterally constrained inversion algorithm for time domain induced polarization data. The algorithm is based on the time...... transform of a complex resistivity forward response and the inversion extracts the spectral information of the time domain measures in terms of the Cole-Cole parameters. The developed forward code and inversion algorithm use the full time decay of the induced polarization response, together with an accurate...

  13. Security-constrained self-scheduling of generation companies in day-ahead electricity markets considering financial risk

    International Nuclear Information System (INIS)

    Amjady, Nima; Vahidinasab, Vahid

    2013-01-01

    Highlights: ► A security-constrained self-scheduling is presented. ► The proposed framework takes into account the uncertainty of the predicted market prices. ► We model the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. ► Unit commitment and inter-temporal constraints of generators are considered in an MIP model. ► Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems. - Abstract: In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and models the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. Furthermore, unit commitment and inter-temporal constraints of generators are considered in the suggested model converting it to a mixed-integer programming (MIP) optimization problem. Sensitivity of the proposed framework with respect to both the level of the market prices and adopted risk level is also evaluated in the paper. Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems illustrating the performance of the proposed self-scheduling model.

  14. An adaptive ES with a ranking based constraint handling strategy

    Directory of Open Access Journals (Sweden)

    Kusakci Ali Osman

    2014-01-01

    Full Text Available To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES along with a simplified Covariance Matrix Adaptation (CMA based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods.

  15. Optimization of temporal networks under uncertainty

    CERN Document Server

    Wiesemann, Wolfram

    2012-01-01

    Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl

  16. 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.

  17. Constraining dark energy with clusters: Complementarity with other probes

    International Nuclear Information System (INIS)

    Cunha, Carlos; Huterer, Dragan; Frieman, Joshua A.

    2009-01-01

    The Figure of Merit Science Working Group recently forecast the constraints on dark energy that will be achieved prior to the Joint Dark Energy Mission by ground-based experiments that exploit baryon acoustic oscillations, type Ia supernovae, and weak gravitational lensing. We show that cluster counts from ongoing and near-future surveys should provide robust, complementary dark energy constraints. In particular, we find that optimally combined optical and Sunyaev-Zel'dovich effect cluster surveys should improve the Dark Energy Task Force figure of merit for pre-Joint Dark Energy Mission projects by a factor of 2 even without prior knowledge of the nuisance parameters in the cluster mass-observable relation. Comparable improvements are achieved in the forecast precision of parameters specifying the principal component description of the dark energy equation of state parameter, as well as in the growth index γ. These results indicate that cluster counts can play an important complementary role in constraining dark energy and modified gravity even if the associated systematic errors are not strongly controlled.

  18. Characteristics and critical success factors for implementing problem-based learning in a human resource-constrained country.

    Science.gov (United States)

    Giva, Karen R N; Duma, Sinegugu E

    2015-08-31

    Problem-based learning (PBL) was introduced in Malawi in 2002 in order to improve the nursing education system and respond to the acute nursing human resources shortage. However, its implementation has been very slow throughout the country. The objectives of the study were to explore and describe the goals that were identified by the college to facilitate the implementation of PBL, the resources of the organisation that facilitated the implementation of PBL, the factors related to sources of students that facilitated the implementation of PBL, and the influence of the external system of the organisation on facilitating the implementation of PBL, and to identify critical success factors that could guide the implementation of PBL in nursing education in Malawi. This is an ethnographic, exploratory and descriptive qualitative case study. Purposive sampling was employed to select the nursing college, participants and documents for review.Three data collection methods, including semi-structured interviews, participant observation and document reviews, were used to collect data. The four steps of thematic analysis were used to analyse data from all three sources. Four themes and related subthemes emerged from the triangulated data sources. The first three themes and their subthemes are related to the characteristics related to successful implementation of PBL in a human resource-constrained nursing college, whilst the last theme is related to critical success factors that contribute to successful implementation of PBL in a human resource-constrained country like Malawi. This article shows that implementation of PBL is possible in a human resource-constrained country if there is political commitment and support.

  19. Optimal configuration of microstructure in ferroelectric materials by stochastic optimization

    Science.gov (United States)

    Jayachandran, K. P.; Guedes, J. M.; Rodrigues, H. C.

    2010-07-01

    An optimization procedure determining the ideal configuration at the microstructural level of ferroelectric (FE) materials is applied to maximize piezoelectricity. Piezoelectricity in ceramic FEs differs significantly from that of single crystals because of the presence of crystallites (grains) possessing crystallographic axes aligned imperfectly. The piezoelectric properties of a polycrystalline (ceramic) FE is inextricably related to the grain orientation distribution (texture). The set of combination of variables, known as solution space, which dictates the texture of a ceramic is unlimited and hence the choice of the optimal solution which maximizes the piezoelectricity is complicated. Thus, a stochastic global optimization combined with homogenization is employed for the identification of the optimal granular configuration of the FE ceramic microstructure with optimum piezoelectric properties. The macroscopic equilibrium piezoelectric properties of polycrystalline FE is calculated using mathematical homogenization at each iteration step. The configuration of grains characterized by its orientations at each iteration is generated using a randomly selected set of orientation distribution parameters. The optimization procedure applied to the single crystalline phase compares well with the experimental data. Apparent enhancement of piezoelectric coefficient d33 is observed in an optimally oriented BaTiO3 single crystal. Based on the good agreement of results with the published data in single crystals, we proceed to apply the methodology in polycrystals. A configuration of crystallites, simultaneously constraining the orientation distribution of the c-axis (polar axis) while incorporating ab-plane randomness, which would multiply the overall piezoelectricity in ceramic BaTiO3 is also identified. The orientation distribution of the c-axes is found to be a narrow Gaussian distribution centered around 45°. The piezoelectric coefficient in such a ceramic is found to

  20. Fragment approach to constrained density functional theory calculations using Daubechies wavelets

    International Nuclear Information System (INIS)

    Ratcliff, Laura E.; Genovese, Luigi; Mohr, Stephan; Deutsch, Thierry

    2015-01-01

    In a recent paper, we presented a linear scaling Kohn-Sham density functional theory (DFT) code based on Daubechies wavelets, where a minimal set of localized support functions are optimized in situ and therefore adapted to the chemical properties of the molecular system. Thanks to the systematically controllable accuracy of the underlying basis set, this approach is able to provide an optimal contracted basis for a given system: accuracies for ground state energies and atomic forces are of the same quality as an uncontracted, cubic scaling approach. This basis set offers, by construction, a natural subset where the density matrix of the system can be projected. In this paper, we demonstrate the flexibility of this minimal basis formalism in providing a basis set that can be reused as-is, i.e., without reoptimization, for charge-constrained DFT calculations within a fragment approach. Support functions, represented in the underlying wavelet grid, of the template fragments are roto-translated with high numerical precision to the required positions and used as projectors for the charge weight function. We demonstrate the interest of this approach to express highly precise and efficient calculations for preparing diabatic states and for the computational setup of systems in complex environments

  1. Fragment approach to constrained density functional theory calculations using Daubechies wavelets

    Energy Technology Data Exchange (ETDEWEB)

    Ratcliff, Laura E., E-mail: lratcliff@anl.gov [Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439 (United States); Université de Grenoble Alpes, CEA, INAC-SP2M, L-Sim, F-38000 Grenoble (France); Genovese, Luigi; Mohr, Stephan; Deutsch, Thierry [Université de Grenoble Alpes, CEA, INAC-SP2M, L-Sim, F-38000 Grenoble (France)

    2015-06-21

    In a recent paper, we presented a linear scaling Kohn-Sham density functional theory (DFT) code based on Daubechies wavelets, where a minimal set of localized support functions are optimized in situ and therefore adapted to the chemical properties of the molecular system. Thanks to the systematically controllable accuracy of the underlying basis set, this approach is able to provide an optimal contracted basis for a given system: accuracies for ground state energies and atomic forces are of the same quality as an uncontracted, cubic scaling approach. This basis set offers, by construction, a natural subset where the density matrix of the system can be projected. In this paper, we demonstrate the flexibility of this minimal basis formalism in providing a basis set that can be reused as-is, i.e., without reoptimization, for charge-constrained DFT calculations within a fragment approach. Support functions, represented in the underlying wavelet grid, of the template fragments are roto-translated with high numerical precision to the required positions and used as projectors for the charge weight function. We demonstrate the interest of this approach to express highly precise and efficient calculations for preparing diabatic states and for the computational setup of systems in complex environments.

  2. 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.

  3. Optimization-based topology identification of complex networks

    International Nuclear Information System (INIS)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

  4. Optimization of sources for focusing wave energy in targeted formations

    KAUST Repository

    Jeong, C; Kallivokas, L F; Huh, C; Lake, L W

    2010-01-01

    that will maximize the kinetic energy in the target zone, while keeping silent the neighbouring zones. To this end, we cast the problem as an inverse-source problem, and use a partial-differential- equation-constrained optimization approach to arrive at an optimized

  5. A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ruisheng Sun

    2016-01-01

    Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

  6. 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.

  7. Direct Speed Control of PMSM Drive Using SDRE and Convex Constrained Optimization

    Czech Academy of Sciences Publication Activity Database

    Šmídl, V.; Janouš, Š.; Adam, Lukáš; Peroutka, Z.

    2018-01-01

    Roč. 65, č. 1 (2018), s. 532-542 ISSN 1932-4529 Grant - others:GA MŠk(CZ) LO1607 Institutional support: RVO:67985556 Keywords : Velocity control * Optimization * Stators * Voltage control * Predictive control * Optimal control * Rotors Subject RIV: BD - Theory of Information Impact factor: 10.710, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/smidl-0481225.pdf

  8. Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

    Directory of Open Access Journals (Sweden)

    B. Thamaraikannan

    2014-01-01

    Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

  9. Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2015-09-01

    Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

  10. Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis.

    Science.gov (United States)

    Chen, Jun; Bushman, Frederic D; Lewis, James D; Wu, Gary D; Li, Hongzhe

    2013-04-01

    Motivated by studying the association between nutrient intake and human gut microbiome composition, we developed a method for structure-constrained sparse canonical correlation analysis (ssCCA) in a high-dimensional setting. ssCCA takes into account the phylogenetic relationships among bacteria, which provides important prior knowledge on evolutionary relationships among bacterial taxa. Our ssCCA formulation utilizes a phylogenetic structure-constrained penalty function to impose certain smoothness on the linear coefficients according to the phylogenetic relationships among the taxa. An efficient coordinate descent algorithm is developed for optimization. A human gut microbiome data set is used to illustrate this method. Both simulations and real data applications show that ssCCA performs better than the standard sparse CCA in identifying meaningful variables when there are structures in the data.

  11. Reliability-Based Optimization of Series Systems of Parallel Systems

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1993-01-01

    Reliability-based design of structural systems is considered. In particular, systems where the reliability model is a series system of parallel systems are treated. A sensitivity analysis for this class of problems is presented. Optimization problems with series systems of parallel systems...... optimization of series systems of parallel systems, but it is also efficient in reliability-based optimization of series systems in general....

  12. Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

    Directory of Open Access Journals (Sweden)

    Kyungsung An

    2017-05-01

    Full Text Available This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs; EVs interact with the grid through grid-to-vehicle (G2V and vehicle-to-grid (V2G services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs.

  13. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  14. The Two-stage Constrained Equal Awards and Losses Rules for Multi-Issue Allocation Situation

    NARCIS (Netherlands)

    Lorenzo-Freire, S.; Casas-Mendez, B.; Hendrickx, R.L.P.

    2005-01-01

    This paper considers two-stage solutions for multi-issue allocation situations.Characterisations are provided for the two-stage constrained equal awards and constrained equal losses rules, based on the properties of composition and path independence.

  15. An Interval Fuzzy-Stochastic Chance-Constrained Programming Based Energy-Water Nexus Model for Planning Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Jing Liu

    2017-11-01

    Full Text Available In this study, an interval fuzzy-stochastic chance-constrained programming based energy-water nexus (IFSCP-WEN model is developed for planning electric power system (EPS. The IFSCP-WEN model can tackle uncertainties expressed as possibility and probability distributions, as well as interval values. Different credibility (i.e., γ levels and probability (i.e., qi levels are set to reflect relationships among water supply, electricity generation, system cost, and constraint-violation risk. Results reveal that different γ and qi levels can lead to a changed system cost, imported electricity, electricity generation, and water supply. Results also disclose that the study EPS would tend to the transition from coal-dominated into clean energy-dominated. Gas-fired would be the main electric utility to supply electricity at the end of the planning horizon, occupying [28.47, 30.34]% (where 28.47% and 30.34% present the lower bound and the upper bound of interval value, respectively of the total electricity generation. Correspondingly, water allocated to gas-fired would reach the highest, occupying [33.92, 34.72]% of total water supply. Surface water would be the main water source, accounting for more than [40.96, 43.44]% of the total water supply. The ratio of recycled water to total water supply would increase by about [11.37, 14.85]%. Results of the IFSCP-WEN model present its potential for sustainable EPS planning by co-optimizing energy and water resources.

  16. Bilevel Fuzzy Chance Constrained Hospital Outpatient Appointment Scheduling Model

    Directory of Open Access Journals (Sweden)

    Xiaoyang Zhou

    2016-01-01

    Full Text Available Hospital outpatient departments operate by selling fixed period appointments for different treatments. The challenge being faced is to improve profit by determining the mix of full time and part time doctors and allocating appointments (which involves scheduling a combination of doctors, patients, and treatments to a time period in a department optimally. In this paper, a bilevel fuzzy chance constrained model is developed to solve the hospital outpatient appointment scheduling problem based on revenue management. In the model, the hospital, the leader in the hierarchy, decides the mix of the hired full time and part time doctors to maximize the total profit; each department, the follower in the hierarchy, makes the decision of the appointment scheduling to maximize its own profit while simultaneously minimizing surplus capacity. Doctor wage and demand are considered as fuzzy variables to better describe the real-life situation. Then we use chance operator to handle the model with fuzzy parameters and equivalently transform the appointment scheduling model into a crisp model. Moreover, interactive algorithm based on satisfaction is employed to convert the bilevel programming into a single level programming, in order to make it solvable. Finally, the numerical experiments were executed to demonstrate the efficiency and effectiveness of the proposed approaches.

  17. Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods

    Directory of Open Access Journals (Sweden)

    Kin Wei Ng

    2012-01-01

    Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.

  18. Reliability-Based Structural Optimization of Wave Energy Converters

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kramer, Morten; Sørensen, John Dalsgaard

    2014-01-01

    More and more wave energy converter (WEC) concepts are reaching prototype level. Once the prototype level is reached, the next step in order to further decrease the levelized cost of energy (LCOE) is optimizing the overall system with a focus on structural and maintenance (inspection) costs......, as well as on the harvested power from the waves. The target of a fully-developed WEC technology is not maximizing its power output, but minimizing the resulting LCOE. This paper presents a methodology to optimize the structural design of WECs based on a reliability-based optimization problem...

  19. Automated beam steering using optimal control

    Energy Technology Data Exchange (ETDEWEB)

    Allen, C. K. (Christopher K.)

    2004-01-01

    We present a steering algorithm which, with the aid of a model, allows the user to specify beam behavior throughout a beamline, rather than just at specified beam position monitor (BPM) locations. The model is used primarily to compute the values of the beam phase vectors from BPM measurements, and to define cost functions that describe the steering objectives. The steering problem is formulated as constrained optimization problem; however, by applying optimal control theory we can reduce it to an unconstrained optimization whose dimension is the number of control signals.

  20. Node Discovery and Interpretation in Unstructured Resource-Constrained Environments

    DEFF Research Database (Denmark)

    Gechev, Miroslav; Kasabova, Slavyana; Mihovska, Albena D.

    2014-01-01

    for the discovery, linking and interpretation of nodes in unstructured and resource-constrained network environments and their interrelated and collective use for the delivery of smart services. The model is based on a basic mathematical approach, which describes and predicts the success of human interactions...... in the context of long-term relationships and identifies several key variables in the context of communications in resource-constrained environments. The general theoretical model is described and several algorithms are proposed as part of the node discovery, identification, and linking processes in relation...

  1. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  2. Iris recognition in less constrained environments: a video-based approach

    OpenAIRE

    Mahadeo, Nitin Kumar

    2017-01-01

    This dissertation focuses on iris biometrics. Although the iris is the most accurate biometric, its adoption has been relatively slow. Conventional iris recognition systems utilize still eye images captured in ideal environments and require highly constrained subject presentation. A drop in recognition performance is observed when these constraints are removed as the quality of the data acquired is affected by heterogeneous factors. For iris recognition to be widely adopted, it can therefore ...

  3. Human fetal growth is constrained below optimal for perinatal survival

    NARCIS (Netherlands)

    Vasak, B.; Koenen, S. V.; Koster, M. P. H.; Hukkelhoven, C. W. P. M.; Franx, A.; Hanson, M. A.; Visser, GHA

    ObjectiveThe use of fetal growth charts assumes that the optimal size at birth is at the 50(th) birth-weight centile, but interaction between maternal constraints on fetal growth and the risks associated with small and large fetal size at birth may indicate that this assumption is not valid for

  4. Object-based implicit learning in visual search: perceptual segmentation constrains contextual cueing.

    Science.gov (United States)

    Conci, Markus; Müller, Hermann J; von Mühlenen, Adrian

    2013-07-09

    In visual search, detection of a target is faster when it is presented within a spatial layout of repeatedly encountered nontarget items, indicating that contextual invariances can guide selective attention (contextual cueing; Chun & Jiang, 1998). However, perceptual regularities may interfere with contextual learning; for instance, no contextual facilitation occurs when four nontargets form a square-shaped grouping, even though the square location predicts the target location (Conci & von Mühlenen, 2009). Here, we further investigated potential causes for this interference-effect: We show that contextual cueing can reliably occur for targets located within the region of a segmented object, but not for targets presented outside of the object's boundaries. Four experiments demonstrate an object-based facilitation in contextual cueing, with a modulation of context-based learning by relatively subtle grouping cues including closure, symmetry, and spatial regularity. Moreover, the lack of contextual cueing for targets located outside the segmented region was due to an absence of (latent) learning of contextual layouts, rather than due to an attentional bias towards the grouped region. Taken together, these results indicate that perceptual segmentation provides a basic structure within which contextual scene regularities are acquired. This in turn argues that contextual learning is constrained by object-based selection.

  5. Exploring Constrained Creative Communication

    DEFF Research Database (Denmark)

    Sørensen, Jannick Kirk

    2017-01-01

    Creative collaboration via online tools offers a less ‘media rich’ exchange of information between participants than face-to-face collaboration. The participants’ freedom to communicate is restricted in means of communication, and rectified in terms of possibilities offered in the interface. How do...... these constrains influence the creative process and the outcome? In order to isolate the communication problem from the interface- and technology problem, we examine via a design game the creative communication on an open-ended task in a highly constrained setting, a design game. Via an experiment the relation...... between communicative constrains and participants’ perception of dialogue and creativity is examined. Four batches of students preparing for forming semester project groups were conducted and documented. Students were asked to create an unspecified object without any exchange of communication except...

  6. A comparison of physically and radiobiologically based optimization for IMRT

    International Nuclear Information System (INIS)

    Jones, Lois; Hoban, Peter

    2002-01-01

    Many optimization techniques for intensity modulated radiotherapy have now been developed. The majority of these techniques including all the commercial systems that are available are based on physical dose methods of assessment. Some techniques have also been based on radiobiological models. None of the radiobiological optimization techniques however have assessed the clinically realistic situation of considering both tumor and normal cells within the target volume. This study considers a ratio-based fluence optimizing technique to compare a dose-based optimization method described previously and two biologically based models. The biologically based methods use the values of equivalent uniform dose calculated for the tumor cells and integral biological effective dose for normal cells. The first biologically based method includes only tumor cells in the target volume while the second considers both tumor and normal cells in the target volume. All three methods achieve good conformation to the target volume. The biologically based optimization without the normal tissue in the target volume shows a high dose region in the center of the target volume while this is reduced when the normal tissues are also considered in the target volume. This effect occurs because the normal tissues in the target volume require the optimization to reduce the dose and therefore limit the maximum dose to that volume

  7. TU-EF-204-03: Task-Based KV and MAs Optimization for Radiation Dose Reduction in CT: From FBP to Statistical Model-Based Iterative Reconstruction (MBIR)

    International Nuclear Information System (INIS)

    Gomez-Cardona, D; Li, K; Lubner, M G; Pickhardt, P J; Chen, G-H

    2015-01-01

    Purpose: The introduction of the highly nonlinear MBIR algorithm to clinical CT systems has made CNR an invalid metric for kV optimization. The purpose of this work was to develop a task-based framework to unify kV and mAs optimization for both FBP- and MBIR-based CT systems. Methods: The kV-mAs optimization was formulated as a constrained minimization problem: to select kV and mAs to minimize dose under the constraint of maintaining the detection performance as clinically prescribed. To experimentally solve this optimization problem, exhaustive measurements of detectability index (d’) for a hepatic lesion detection task were performed at 15 different mA levels and 4 kV levels using an anthropomorphic phantom. The measured d’ values were used to generate an iso-detectability map; similarly, dose levels recorded at different kV-mAs combinations were used to generate an iso-dose map. The iso-detectability map was overlaid on top of the iso-dose map so that for a prescribed detectability level d’, the optimal kV-mA can be determined from the crossing between the d’ contour and the dose contour that corresponds to the minimum dose. Results: Taking d’=16 as an example: the kV-mAs combinations on the measured iso-d’ line of MBIR are 80–150 (3.8), 100–140 (6.6), 120–150 (11.3), and 140–160 (17.2), where values in the parentheses are measured dose values. As a Result, the optimal kV was 80 and optimal mA was 150. In comparison, the optimal kV and mA for FBP were 100 and 500, which corresponded to a dose level of 24 mGy. Results of in vivo animal experiments were consistent with the phantom results. Conclusion: A new method to optimize kV and mAs selection has been developed. This method is applicable to both linear and nonlinear CT systems such as those using MBIR. Additional dose savings can be achieved by combining MBIR with this method. This work was partially supported by an NIH grant R01CA169331 and GE Healthcare. K. Li, D. Gomez-Cardona, M. G

  8. Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach

    Science.gov (United States)

    Dai, C.; Qin, X. S.; Chen, Y.; Guo, H. C.

    2018-06-01

    A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit (αSB) and Gini coefficient (αG) under different significance levels (i.e. q) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower q generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.

  9. Application of the EGM Method to a LED-Based Spotlight: A Constrained Pseudo-Optimization Design Process Based on the Analysis of the Local Entropy Generation Maps

    Directory of Open Access Journals (Sweden)

    Enrico Sciubba

    2011-06-01

    Full Text Available In this paper, the entropy generation minimization (EGM method is applied to an industrial heat transfer problem: the forced convective cooling of a LED-based spotlight. The design specification calls for eighteen diodes arranged on a circular copper plate of 35 mm diameter. Every diode dissipates 3 W and the maximum allowedtemperature of the plate is 80 °C. The cooling relies on the forced convection driven by a jet of air impinging on the plate. An initial complex geometry of plate fins is presented and analyzed with a commercial CFD code that computes the entropy generation rate. A pseudo-optimization process is carried out via a successive series of design modifications based on a careful analysis of the entropy generation maps. One of the advantages of the EGM method is that the rationale behind each step of the design process can be justified on a physical basis. It is found that the best performance is attained when the fins are periodically spaced in the radial direction.

  10. Optimization of ultrasonic arrays design and setting using a differential evolution

    International Nuclear Information System (INIS)

    Puel, B.; Chatillon, S.; Calmon, P.; Lesselier, D.

    2011-01-01

    Optimization of both design and setting of phased arrays could be not so easy when they are performed manually via parametric studies. An optimization method based on an Evolutionary Algorithm and numerical simulation is proposed and evaluated. The Randomized Adaptive Differential Evolution has been adapted to meet the specificities of the non-destructive testing applications. In particular, the solution of multi-objective problems is aimed at with the implementation of the concept of pareto-optimal sets of solutions. The algorithm has been implemented and connected to the ultrasonic simulation modules of the CIVA software used as forward model. The efficiency of the method is illustrated on two realistic cases of application: optimization of the position and delay laws of a flexible array inspecting a nozzle, considered as a mono-objective problem; and optimization of the design of a surrounded array and its delay laws, considered as a constrained bi-objective problem. (authors)

  11. Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Shanmugasundaram Thilagavathi

    2015-01-01

    Full Text Available Wireless sensor networks (WSNs are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO algorithm with modified connected dominating set (CDS based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH. Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

  12. Performance-based Pareto optimal design

    NARCIS (Netherlands)

    Sariyildiz, I.S.; Bittermann, M.S.; Ciftcioglu, O.

    2008-01-01

    A novel approach for performance-based design is presented, where Pareto optimality is pursued. Design requirements may contain linguistic information, which is difficult to bring into computation or make consistent their impartial estimations from case to case. Fuzzy logic and soft computing are

  13. Identification of different geologic units using fuzzy constrained resistivity tomography

    Science.gov (United States)

    Singh, Anand; Sharma, S. P.

    2018-01-01

    Different geophysical inversion strategies are utilized as a component of an interpretation process that tries to separate geologic units based on the resistivity distribution. In the present study, we present the results of separating different geologic units using fuzzy constrained resistivity tomography. This was accomplished using fuzzy c means, a clustering procedure to improve the 2D resistivity image and geologic separation within the iterative minimization through inversion. First, we developed a Matlab-based inversion technique to obtain a reliable resistivity image using different geophysical data sets (electrical resistivity and electromagnetic data). Following this, the recovered resistivity model was converted into a fuzzy constrained resistivity model by assigning the highest probability value of each model cell to the cluster utilizing fuzzy c means clustering procedure during the iterative process. The efficacy of the algorithm is demonstrated using three synthetic plane wave electromagnetic data sets and one electrical resistivity field dataset. The presented approach shows improvement on the conventional inversion approach to differentiate between different geologic units if the correct number of geologic units will be identified. Further, fuzzy constrained resistivity tomography was performed to examine the augmentation of uranium mineralization in the Beldih open cast mine as a case study. We also compared geologic units identified by fuzzy constrained resistivity tomography with geologic units interpreted from the borehole information.

  14. Constrained Optimization Problems in Cost and Managerial Accounting--Spreadsheet Tools

    Science.gov (United States)

    Amlie, Thomas T.

    2009-01-01

    A common problem addressed in Managerial and Cost Accounting classes is that of selecting an optimal production mix given scarce resources. That is, if a firm produces a number of different products, and is faced with scarce resources (e.g., limitations on labor, materials, or machine time), what combination of products yields the greatest profit…

  15. Novel methods for Solving Economic Dispatch of Security-Constrained Unit Commitment Based on Linear Programming

    Science.gov (United States)

    Guo, Sangang

    2017-09-01

    There are two stages in solving security-constrained unit commitment problems (SCUC) within Lagrangian framework: one is to obtain feasible units’ states (UC), the other is power economic dispatch (ED) for each unit. The accurate solution of ED is more important for enhancing the efficiency of the solution to SCUC for the fixed feasible units’ statues. Two novel methods named after Convex Combinatorial Coefficient Method and Power Increment Method respectively based on linear programming problem for solving ED are proposed by the piecewise linear approximation to the nonlinear convex fuel cost functions. Numerical testing results show that the methods are effective and efficient.

  16. Rank-Constrained Beamforming for MIMO Cognitive Interference Channel

    Directory of Open Access Journals (Sweden)

    Duoying Zhang

    2016-01-01

    Full Text Available This paper considers the spectrum sharing multiple-input multiple-output (MIMO cognitive interference channel, in which multiple primary users (PUs coexist with multiple secondary users (SUs. Interference alignment (IA approach is introduced that guarantees that secondary users access the licensed spectrum without causing harmful interference to the PUs. A rank-constrained beamforming design is proposed where the rank of the interferences and the desired signals is concerned. The standard interferences metric for the primary link, that is, interference temperature, is investigated and redesigned. The work provides a further improvement that optimizes the dimension of the interferences in the cognitive interference channel, instead of the power of the interference leakage. Due to the nonconvexity of the rank, the developed optimization problems are further approximated as convex form and are solved via choosing the transmitter precoder and receiver subspace iteratively. Numerical results show that the proposed designs can improve the achievable degree of freedom (DoF of the primary links and provide the considerable sum rate for both secondary and primary transmissions under the rank constraints.

  17. Optimization of power system operation

    CERN Document Server

    Zhu, Jizhong

    2015-01-01

    This book applies the latest applications of new technologies topower system operation and analysis, including new and importantareas that are not covered in the previous edition. Optimization of Power System Operation covers both traditional andmodern technologies, including power flow analysis, steady-statesecurity region analysis, security constrained economic dispatch,multi-area system economic dispatch, unit commitment, optimal powerflow, smart grid operation, optimal load shed, optimalreconfiguration of distribution network, power system uncertaintyanalysis, power system sensitivity analysis, analytic hierarchicalprocess, neural network, fuzzy theory, genetic algorithm,evolutionary programming, and particle swarm optimization, amongothers. New topics such as the wheeling model, multi-areawheeling, the total transfer capability computation in multipleareas, are also addressed. The new edition of this book continues to provide engineers andac demics with a complete picture of the optimization of techn...

  18. Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Feifei Dong

    2014-01-01

    Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

  19. Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability

    International Nuclear Information System (INIS)

    Han, Jing-Cheng; Huang, Guohe; Huang, Yuefei; Zhang, Hua; Li, Zhong; Chen, Qiuwen

    2015-01-01

    Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications. - Highlights: • We develop an improved hydrologic model considering the scaling effect of rainfall. • A

  20. Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability

    Energy Technology Data Exchange (ETDEWEB)

    Han, Jing-Cheng [State Key Laboratory of Hydroscience & Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084 (China); Huang, Guohe, E-mail: huang@iseis.org [Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Huang, Yuefei [State Key Laboratory of Hydroscience & Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084 (China); Zhang, Hua [College of Science and Engineering, Texas A& M University — Corpus Christi, Corpus Christi, TX 78412-5797 (United States); Li, Zhong [Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Chen, Qiuwen [Center for Eco-Environmental Research, Nanjing Hydraulics Research Institute, Nanjing 210029 (China)

    2015-08-15

    Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications. - Highlights: • We develop an improved hydrologic model considering the scaling effect of rainfall. • A