Dynamic Optimization Research Papers - Academia.edu (original) (raw)

We propose a new hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also

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- Cognitive Science, Algorithms, Medical Imaging, Computed Tomography
- by vasanth A
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- Dynamic Optimization, OPERATING SYSTEM, Design and Implementation
- by Gombosuren Adiya
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- Management Science, Dynamic Optimization, Optimal Control theory, Profitability
- by Anastasios Chassiakos
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- Applied Mathematics, Dynamic Optimization, Los Angeles, Case Study
- by Dominique Bonvin
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- Chemical Engineering, Dynamic Optimization, Polyethylene, Fluidized Bed
- by Ali Lohi and +1
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- Dynamic Optimization, Electrical And Electronic Engineering, Production Rate, Objective function
- by davood iranshahi
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- Engineering, Performance, Differential Evolution, Dynamic Optimization
- by herman Kramer
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- Optimal Control, Dynamic Optimization, Feedback Control, Optimal Control Problem

An energy management strategy–torque distribution and charge sustaining control–for a parallel hybrid electric vehicle with continuously variable transmission is proposed in this study. The torque distribution control problem is formulated as a multi-objective nonlinear optimization problem. In order to facilitate the implementation of the proposed approach in real time, the aforementioned multi-objective nonlinear optimization problem is recast as a single objective linear optimization problem by linearization of the objective functions and ...

- by Jong-Seob Won
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- Engineering, Applied Mathematics, Optimal Control, Fuzzy Logic
- by Flavio Manenti
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- Dynamic Optimization, Fortran, Dynamic Simulation, Real time optimization
- by Suresh Sethi and +1
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- Management Science, Dynamic Optimization, Optimal Control theory, Profitability

The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of Modelica models few efforts have been made

- by Mohd S Ahmad
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- Engineering, Cellular Automata, Dynamic Optimization, Traffic control
- by George Tagaras
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- Management, Engineering, Distributed Computing, Economics

In this chapter we present a mathematical formulation of complementarity dynamical systems with arbitrary dimension and arbitrary relative degree between the complementary slackness variables. The proposed model incorporates the state jumps via high-order distributions through the extension of Moreau’s sweeping process, which is a special type of differential inclusion. The time-discretization of these nonsmooth systems, which is non-trivial, is also presented. Applications of such high-order sweeping processes can be found in dynamic optimization under state constraints and electrical circuits with ideal diodes, where it may be helpful for a better understanding of the closed-loop dynamics induced by some feedback laws.

- by Vincent Acary
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- Dynamic Optimization, Solid Mechanics and Mathematics
- by Julio Banga
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- Engineering, Algorithms, Technology, Optimal Control

Numerical methods and software packages for solving dynamic optimization or optimal control problems require a suitable initial estimation of the solution. This paper focuses on problems that arise in chemical processes described by complex dynamics. We present a very simple method, based on Pontryagin’s Minimum Principle, to obtain an initial guess for the solution. Our method presents numerous advantages: it is very easy to programme, it allows a wide range of problems to be addressed, the computation time is very short, the initial guess is very close to the solution and is attracted to a global minimum.

- by Mireya Ruiz
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- Dynamic Optimization, Numerical Method, Mathematical Sciences, Mathematical chemistry
- by Anibal Blanco and +1
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- Dynamic Optimization, Food Sciences, Batch Reactor, Food control

- by Vincent Acary
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- Dynamic Optimization, Solid Mechanics and Mathematics

Optimization is becoming a standard methodology in many engineering disciplines to im- prove products and processes. The need for optimization is driven by factors such as increased costs for raw materials and stricter environmental regulations as well as a general need to meet increased com- petition. As model-based design processes are being used increasingly in industry, the prerequisites for optimization

- by Hubertus Tummescheit
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- Dynamic Optimization, Open Source, Optimization Problem, Language Use
- by Julio Banga
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- Optimal Control, Food Engineering, Dynamic Optimization, Lower Bound
- by Radoslav Paulen and +2
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- Engineering, Membrane Science, Dynamic Optimization, CHEMICAL SCIENCES
- by Jose Caballero
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- Mechanical Engineering, Chemical Engineering, Kinetics, Dynamic Optimization

This work performs the dynamic optimization of semibatch vinyl acetate (VAc)/acrylic acid (AA) suspension copolymerizations. The proposed dynamic optimization strategy is based on a direct search Complex algorithm and is used to control the copolymer composition along the batch. First, a sequential optimization procedure is used to determine the optimum AA concentration and feed rate profiles, required to provide the specified copolymer composition. In the second step, a sequential optimization procedure is coupled with a predictive controller to guarantee that the manipulation of feed flow rates can allow for attainment of the desired copolymer compositions. The optimization strategy is validated through simulation, by assuming that reactions are subject to perturbations of the reaction temperature, initiator, and VAc concentrations. It is shown that the proposed optimization strategy can be used successfully both for design of monomer feed rate profiles and removal of process disturbances during semibatch suspension copolymerizations, to keep the copolymer composition constant throughout the batch. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers

- by Fabrício Machado
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- Materials Engineering, Chemical Engineering, Modeling, Dynamic Optimization
- by Ali Abbas
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- Chemical Engineering, Kinetics, Dynamic Optimization, Parameter estimation
- by Peter Michaelis
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- Climate Change, Dynamic Optimization, Applied Economics, Global Warming
- by Alec MacCall
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- Time Series, Fisheries, Monte Carlo, Ecology
- by herman Kramer
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- Chemical Engineering, Design, Crystal Growth, Dynamic Optimization
- by Javier Andres
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- Macroeconomics, Economic Theory, Dynamic Optimization, Applied Economics

The SSPCO (See-See Particle Chicks Optimization) is a type of swarm intelligence algorithm derived from the behavior of See-See Partridge. Although efficiency of this algorithm has been proven for solving static optimization problems, it has not yet been tested to solve dynamic optimization problems. Due to the nature of NP-Hard dynamic problems, this algorithm alone is not able to solve such optimization problems. Therefore, to enable the algorithm to optimally track the variable in these problems, it is necessary to be provided solutions with this algorithm so that can increase the performance of this algorithm for dynamic environments. In this paper, two solutions for combining SSPCO are presented: (1) the multi-swarm method and (2) memory with Gaussian density estimation. The problem with most multi-swarm methods is that as the population increases uncontrollably, the speed and efficiency of the algorithm gradually decreases. The multi-swarm methods presented in this paper is adapted to the problem space, and whenever there is a need to increase the population, a population is created adaptively, and this reduces the problems of previous methods. One of the issues that is being addressed to solve uncertainty problems is prediction of near future using data of the near past. In this article, to preserve past data a new memory called Gaussian density estimation memory is used. This memory fixes standard memory defects and improves the performance of the proposed algorithm. To evaluate the efficiency of the proposed method, the well-known moving peak benchmark function, which simulates behavior of dynamic problems, is used. The proposed algorithm is compared with the 10 most popular dynamic optimization algorithms. According to the experimental results, the proposed method reduces offline error to a great extent compared to other methods and the error produced by the proposed method is very small.

- by Soft Computing Journal (SCJ)
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- Dynamic Optimization, Clustering
- by Jacob Frenkel
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- Public Finance, Economic Growth, Fiscal policy, Dynamic Optimization

In an effort to relieve peak hour congestion on freeways, various ramp metering algorithms have been employed to regulate the inputs to freeways from entry ramps. In this paper, we consider a freeway system comprised of a freeway section and its entry/exit ramps, and formulate the ramp control problem as a dynamic optimal process to minimize the total time spent in this system. Within this framework, we are able to show when ramp metering is beneficial to the system in terms of total time savings, and when it is not, under the restriction that the controlled freeway has to serve all of its ramp demand, and the traffic flow process follows the rules prescribed by the LWR theory with a triangular flow-density relationship. We also provide solution techniques to the problem and present some preliminary numerical results and empirical validation.

- by Will Recker
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- Engineering, Dynamic Optimization, Traffic Flow, Ramp Metering

In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in dynamic and uncertain environments.

This paper assesses the value of correlation dynamics in mean-variance asset allocation. A correlation-timing framework is deployed with state of the art models competing against industry correlation-updating rivals and static allocation strategies. We address the extent to which the superior statistical properties of multivariate conditional correlation models translate into enhanced investment performance. The success of the conditional correlation models is

- by Elena Kalotychou
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- Portfolio Management, Asset Allocation, Dynamic Optimization, Risk Aversion
- by Peter Voersmann
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- Dynamic Optimization, Design Methodology, Flight Dynamics, Center of Gravity
- by Terje Hertzberg and +1
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- Mechanical Engineering, Chemical Engineering, Operations Strategy, Dynamic programming
- by Ivo Maljevic and +1
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- Grid Computing, High Performance Computing, Signal Processing, Scheduling
- by Benoit Chachuat
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- Chemical Engineering, Wastewater Treatment, Energy Consumption, Activated Sludge
- by vasanth A
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- Dynamic Optimization, Design and Implementation
- by Iraj Saniee
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- Dynamic Optimization, Dynamic control, Cellular Network, Real Time
- by Suwat Sikkabut
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- Renewable Energy, Fuzzy Logic, Power System, Dynamic Optimization