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European Control Conference

作者： Tomasz Minko Rafal Wisniewski Jan Dimon Bendtsen Roozbeh Izadi-Zamanabadi Automation and Control Aalborg University Danfoss A/S Electronic Controllers & Services

ISBN: (纸本)9781467371605

In this paper, we present a simple modelling approach for a thermal system, which consists of heating, ventilation, air conditioning system (HVAC) and a vapour compression cycle (VCC) system, with one loop heat recovery. In addition a simple model for water tank is presented, in which the reclaimed heat is stored and/or which can be used for heating purposes of the building. We present a dynamic optimization algorithm that according to the received price signal, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system to generate heat during the low price period, store it and use it to substitute the conventional heater during the high price period.

关键词： heat recovery supervisory controller Refrigeration systems SUB-SYSTEM Prices local controller Supermarkets optimization algorithms thermal relief vent heat

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Moth-Flame optimization for Training Multi-layer Perceptrons

Moth-Flame Optimization for Training Multi-layer Perceptrons

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International Computer Engineering Conference

作者： Waleed Yamany Mohammed Fawzy Alaa Tharwat Aboul Ella Hassanien Fayoum University Faculty of Computers and Information Electrical Department Faculty of Engineering Suez Canal University Faculty of Computers and Information Cairo University

ISBN: (纸本)9781509002764

Multi-Layer Perceptron (MLP) is one of the Feed-Forward Neural Networks (FFNNs) types. Searching for weights and biases in MLP is important to achieve minimum training error. In this paper, Moth-Flame Optimizer (MFO) is used to train Multi-Layer Perceptron (MLP). MFO-MLP is used to search for the weights and biases of the MLP to achieve minimum error and high classification rate. Five standard classification datasets are utilized to evaluate the performance of the proposed method. Moreover, three function-approximation datasets are used to test the performance of the proposed method. The proposed method (i.e. MFO-MLP) is compared with four well-known optimization algorithms, namely, Genetic Algorithm (GA), Particle Swarm optimization (PSO), Ant Colony optimization (ACO), and Evolution Strategy (ES). The experimental results prove that the MFO algorithm is very competitive, solves the local optima problem, and it achieves a high accuracy.

关键词： CSRP3 gene Neural Networks (Computer) optimization algorithms local optimum Bias function approximation Functional training

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Directional Real-Time optimization Applied to a Kite-Control Simulation Benchmark

Directional Real-Time Optimization Applied to a Kite-Control...

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European Control Conference

作者： Sean Costello Gregory Francois Dominique Bonvin Laboratoire d'Automatique Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781467371605

This paper applies a novel two-layer optimizing control scheme to a kite-control benchmark problem. The upper layer is a recent real-time optimization algorithm, called Directional Modifier Adaptation, which represents a variation of the popular Modifier Adaptation algorithm. The lower layer consists of a path-following controller that can follow arbitrary paths. Application to a challenging benchmark scenario in simulation shows that this two-layer scheme is capable of substantially improving the performance of a complex system affected by significant stochastic disturbances, measurement noise and plant-model mismatch, while respecting operational constraints.

关键词： optimization algorithms DIRECTIONAL BENCHMARKS Complex systems simulating Modifiers Controllers Optimal control OPERATING LIMIT Benchmark testing Noise measurement

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MULTI-STRATEGY COEVOLVING AGING PARTICLE optimization

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2014年第1期24卷 1450008-1450026页

作者： Iacca, Giovanni Caraffini, Fabio Neri, Ferrante INCAS 3 Innovat Ctr Adv Sensors & Sensor Syst NL-9400 AT Assen Netherlands De Montfort Univ Sch Comp Sci & Informat CCI Leicester LE1 9BH Leics England

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

关键词： Swarm intelligence evolutionary computation optimization algorithms memetic computing robotic arms feedforward neural network

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Embedded Online optimization for Model Predictive Control at Megahertz Rates

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2014年第12期59卷 3238-3251页

作者： Jerez, Juan L. Goulart, Paul J. Richter, Stefan Constantinides, George A. Kerrigan, Eric C. Morari, Manfred ETH Automat Control Lab CH-8092 Zurich Switzerland Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med Dept Aeronaut London SW7 2AZ England

Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.

关键词： Embedded systems optimization algorithms predictive control of linear systems

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Approximation of transformation media-based reshaping action by genetic optimization

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APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 2014年第2期117卷 597-604页

作者： Ozgun, Ozlem Kuzuoglu, Mustafa TED Univ Dept Elect & Elect Engn Ankara Turkey Middle E Tech Univ Dept Elect & Elect Engn TR-06531 Ankara Turkey

The reshaping technique that is based on transformation optics renders an object to be perceived as if it has a different shape irrespective of the location of the observer. This is achieved by coating the object with an anisotropic and spatially varying metamaterial layer by employing the concept of coordinate transformation. This paper presents a design approach for numerical approximation of reshaper medium by means of concentric layers coated over the object, each of which has simpler and easily realizable material parameters that are determined by the genetic optimization algorithm. The results of various finite element simulations are presented.

关键词： transformation media-based reshaping action material parameter approximation Coordinate transformations numerical approximation design approach Anisotropy optimization algorithms

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Polynomial metamodel based fast optimization of nano-CMOS oscillator circuits

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2014年第3期79卷 437-453页

作者： Mohanty, Saraju P. Kougianos, Elias Univ N Texas Denton TX 76203 USA

Modern consumer electronics are designed as analog/mixed-signal systems-on-chip (AMS-SoCs). In an AMS-SoC, the analog and mixed-signal portions have not received systematic attention due to their complex nature and the fact that their optimization and simulation consume significant portions of the design cycle time. This paper presents a new approach to reduce the design cycle time by combining accurate polynomial metamodels and optimization algorithms. The approach relies on a mathematical representation (metamodel or surrogate model) of AMSSoC subsystems/components. Polynomial metamodels are created from post-layout parasitic netlists and provide an accurate representation for each figure-of-merit over the entire design space of the AMS-SoC component. The metamodel approach saves a very significant amount of time during design iterations. Polynomial metamodels are reusable and language independent. Three algorithms are investigated to compare the speed for optimization on the polynomial metamodels. Two widely used circuits have been designed in two different technologies as comparative case studies: an 180 nm LC-VCO and a 45 nm ring oscillator (RO). Experimental results prove that the metamodel- based optimization achieved speed-up as high as 21,600 for the LC-VCO circuit and 11,750 for the RO in comparison to the actual circuit netlist-based (SPICE) optimization, with less than 1 % error. Thus, the paper demonstrates that the polynomial metamodeling approach to the design problem is an effective and accurate means for fast design space exploration and optimization.

关键词： Mixed-signal circuits Oscillator circuits Polynomial metamodeling Performance optimization optimization algorithms

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A coral-reef optimization algorithm for the optimal service distribution problem in mobile radio access networks

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2014年第11期25卷 1057-1069页

作者： Salcedo-Sanz, S. Sanchez-Garcia, J. E. Portilla-Figueras, J. A. Jimenez-Fernandez, S. Ahmadzadeh, A. M. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain WIK Consult GmbH Dept Cost Modeling & Internet Econ D-53604 Bad Honnef Germany

Mobile technology is currently one of the main pillars of worldwide economy. The constant evolution that mobile communications have undergone in the last decades, due to the appearance of new services and new technologies such as Universal Mobile Telecommunication Systems/High Speed Data Access and Long Term Evolution, has contributed to achieve this position in global economy. However, because of the crisis of the sector in the last 5years, mobile operator's revenues and investments have been reduced. Thus, mobile network operators tend to exploit the existing infrastructure at maximum possible, trying to use the existing network in the most efficient way. In this paper, a novel bio-inspired algorithm, the coral reef optimization algorithm (CRO) is introduced to minimise a network deployment investment cost problem. This is carried out by means of optimising the user demand of different services offered by mobile operators over the available technologies in the market, namely the optimal service distribution problem. The CRO is a recently proposed meta-heuristic based on the computer simulation of corals reproduction and reefs' formation. In this paper, this algorithm has been tested on several optimal service distribution problem scenarios in Spain, observing a significant reduction (up to 400 MEuro) on the total investment costs associated to the radio access network deployment. We compare the performance of the CRO approach with that of a classical (experience-based) services distribution, and with alternative meta-heuristics techniques, obtaining good results in all cases. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： optimization algorithms mobile communication Mobile radios Mobile Operators Investments Mobile Technologies Wireless networks Cathode ray oscilloscopes access network Long Term Evolution

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optimization of hydro energy storage plants by using differential evolution algorithm

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ENERGY 2014年 77卷 97-107页

作者： Glotic, Arnel Glotic, Adnan Kitak, Peter Pihler, Joze Ticar, Igor Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia Holding Slovenske Elekt Grp SI-1000 Ljubljana Slovenia

The optimal dispatching of cascade Hydro Power Plants is known as a complex optimization problem. In order to solve this problem the authors have applied an adapted differential evolution algorithm by using a fixed and dynamic population size. According to the dynamic population size, the proposed algorithm uses novel random and minimum to maximum sort strategy in order to create new populations with decreased or increased sizes. This implementation enables global search with fast convergence. It also uses a multi-core processor, where all the necessary optimization data are sent to the individual core of a central processing unit The main aim of the optimization process is to satisfy 24 h demand by minimizing the water quantity used per electrical energy produced. This optimization process also satisfies the desired reservoir levels at the end of the day. The models used in this paper were the real parameters' models of eight cascade Hydro Power Plants located in Slovenia (Europe). Also the standard model from the literature is used in order to compare the performance of the adapted optimization algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Cascade hydro power plants Energy storage Hydro generation scheduling optimization algorithms

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CUCKOO SEARCH ALGORITHM FOR optimization PROBLEMS-A LITERATURE REVIEW AND ITS APPLICATIONS

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APPLIED ARTIFICIAL INTELLIGENCE 2014年第5期28卷 419-448页

作者： Mohamad, Azizah Binti Zain, Azlan Mohd Bazin, Nor Erne Nazira Univ Teknol Malaysia Fac Comp Soft Comp Res Grp Utm Skudai 81310 Johor Malaysia

Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This article describes an overview of CS, which is inspired by the life of a bird family, as well as an overview of CS applications in various categories for solving optimization problems. optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. The categories reviewed are Engineering, Pattern Recognition, Job Scheduling, Networking, Object-Oriented Software (Software Testing), and Data Fusion in Wireless Sensor Networks. From the reviewed literature, CS is mostly applied in the engineering area for solving optimization problems. The objective of this study is to provide an overview and to summarize the review of applications of the CS.

关键词： Literature Review Cuculidae Data fusion algorithms Wireless sensor networks optimization algorithms

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