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Linear Ensemble Antennas Resulting from the optimization of Log Periodic Dipole Arrays Using Genetic algorithms

Linear Ensemble Antennas Resulting from the Optimization of ...

引用

Congress on Evolutionary Computation

作者： T.L. Pitzer J.A. Fellows G.B. Lamont A.J. Terzuoli Department of Electrical Engineering Air Force Institute of Technology (AFIT) Dayton OH USA

optimization with genetic algorithms (GAs) has become both popular and realistic in the electromagnetic community with the growth in computers and precise electromagnetic computer programs. Intuition is often required for antenna design but GAs can instead define and search a large design space. Using this method results in a non-intuitive and yet very effective antenna architectures. This paper presents an integrated technique to optimize antennas whose basis is Log Periodic Dipole Arrays (LPDA). We use an aggregated fitness function in a multi-objective GA, the NSGA-II, and the optimization software package iSIGHT, along with the Graphical Numerical Electromagnetics Code (GNEC), a method of moments program for antenna design. The results of this design technique compare excellently to Yagi-Uda designs as well as produce acceptable antenna designs based on LPDAs.

关键词： Dipole antennas Log periodic antennas Genetic algorithms Design optimization high performance computing Algorithm design and analysis software packages Electromagnetics Brain modeling Computer architecture

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Code Design as an optimization Problem: from Mixed Integer Programming to an Improved high performance Randomized GRASP like Algorithm and from This One to an Improved Genetic Algorithm

Code Design as an Optimization Problem: from Mixed Integer P...

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International conference on Computational Intelligence for Modelling, Control and Automation, and International conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Jose Barahona Da Fonseca Department of Electrical Engineering and Computer Science Faculty of Sciences and Technology New University of Lisbon Portugal

We begin to show that the design of optimum codes is a very difficult task by a set of preliminary brute force experiments where we generate all the possible optimum codes of a given length and minimum Hamming distance and then estimate the probability of finding one of these codes filling randomly the matrix that defines the code. Then we develop a novel approach to the code design problem based on the well known optimization technique of Mixed Integer Programming. Unfortunately the GAMS optimization software package limitation of 10 indexes imposes a limit of a maximum length 5 in the code to be designed. We show some results confirmed by the literature with this MIP model. Finally we develop a high performance randomized algorithm that surprisingly has much better runtimes than the MIP model.

关键词： Algorithm design and analysis Design optimization Linear programming Genetic algorithms Hamming distance Filling Error correction codes Binary codes Computational intelligence software packages

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A Genetic Binary Particle Swarm optimization Model

A Genetic Binary Particle Swarm Optimization Model

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Congress on Evolutionary Computation

作者： J. Sadri C.Y. Suen CENPARMI (Center for Pattern Recognition and Machine Intelligence) Computer Science and Software Enginneering Concordia University Montreal West QUE Canada Computer Science and Software Engineering Department Concordia University Montreal QUE Canada

In this paper, a genetic binary particle swarm optimization (GBPSO) model is proposed, and its performance is compared with the regular binary particle swarm optimizer (PSO), introduced by Kennedy and Eberhart. In the original model, the size of the swarm was fixed. In our model, we introduce birth and death operations in order to make the population very dynamic. Since birth and mortality rates change naturally with time, our model allows oscillations in the size of the population. Compared to the original PSO model, and genetic algorithms, our strategy proposes a more natural simulation of the social behavior of intelligent animals. The experimental results show that compared to original PSO, our GBPSO model can reach broader domains in the search space and converge faster in very high dimensional and complex environments.

关键词： Particle swarm optimization Multidimensional systems Computer science software engineering Java Genetic algorithms Animals Adaptive algorithm Birds Educational institutions

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high performance Evolutionary Computing

High Performance Evolutionary Computing

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Users Group conference

作者： Edwin Nunez Edwin Roger Banks Paul Agarwal Marshall McBride Ron Liedel USASMDC Advanced Research Center COLSA Corporation Huntsville AL USA U.S. Army Space and Missile Defence Command AL USA

Evolutionary computing (EC) comprises a family of global optimization techniques that start with a random population of potential solutions and then evolve more fit solutions over many generations. To accomplish this increase in fitness, EC uses basic operations like selection, recombination, and mutation. Because of its compute- intensive nature, EC research is an obvious candidate for hosting on HPC clusters or systems. EC requires high performance computers (HPC) because the selection process needs to evaluate the fitness of each member of a population of solutions, so the more fit individuals may propagate their characteristics to the next generation of solutions. This requirement becomes even more acute because the evaluation process must be iterated over a very large number of generations. In this paper, we provide a general overview of EC, its applicability to a broad range of problems. In particular, we focus on some subclasses of EC known as genetic programming (GP), genetic algorithms (GA), hybrids, and other EC forms. This paper also discusses the architectural issues of hosting EC on a HPC cluster, and the related issue of population management. Two possible EC architectures are presented: (1) a single chromosome evaluator that treats a pool of cluster nodes as evaluators for an individual solution, and (2) a parallel evolver that manages a sub-population of solutions at each node. Advantages and disadvantages of each approach will be discussed. EC may be applied to a wide variety of problems. Applications of EC include schedule optimization, robotic navigation, image enhancement/processing, discrimination of buried unexploded ordnance, discovery of innovative electronic filter and controller designs, lens design optimization, radar response modeling, and many more. EC excels at solving high-dimensional and nonlinear problems. HPC resources have enabled the broader application of EC optimization techniques. However, at present, EC is underutilized in the

关键词： high performance computing Genetic programming Design optimization Genetic mutations Genetic algorithms Biological cells Optimal scheduling Robots Navigation Image enhancement

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A Flexible Grid Framework for Automatic Protein-Ligand Docking

A Flexible Grid Framework for Automatic Protein-Ligand Docki...

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IEEE International conference on e-Science and Grid Computing

作者： David Abramson Celine Amoreira Kim K. Baldridge Laura Berstis Chris Kondrick Tom Peachey CSIT Monash University Clayton VIC Australia Faculty of Chemistry University of Zurich Zurich Switzerland San Diego Supercomputer Center La Jolla CA USA

ISBN: (纸本)9781424430116

Many important and fundamental questions in biology and biochemistry can be better understood through investigations performed at the protein-ligand or drugreceptor level. A variety of techniques have been used over the years, and it is an area of active research. In this paper we illustrate an approach that leverages a number of different computational chemistry approaches, and combines these with non-linear optimization algorithms and grid based high performance computing platforms. The result is a very flexible, high performance method of evaluating protein-ligand interaction algorithms. We illustrate the approach by evaluating a hybrid molecular modeling and quantum theoretical based algorithm.

关键词： Proteins high performance computing Chemistry Grid computing Distributed computing Biochemistry Biology computing Quantum mechanics software tools Concurrent computing

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Optimal Estimation of Perspective Camera Pose

Optimal Estimation of Perspective Camera Pose

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International conference on Pattern Recognition

作者： C. Olsson F. Kahl M. Oskarsson Centre for Mathematical Sciences Lund University Sweden

In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of camera pose estimation for calibrated cameras. While traditional methods may get trapped in local minima, due to the non-convexity of the problem, we have developed an approach that guarantees global optimality. The scheme is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data

关键词： Cameras Iterative algorithms Computer vision Application software Numerical stability Councils nonlinear equations Gaussian noise Noise measurement Maximum likelihood estimation

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Importance of Architecture Knowledge and Algorithm Redesign for Efficient DSP Programming

Importance of Architecture Knowledge and Algorithm Redesign ...

引用

IEEE International conference on Electro-Information Technology

作者： Archana Kalyansundar Rita Chattopadhyay Corporate Technology Siemens AG Communications Bangalore India

In the recent past, there has been an increase in the demand for vision-based embedded systems. These applications have high computational requirements and therefore severely strain the limited resources available in most embedded systems. With today's high-performance digital signal processors (DSPs) offering additional features to address multi-media needs, it is necessary to understand and exploit these features to realize a real-time vision-based application. Algorithm level optimizations also contribute to reduction in processing power and memory requirements. Hence, software redesign is an important process for optimizing the software. In this paper, we emphasize the need for an in-depth knowledge of the architecture for optimal, cost-effective real-time performance. We also explain the impact of various optimization techniques on the execution time and memory consumption of a simple block-based algorithm and a complex real-world stationary vehicle detection algorithm

关键词： Digital signal processing Signal processing algorithms Computer architecture Embedded system Computer applications Embedded computing Capacitive sensors Digital signal processors Application software Vehicle detection

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Handling Time-Varying TSP Instances

Handling Time-Varying TSP Instances

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Congress on Evolutionary Computation

作者： F.O. de Franca L.C.T. Gomes L.N. de Castro F.J. von Zuben Laboratory of Bioinformatics and Bio-inspired Computing (LBiC) School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil Catholic University of Santos Unisantos Sao Paulo Brazil

Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.

关键词： Ant colony optimization Proposals Immune system Algorithm design and analysis Design optimization Cost function Cities and towns Application software Routing Computer networks

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moPGA: Towards a New Generation of Multi-objective Genetic algorithms

moPGA: Towards a New Generation of Multi-objective Genetic A...

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Congress on Evolutionary Computation

作者： H. Soh M. Kirley Agency for Science Technology and Research (ASTAR) Institute of High Performance Computing Singapore Department of Computer Science and Software Engineering University of Melbourne VIC Australia

This paper describes a multi-objective parameter-less genetic algorithm (moPGA), which combines several recent developments including efficient non-dominated sorting, linkage learning, isin-Dominance, building-block mutation and convergence detection. Additionally, a novel method of clustering in the objective space using an isin-Pareto Set is introduced. Comparisons with well-known multi-objective GAs on scalable benchmark problems indicate that the algorithm scales well with problem size in terms of number of function evaluations and quality of solutions found. moPGA was built for easy usage and hence, in addition to the problem function and encoding, there are only two required user defined parameters; (1) the maximum running time or generations and (2) the precision of the desired solutions (isin).

关键词： Genetic algorithms Clustering algorithms Genetic mutations Bayesian methods Constraint optimization Sorting Evolutionary computation Computer science software engineering Testing

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Coordination optimization-based Variable Structure Control for Main Steam Pressure of Power Plant

Coordination Optimization-based Variable Structure Control f...

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International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Yang Yong Luo An College of Automotive and Mech. Engineering Changsha University of Science and Technology Changsha China College of Information Science and Engineering Central South University Changsha China College of Electric Engineering and Information Hunan University Changsha China

An optimal variable structure control (VSC) based on a coordination genetic algorithm has been developed. Steady-state error and control switching frequency are used to constitute the system performance indexes in the coordination optimization, while the tuning rate of boundary layer width (BLW) is employed as the optimization parameter. Then based on the mathematical relationship between BLW tuning law and steady state error, an optimized BLW tuning rate is added to the nonlinear control term of VSC. Simulation experiment results applied to the main steam pressure control (MSPC) of power plant show the comprehensive superiority of dynamical and static state performance by using the proposed controller over that of by using an optimized PID control. The proposed VSC system has better robustness against large parameter variations and disturbances. This succeeds in coordinately considering both chattering reduction and high-precision control in VSC

关键词： Pressure control Power generation Tuning Steady-state Error correction Optimal control Genetic algorithms Control systems Switching frequency System performance

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