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Guest editorial nonlinear optimization of communication systems

IEEE Journal on Selected Areas in Communications 2006年第8期24卷 1421-1423页

作者： Chiang, Mung Low, Steven H. Luo, Zhi-Quan Shroff, Ness B. Yu, Wei Steenstrup, M. Princeton University Department of Electrical Engineering Program in Applied and Computational Mathematics Princeton NJ 08544 United States California Institute of Technology Department of Computer Science Pasadena CA 91125 United States University of Minnesota Department of Electrical and Computer Engineering Minneapolis MN 55455 United States Purdue University School of Electrical and Computer Engineering West Lafayette IN 47907 United States University of Toronto Edward S. Rogers Sr. Department of Electrical and Computer Engineering Toronto Ont. M4S 3G5 Canada

No abstract available

关键词： Communication systems Design optimization Communication system control optimization methods Protocols Distributed algorithms Power system modeling nonlinear control systems Lagrangian functions Surges

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An ant system-based routing algorithm for wireless sensor networks

An ant system-based routing algorithm for wireless sensor ne...

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3rd International conference on Cybernetics and Information Technologies, Systems and Applications/4th International conference on Computing, Communications and Control Technologies

作者： Aguilar, Jose Labrador, Miguel A. Univ Los Andes Merida 5201 Venezuela

ISBN: (纸本)9789806560833

In this paper, a general combinatorial Ant System-based distributed algorithm modeled like a dynamic optimization problem is presented. In the proposed algorithm, the solution space of the dynamic combinatorial optimization problem is mapped into the space where the ants will walk, and the transition probability and the pheromone update formula of the ant system are defined according to the objective function of the communication problem. We test and compare the performance of our routing algorithm against well-known routing schemes for wireless sensor networks and via simulations show that it consumes less energy per packet and extends the lifetime of the network.

关键词： dynamic combinatorial optimization distributed routing algorithms wireless sensor networks

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Applied Parallel Computing - State of the Art in Scientific Computing - 7th International conference, PARA 2004, Revised Selected Papers

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7th International conference on Applied Parallel Computing, PARA 2004

ISBN: (纸本)3540290672

The proceedings contain 128 papers. The topics discussed include: fast and reliable random number generators for scientific computing;large-scale computations with the unified Danish Eulerian model;a chemical engineering challenge problem that can benefit from interval methods;interval based Markov Decision processes;a verification method for solutions of linear programming problems;on the approximation of interval functions;the distributed interval geometric machine model;new algorithms for statistical analysis of interval data;on efficiency of tightening bonds in interval global optimization;applying software testing matrices to lapack;parallel algorithms for balanced truncation model reduction of sparse systems;and applying high performance computing techniques in astrophysics.

关键词： Parallel processing systems

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Genetic algorithms for Optimal Design of Vehicle Suspensions

Genetic Algorithms for Optimal Design of Vehicle Suspensions

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IEEE International conference on Engineering of Intelligent Systems, ICEIS

作者： Jingjun Zhang Yanhong Zhang Ruizhen Gao Hebei University of Engineering Handan China

The optimal design of vehicle suspensions is a complex design optimization problem which may have highly nonlinear design spaces with many local optima. At present, there are two main problems for the optimal design of vehicle suspensions. The first is that the global optimization solution is hardly reached using the numerical methods or the simple genetic algorithms. The second is that the suspension models are all lumped mass models in genetic algorithms. Virtual prototyping software can deal with complex multibody models and provide a good approximation to real system performance in different simulated scenarios, but the optimization method is a numerical method, thus the complex model gets local optimal solutions. So an ideal approach is to use virtual prototyping as the individuals performance evaluation means and to optimize the design based on GAs (genetic algorithms). The paper completes the interface between GAs and the ADAMS (Automatic Dynamic Analysis of Mechanical Systems) software, and presents an improved genetic algorithm for the optimal design of vehicle suspensions. In this method, a new scheme of genetic algorithms is used first, thus no crossover and mutation rates are needed. Father-offspring combined elitist scheme and generation replacement strategy are introduced to ensure a stable convergence of the algorithm. The optimization results prove that the improved genetic algorithm is better than the numerical optimization method, the simple genetic algorithm and the niching genetic algorithm in the aspects of gaining global optimization solutions and accelerating convergence

关键词： Genetic algorithms Algorithm design and analysis Suspensions optimization methods Design optimization Virtual prototyping Space vehicles software performance System performance Vehicle dynamics

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Comparing Particle Swarm Optimisation and Genetic algorithms for nonlinear Mapping

Comparing Particle Swarm Optimisation and Genetic Algorithms...

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Congress on Evolutionary Computation

作者： A. Edwards A.P. Engelbrecht Department of Computer Science University of Pretoria Gauteng South Africa

Reducing the dimensionality of high-dimensional data simplifies how data is presented, allowing easier visualisation of high-dimensional data and facilitating more efficient extraction of knowledge. nonlinear mapping methods transform data existing in high-dimensional space into a lower-dimensional space such that the topological characteristics of the high-dimensional data are preserved. Recent work proposed a particle swarm optimisation algorithm to perform nonlinear mapping. This paper compares a number of optimisation algorithms in performing nonlinear mapping. Experimental results distinguish between each of the optimisation algorithms. nonlinear mapping methods were designed to map small datasets and are unable to project new data points. A proposed method to perform nonlinear mapping on large datasets is discussed.

关键词： Particle swarm optimization Genetic algorithms Convergence Data visualization Computer science Africa performance analysis Data mining Design methodology optimization methods

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Efficient algorithms for Hardware/software Partitioning to Minimize Hardware Area

Efficient Algorithms for Hardware/Software Partitioning to M...

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IEEE Asia-Pacific conference on Circuits and Systems

作者： Wu Jigang Thambipillai Srikanthan Centre for High Performance Embedded SystemsSchool of Computing Engineering Nanyang Technological University Singapore

Area efficiency is one of the major considerations in constraint aware hardware/software partitioning process. This paper models hardware/software partitioning as an optimization problem with the objective of minimizing area utilization under the constraints of execution time and power consumption. An efficient heuristic algorithm running in O(n log n) is proposed by extending the method solving the 0-1 knapsack problem. Also, an exact algorithm based on dynamic programming is proposed to produce the optimal solution for small-sized problems. Computational results show that the proposed heuristic algorithm yields very good approximate solutions while dramatically reduces the execution time

关键词： Hardware Partitioning algorithms software algorithms Heuristic algorithms software performance Embedded software Time factors Energy consumption Dynamic programming Embedded system

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Computing Large Sparse Multivariate optimization Problems with an Application in Biophysics

Computing Large Sparse Multivariate Optimization Problems wi...

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Supercomputing conference

作者： Emre H. Brookes Rajendra V. Boppana Borries Demeler Department of Computer Science University of Texas San Antonio USA Science Center at San Antonio University of Texas Health Sciences Center USA

We present a novel divide and conquer method for parallelizing a large scale multivariate linear optimization problem, which is commonly solved using a sequential algorithm with the entire parameter space as the input. The optimization solves a large parameter estimation problem where the result is sparse in the parameters. By partitioning the parameters and the associated computations, our technique overcomes memory constraints when used in the context of a single workstation and achieves high processor utilization when large workstation clusters are used. We implemented this technique in a widely used software package for the analysis of a biophysics problem, which is representative for a large class of problems in the physical sciences. We evaluate the performance of the proposed method on a 512-processor cluster and offer an analytical model for predicting the performance of the algorithm

关键词： Biophysics Workstations Large-scale systems optimization methods Partitioning algorithms Parameter estimation Memory management software packages Analytical models Clustering algorithms

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DAKOTA, a multilevel parellel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 uers's manual.

DAKOTA, a multilevel parellel object-oriented framework for ...

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作者： Hart, William Eugene Dunlavy, Daniel M Kolda, Tamara Gibson Martinez-Canales, Monica L Hough, Patricia Diane Eddy, John P Williams, Pamela J Giunta, Anthony Andrew Swiler, Laura Painton Brown, Shannon L Watson, Jean-Paul Eldred, Michael Scott Adams, Brian M Gay, David M Griffin, Joshua D

The DAKOTA (Design Analysis Kit for optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

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Optimal process design for minimum springback based on RBF network and evolutionary strategy

Optimal process design for minimum springback based on RBF n...

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6th World Congress on Intelligent Control and Automation

作者： Liu, Wenjuan Liang, Zhiyong Huang, Zhenyuan Ruan, Feng South China Univ Technol Guangzhou 510640 Peoples R China Zhaoqing Univ Dept Comp Sci Zhaoqing 526061 Peoples R China

ISBN: (纸本)1424403316

Springback is a very important factor to influence the quality of sheet metal forming. Since it is a multi-variable and high nonlinear problem, there hasn't been an effective numerical or analytical approach proposed. In this paper, a method of generating optimal forming process for minimum springback is presented. The springback process was modeled and analyzed by using the finite-element method, so as to obtain the springback value for different process parameter as the sample signal of neural network. A Radial Basis Function Network (RBFN) was applied to simulate the complex springback process. An improved evolutionary strategy algorithm was used to optimize the identified model for minimum springback. It has been verified that the springback performance is better for sheet metal forming using the improved ES method than normal ES. The results indicate that optimal process parameters can be quickly and accurately accessed through the proposed approach.

关键词： springback optimization radial basis function network evolutionary strategy

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Particle Swarm optimization with Escape Velocity

Particle Swarm Optimization with Escape Velocity

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International conference on Computational Intelligence and Security

作者： Xiuli Wang Yongji Wang Haitao Zeng Hui Zhou Graduate University of Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy and Sciences Beijing China Inst. of Software Key Lab for Comput. Sci. Chin. Acad. of Sci. Beijing China. ywang@*** Inst. of Software Graduate Univ. Chin. Acad. of Sci. Beijing China. zenghaitao@*** Inst. of Software Graduate Univ. Chin. Acad. of Sci. Beijing China. zhouhui@***

ISBN: (纸本)1424406048

This paper presents a model of particle swarm optimization with escape velocity (EVPSO) in order to overcome premature convergence in the basic particle swarm optimization (PSO). The EVPSO model equips particles with the escape velocity to avoid them trapping into local minima and increase the diversity of population. A simulation study shows that the EVPSO outperforms the basic PSO, especially for high dimension function. The EVPSO model facilitates solving multi-modal optimization problems

关键词： Particle swarm optimization Convergence Equations Evolutionary computation optimization methods performance loss Clustering algorithms

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