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International Joint conference on Neural Networks (IJCNN)

作者： Mu, Bin Zhang, Linlin Yuan, Shijin Li, Hongyu Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781479919598

Conditional non linear optimal perturbation (CNOP) is an extension of linear singular vector(LSV) to nonlinear optimization. Generally, CNOP is solved with such adjoint based algorithms as SPG2, SQP. Unfortunately, it is often difficult to obtain the corresponding adjoint models for some non linear models. In addition, for nonlinear models containing discontinuous "on-off" switches, the adjoint based methods can hardly find the correct gradient direction for solving the CNOP. These two factors restrict the application of CNOP. Intelligence algorithms are utilized to handle these problems and has made some improvements. Nevertheless, the intelligent method cannot be applied to solve CNOP of complex models due to its dimensional limitation. Therefore, the principal component analysis based genetic algorithm (PCAGA) is proposed to solve the CNOP of complex models. The PCAGA is composed of two key processes: dimension reduction and genetic optimization. To demonstrate the validity, PCAGA is applied to solve the CNOP of the Zebiak-Cane (ZC) model for studying ENSO predictability and compared with the adjoint based method. Experimental results show that PCAGA can achieve similar results to the adjoint based method in the high-dimensional space of a medium-complexity model without the adjoint models. The proposed method also can future be applied to solve variational data assimilation (VDA).

关键词： principal component analysis genetic algorithm conditional nonlinear optimal perturbation Zebiak-Cane model

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Design, optimization, and Benchmarking of Dense Linear Algebra algorithms on AMD GPUs

Design, Optimization, and Benchmarking of Dense Linear Algeb...

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IEEE high performance Extreme Computing conference (HPEC)

作者： Brown, Cade Abdelfattah, Ahmad Tomov, Stanimire Dongarra, Jack Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA

ISBN: (纸本)9781728192192

Dense linear algebra (DLA) has historically been in the vanguard of software that must be adapted first to hardware changes. This is because DLA is both critical to the accuracy and performance of so many different types of applications, and because they have proved to be outstanding vehicles for finding and implementing solutions to the problems that novel architectures pose. Therefore, in this paper we investigate the portability of the MAGMA DLA library to the latest AMD GPUs. We use auto tools to convert the CUDA code in MAGMA to the Heterogeneous-Computing Interface for Portability (HIP) language. MAGMA provides LAPACK for GPUs and benchmarks for fundamental DLA routines ranging from BLAS to dense factorizations, linear systems and eigen-problem solvers. We port these routines to HIP and quantify currently achievable performance through the MAGMA benchmarks for the main workload algorithms on MI25 and MI50 AMD GPUs. Comparison with performance roofline models and theoretical expectations are used to identify current limitations and directions for future improvements.

关键词： Numerical Linear Algebra HPC GPU Computing AMD GPUs HIP Runtime Portability

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Parallelization of the FICO Xpress-Optimizer

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optimization METHODS & software 2018年第3期33卷 518-529页

作者： Berthold, Timo Farmer, James Heinz, Stefan Perregaard, Michael FICO Fair Isaac Corp Berlin Germany FICO Birmingham W Midlands England

Computing hardware has mostly thrashed out the physical limits for speeding up individual computing cores. Consequently, the main line of progress for new hardware is growing the number of computing cores within a single CPU. This makes the study of efficient parallelization schemes for computation-intensive algorithms more and more important. A natural precondition to achieving reasonable speedups from parallelization is maintaining a high workload of the available computational resources. At the same time, reproducibility and reliability are key requirements for software that is used in industrial applications. In this paper, we present the new parallelization concept for the state-of-the-art MIP solver FICO Xpress-Optimizer. MIP solvers like Xpress are expected to be deterministic. This inevitably results in synchronization latencies which render the goal of a satisfying workload a challenge in itself. We address this challenge by following a partial information approach and separating the concepts of simultaneous tasks and independent threads from each other. Our computational results indicate that this leads to a much higher CPU workload and thereby to an improved, almost linear, scaling on modern high-performance CPUs. As an added value, the solution path that Xpress takes is not only deterministic in a fixed environment, but also, to a certain extent, thread-independent. This paper is an extended version of Berthold et al. [Parallelization of the FICO Xpress-Optimizer, in Mathematical software - ICMS 2016: 5th International conference, G.-M. Greuel, T. Koch, P. Paule, and A. Sommere, eds., Springer International Publishing, Berlin, 2016, pp. 251-258] containing more detailed technical descriptions, illustrative examples and updated computational results.

关键词： Mathematical optimization mixed integer programming parallelization

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SOC-NLNA: Synthesis and optimization for fully integrated narrow-band CMOS low noise amplifiers

SOC-NLNA: Synthesis and optimization for fully integrated na...

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43rd Design Automation conference

作者： Nieuwoudt, Arthur Ragheb, Tamer Massoud, Yehia Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

ISBN: (纸本)1595933816

In this paper we present SOC-NLNA, a systematic synthesis methodology for fully integrated narrow-band CMOS Low Noise Amplifiers (LNA) in high performance System-on-Chip (SoC) designs. SOC-NLNA is based on deterministic numerical nonlinear optimization and the Normal Boundary Intersection (NBI) method for Pareto optimization. To enable SoC integration, we simultaneously optimize both devices and passive components to yield integrated inductor values that are significantly less than those generated by traditional design techniques. When the synthesized LNAs are simulated using Cadence SpectreRF, SOC-NLNA yields up to 35 and 58 percent improvement in noise figure and gain. Leveraging the efficiency of our methodology, we are able to generate the Pareto surfaces between LNA performance metrics in seconds.

关键词： design algorithms Low Noise Amplifier LNA optimization Analog Synthesis

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Multilevel design optimization of hydraulic turbines based on hierarchical metamodel-assisted evolutionary algorithms 29

Multilevel design optimization of hydraulic turbines based o...

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29th IAHR Symposium on Hydraulic Machinery and Systems (IAHR)

作者： Kontoleontos, E. Zormpa, M. Nichtawitz, S. Mack-Sahl, D. Weissenberger, S. ANDRITZ HYDRO GmbH Linz Austria Natl Tech Univ Athens Athens Greece

In this paper, an efficient hydraulic optimization procedure is presented and applied to the design of hydraulic turbines. For computationally expensive industrial design optimization problems, an advanced optimization tool (EASY software) and a fast CFD evaluation tool are required. EASY optimization software is a Hierarchical Metamodel-Assisted Evolutionary Algorithm (HMAEA) that can be used in both single- (SOO) and multi-objective optimization (MOO) problems. In order to minimize the CFD solver calls during the optimization design, the MAEA rely on local metamodels, trained on the fly, that are used to identify the most promising members in each population and then only these are to be re-evaluated by the CPU costly CFD solver. For additional economy in the CPU cost, the hierarchical (two-level) optimization scheme is used in this paper, where at each level, a different evaluation tool, a low and a high fidelity specific software, can be linked. The low level utilizes a low-CPU cost and low-accuracy tool to explore the design space with a minimum impact to the wall clock time and the high level, using the high fidelity, high-CPU cost tool is used to exploit the information from the low level. For the applications presented in this paper, the high fidelity model is an incompressible Navier-Stokes equation solver and the low fidelity model is based on the solution of the incompressible Euler equations. In order to optimize the geometry of hydraulic machines, an in-house automatic geometry and mesh generation tool has been integrated in the optimization tool chain. In what follows, 2 three-objective design optimization problems of 3D Francis hydraulic turbines are presented. The optimization objective functions concern the 'quality' of the runner outlet velocity profile, the cavitation behavior and efficiency of the runner. The optimization results of the hydraulic turbine components along with the performance of the presented optimization procedure are shown in th

关键词： Evolutionary algorithms

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Enhancing software Effort Estimation with Ant Colony optimization Algorithm and Fuzzy-Neural Networks 3

Enhancing Software Effort Estimation with Ant Colony Optimiz...

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3rd International conference on Distributed Computing and high performance Computing, DCHPC 2024

作者： Afshari, Mahnaz Gandomani, Taghi Javdani Shahrekord University Data Science Research Group Shahrekord Iran Shahrekord University Dept. Computer Science Shahrekord Iran

ISBN: (纸本)9798350381580

software effort estimation remains a persistent challenge and requires serious attention in the early stages of software project management. Inherent uncertainties arising from incomplete and inaccurate requirements pose a significant barrier to reliable estimations. Despite numerous efforts and various techniques proposed for cost estimation, the pursuit of improving estimation accuracy remains essential. In response to this challenge, this study introduces a novel model that integrates an Adaptive Neuro-Fuzzy Inference System with the Ant Colony optimization algorithm. The model is further compared with well-known evolutionary algorithms such as Differential Evolution, Genetic Algorithm, Artificial Neural Network, and Particle Swarm optimization. Applying the proposed model to popular software effort estimation datasets, including Albrecht, Desharnais, and Kemerer, demonstrates its superiority over the mentioned algorithms. The improved estimation provided by this model can assist software project managers in better project cost estimation. © 2024 IEEE.

关键词： Fuzzy neural networks

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Learned Two-Step Iterative Shrinkage Thresholding Algorithm for Deep Compressive Sensing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3943-3956页

作者： Gan, Hongping Wang, Xiaoyang He, Lijun Liu, Jie Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China NPU Yangtze River Delta Res Inst Taicang 215400 Peoples R China

Deep unrolling architectures have revitalized compressive sensing (CS) by seamlessly blending deep neural networks with traditional optimization-based reconstruction algorithms. In pursuit of an efficient and deep interpretable approach, we propose LTwIST for CS problem, a novel deep unrolling framework that draws inspiration from the well-known two-step iterative shrinkage thresholding (TwIST) algorithm. LTwIST uses a trainable sensing matrix to adaptively learn structural information in images, and introduces a customized U-block architecture to solve the proximal mapping of nonlinear transformations connected with the sparsity-inducing regularizer. Specifically, each iteration recovery step of LTwIST corresponds to an iterative update step of the traditional TwIST algorithm. Moreover, the proposed method is designed to learn all the parameters end-to-end without manual tuning such as shrinkable thresholds, step sizes, etc. As a result, LTwIST obviates the need for manual parameter optimization, allows for high-quality image recovery and provides unambiguous interpretability. Moreover, our proposed LTwIST is also applicable to CS-based magnetic resonance imaging and exhibits a strong reconstruction performance. Extensive experiments on several public benchmark datasets demonstrate that the proposed LTwIST outperforms existing state-of-the-art deep CS methods by considerable margins in terms of quality evaluation metrics and visual performance. Our code is available on LTwIST.

关键词： Compressive sensing deep learning image reconstruction TwIST magnetic resonance imaging

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Artificial immune algorithm to function optimization problems

Artificial immune algorithm to function optimization problem...

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IEEE International conference on Communication software and Networks

作者： Zhang, Jing Basic Courses Department Beijing Union University Beijing China

ISBN: (纸本)9781612844855

optimization problems abound in the scientific research and engineering applications in various fields, the optimization method has important theoretical and practical value. The existence of the traditional shortcomings of optimization methods, in today's mass production application is limited. Multidisciplinary research to solve the optimization problem provides a new approach to biological intelligence or natural phenomena based on new intelligent optimization algorithms and applications in the study have shown excellent performance, the modern intelligent algorithms has become a new field of artificial intelligence research focus. Artificial immune optimization algorithm is an imitation of biological function of the immune system an intelligent way, providing a similar immune system noise tolerance, non-teacher learning, self-organization, memory and other evolutionary learning mechanism to solve the complex problems of the new distributed program, compared to other intelligent optimization algorithm has a high success rate optimization, individual diversity and good. © 2011 IEEE.

关键词： optimization

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optimization on steady-state cruise for a hypersonic vehicle 33

Optimization on steady-state cruise for a hypersonic vehicle

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33rd Chinese Control conference (CCC)

作者： Liu, Xing Wang, Yongji Liu, Lei Huang, Jian Huazhong Univ Sci & Technol Schlool Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781479947249

This paper concentrates on optimization of the steady-state cruise for a hypersonic vehicle. The steady-state cruise is a state with constant altitude and velocity during the flight. The ratio of fuel consumption to the range over a given period of time is defined as the fuel rate, which is the objective of this fuel optimized problem in this work. The optimal steady-state cruise is determined by minimizing the fuel rate with multi-constraints including boundary constraints of states and path constraints. Since this work needs to find the global solution satisfying the nonlinear constraints, we can apply particle swarm optimization(PSO) combined with the sequential quadratic programming(SQP) to find the optimal solution. Thus, this strategy takes the advantage of intelligent optimization algorithms with global search ability and SQP with high precision and handled constraint ability. A demonstration example results show good performance of fuel savings on the nonlinear model of the hypersonic vehicle.

关键词： Hypersonic vehicle Steady-state cruise Fuel optimization Particle swarm optimization Sequential quadratic programming

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Hardware Architecture for Particle Swarm optimization using Floating-point Arithmetic

Hardware Architecture for Particle Swarm Optimization using ...

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9th International conference on Intelligent Systems Design and Applications

作者： Munoz, Daniel M. Llanos, Carlos H. Coelho, Leandro dos S. Ayala-Rincon, Mauricio Univ Brasilia Dept Mech Engn Automat & Control Grp GRACO Brasilia DF Brazil Pontificia Univ Catol Parana Ind & Syst Engn Grad Program LAS PPGPES Curitiba Parana Brazil Univ Brasilia Dept Comp Sci & Math BR-70910900 Brasilia DF Brazil

ISBN: (纸本)9781424447350

high computational cost for solving large engineering optimization problems point out the design of parallel optimization algorithms. Population based optimization algorithms provide parallel capabilities that can be explored by their implementations done directly in hardware. This paper presents a hardware implementation of Particle Swarm optimization algorithms using an efficient floating-point arithmetic which performs the computations with high precision. All the architectures are parameterizable by bit-width, allowing the designer to choose the suitable format according to the requirements of the optimization problem. Synthesis and simulation results demonstrate that the proposed architecture achieves satisfactory results obtaining a better performance in therms of elapsed time than conventional software implementations.

关键词： PSO FPGA Floating-point arithmetic

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