检索结果-内蒙古大学图书馆

A Python/C plus plus library for bound-constrained global optimization using a biased random-key genetic algorithm

JOURNAL OF COMBINATORIAL OPTIMIZATION 2015年第3期30卷 710-728页

作者： Silva, R. M. A. Resende, M. G. C. Pardalos, P. M. Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (J Heuristics 17:487-525, 2011b) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (ORSA J Comput 6:154-160, 1994), differing in the way solutions are combined to produce offspring. After a brief introduction to the BRKGA, including a description of the local search procedure used in its decoder, we show how to download, install, configure, and use the library through an illustrative example.

关键词： Biased random-key genetic algorithm Global optimization Multimodal functions Continuous optimization Heuristic stochastic algorithm stochastic local search Nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

Multilayer dielectric filter design using a multiobjective evolutionary algorithm

引用

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2005年第11期53卷 3625-3632页

作者： Venkatarayalu, NV Ray, T Gan, YB Natl Univ Singapore Temasek Labs Singapore 119260 Singapore

Design of multilayer dielectric filters involve the identification of suitable dielectric material and appropriate thicknesses of the layers that best satisfies the desired frequency response for the application. Such problems, like any other practical design optimization problem require simultaneous consideration of multiple objectives and constraints. In this paper, we introduce a multiobjective evolutionary algorithm that is capable of handling unconstrained and constrained, single and multiobjective problems without any restriction on the number and nature of variables, constraints and objectives. The algorithm handles constraints and objectives separately using two fitness measures derived out of nondominance, unlike most of its counterparts which use a single fitness measure. Unlike most evolutionary algorithms where only the good parents participate in mating, our algorithm ensures that all solutions participate in mating, which is useful for exploring highly nonlinear search spaces. The diversity of the solutions is controlled by the partner selection scheme that prefers elites with distant neighbors as mating partners. The results of two multiobjective test problems, three multilayer dielectric filter designs (low-pass, bandpass and stopband) and one variable layer low-pass filter design are presented in this paper to highlight the benefits offered by our algorithm in terms of modeling flexibility, computational efficiency and its ability to arrive at competitive nondominated designs. A comparison of our results with those obtained using a single objective aggregated formulation for the stopband filter design is also presented. We have also compared the performance of our algorithm with nondominated sorting genetic algorithm (NSGA-II) for the low-pass filter design where our results are better.

关键词： multiobjective optimization optimization of dielectric filters stochastic algorithm

来源：评论

学校读者我要写书评

暂无评论

The vanishing learning rate asymptotic for linear L²-boosting

引用

ESAIM-PROBABILITY AND STATISTICS 2024年 28卷 227-257页

作者： Dombry, Clement Esstafa, Youssef Univ Franche Comte CNRS LmB F-25000 Besancon France Le Mans Univ Lab Manceau Math Ave Olivier Messiaen F-72085 Le Mans 09 France

We investigate the asymptotic behaviour of gradient boosting algorithms when the learning rate converges to zero and the number of iterations is rescaled accordingly. We mostly consider L-2-boosting for regression with linear base learner as studied in P. B & uuml;hlmann and B. Yu, J. Am. Statist. Assoc. 98 (2003) 324-339 and analyze also a stochastic version of the model where subsampling is used at each step (J.H. Friedman, Computat. Statist. Data Anal. 38 (2002) 367-378). We prove a deterministic limit in the vanishing learning rate asymptotic and characterize the limit as the unique solution of a linear differential equation in an infinite dimensional function space. Besides, the training and test error of the limiting procedure are thoroughly analyzed. We finally illustrate and discuss our result on a simple numerical experiment where the linear L-2-boosting operator is interpreted as a smoothed projection and time is related to its number of degrees of freedom.

关键词： Boosting non parametric regression statistical learning stochastic algorithm Markov chain convergence of stochastic process

来源：评论

学校读者我要写书评

暂无评论

The impact of uncertain centrifuge capillary pressure on reservoir simulation

引用

SIAM JOURNAL ON SCIENTIFIC COMPUTING 2004年第2期26卷 537-557页

作者： Subbey, S Christie, M Sambridge, M Heriot Watt Univ Inst Petr Engn Edinburgh EH14 4AS Midlothian Scotland Australian Natl Univ Res Sch Earth Sci Inst Adv Studies Canberra ACT 0200 Australia

The capillary pressure in a reservoir determines the saturation distribution, and hence the total in situ volumes of fluids (oil/water/gas). The accurate knowledge of the capillary pressure distribution is one of the primary factors that may be decisive in the reliable estimation of hydrocarbon reserves. The centrifuge procedure provides laboratory data, which can be inverted to derive capillary pressure curves in laboratory conditions. The derived laboratory capillary pressure curves are then scaled up for full-field simulation of petroleum reservoirs. The inversion procedure is uncertain and gives nonunique capillary pressure curves. The standard industrial practice, however, has been to derive a single capillary pressure curve, and ignoring the uncertainty. Since the capillary pressure is uncertain, estimates of important reservoir parameters dependent on capillary pressure distribution are uncertain. This paper shows how the uncertainty in centrifuge capillary pressure can be quantified. It also shows how this uncertainty propagates through the scale-up process and impacts on the estimate of the oil recovery potential. We illustrate using a simple, two-phase (oil/water) synthetic reservoir model.

关键词： uncertainty Volterra linear integral equation inverse ill-posed stochastic algorithm Voronoi cells measured data synthetic data capillary pressure centrifuge

来源：评论

学校读者我要写书评

暂无评论

Solving systems of nonlinear equations with continuous GRASP

引用

NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS 2009年第4期10卷 2000-2006页

作者： Hirsch, Michael J. Pardalos, Panos M. Resende, Mauricio G. C. Raytheon Inc Network Centr Syst St Petersburg FL 33733 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

A method for finding all roots of a system of nonlinear equations is described. Our method makes use of C-GRASP, a recently proposed continuous global optimization heuristic. Given a nonlinear system, we solve a corresponding adaptively modified global optimization problem multiple times, each time using C-GRASP, with areas of repulsion around roots that have already been found. The heuristic makes no use of derivative information. We illustrate the approach using systems found in the literature. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Nonlinear systems of equations Global optimization Continuous optimization Heuristic stochastic algorithm stochastic local search Nonlinear programming GRASP C-GRASP

来源：评论

学校读者我要写书评

暂无评论

Application of inverse analysis to determine the geometric configuration of filament heaters for uniform heating

引用

INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2016年 105卷 1-12页

作者： Lemos, Larissa D. Brittes, Rogerio Franca, Francis H. R. Univ Fed Rio Grande do Sul Dept Mech Engn Rua Sarmento Leite 425 BR-90050170 Porto Alegre RS Brazil Univ Fed Santa Maria Av Presidente Vargas 1958 BR-96506302 Cachoeira Do Sul RS Brazil

In typical designs of ovens, it is often desired to determine the position and geometrical configurations of heaters to attain uniform heating of the materials subjected to thermal processes. In this study, uniform temperature and heat flux are prescribed in one of the surfaces of a radiative system, while the shape and position of filament heaters are left to be found. This is equivalent to the inverse problem of the conventional trial-and-error approach. The oven is modeled as a three-dimensional enclosure formed with diffuse-gray walls, and filled with transparent medium. The inverse problem is solved as an optimization problem. The solution is obtained by the generalized extreme optimization (GEO) algorithm, a stochastic global optimization method. The proposed methodology can lead to filament heaters capable of satisfying the two conditions on the design surface with maximum deviation of about 2% and average deviation less than 0.5%. (C) 2016 Elsevier Masson SAS. All rights reserved.

关键词： Uniform heating Filament heater Inverse design stochastic algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-scale quantum harmonic oscillator algorithm for global numerical optimization

引用

APPLIED SOFT COMPUTING 2018年 69卷 655-670页

作者： Wang, Peng Ye, Xinggui Li, Bo Cheng, Kun Southwest Univ Nationalities Sch Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China China Unicom Fujian Branch Cloud Comp & Big Data Ctr Fuzhou 350007 Fujian Peoples R China Tangshan Univ Sch Comp Sci & Technol Tangshan 063000 Peoples R China

This paper aims to provide a novel metaheuristic algorithm motivated from quantum motion entitled Multi-scale Quantum Harmonic Oscillator algorithm (MQHOA). The calculation accuracy of MQHOA is adjustable. The physical model and mathematical analysis of the proposed algorithm are detailed and well interpreted in this paper. The structure of MQHOA is very simple, including merely two phases, the quantum harmonic oscillator process (QHO process) and multi-scale process (M process). Experiments are carried out to validate the effectiveness and efficiency of MQHOA by applying it to 30 well defined benchmark functions. We also compare MQHOA with several well-known metaheuristic algorithms, such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO). The comparative results indicate the competitive and superior performance of the proposed algorithm in both convergence speed and optimal solution accuracy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Quantum harmonic oscillator algorithm Quantum algorithm Metaheuristic algorithm stochastic algorithm Global optimization

来源：评论

学校读者我要写书评

暂无评论

Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming

引用

NEURAL NETWORKS 2008年第2-3期21卷 358-367页

作者： Chen, S. Wolfgang, A. Harris, C. J. Hanzo, L. Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output's probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： radial basis function network symmetry probability density function recursive learning stochastic algorithm multiple-antenna system beamforming

来源：评论

学校读者我要写书评

暂无评论

MCMC methods for restoration of nonlinearly distorted autoregressive signals

引用

SIGNAL PROCESSING 2001年第1期81卷 83-97页

作者： Troughton, PT Godsill, SJ Univ Cambridge Dept Engn Signal Proc Grp Cambridge CB2 1PZ England

We approach the problem of restoring distorted autoregressive (AR) signals by using a cascade model, in which the observed signal is modelled as the output of a nonlinear AR (NAR) process excited by the linear AR signal we are attempting to recover. The Volterra expansion of the NAR model has a very large number of possible terms even when truncated at fairly small maximum polynomial degrees and lags. We address the problem of subset selection and uncertainty in the nonlinear stage and model order uncertainty in the linear stage through a hierarchical Bayesian approach. A Markov chain Monte Carlo (MCMC) approach is used for implementation, with reversible-jump moves for the linear model order and a rapidly mixing Gibbs sampler for subset selection in the nonlinear model stage, exploiting the partially analytic properties of the model. We demonstrate the method using both synthetic AR data and an audio extract, and extend the approach to process a long distorted audio time series, for which the source model cannot be considered to be time invariant. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： stochastic algorithm reversible-jump Markov chain Monte Carlo (MCMC) Gibbs sampler Bayesian model selection subset selection Volterra expansion polynomial autoregressive model cascade modelling

来源：评论

学校读者我要写书评

暂无评论

A MODEL OF INVARIANT CONTROL SYSTEM USING MEAN CURVATURE DRIFT FROM BROWNIAN MOTION UNDER SUBMERSIONS

引用

QUARTERLY OF APPLIED MATHEMATICS 2023年第1期81卷 175-202页

作者： Ching-peng, H. U. A. N. G. Brown Univ Div Appl Math 170 Hope St Providence RI 02912 USA

Given a Riemannian submersion phi : M-+ N, we construct a stochastic process X on M such that the image Y := phi(X) is a (reversed, scaled) mean curvature flow of the fibers of the submersion. The model example is the mapping pi : GL(n)-+ GL(n)/O(n), whose image is equivalent to the space of n-by -n positive definite matrices, S+(n, n), and the said flow has deterministic image. We are able to compute explicitly the mean curvature (and hence the drift term) of the fibers w.r.t. this map, (i) under diagonalization and (ii) in matrix entries, writing mean curvature as the gradient of log volume of orbits. As a consequence, we are able to write down Brownian motions explicitly on several common homogeneous spaces, such as Poincare acute accent 's upper half plane and the Bures-Wasserstein geometry on S+(n, n), on which we can see the eigenvalue processes of Brownian motion reminiscent of Dyson's Brownian *** choosing the background metric via natural GL(n) action, we arrive at an invariant control system on the GL(n)-homogenous space GL(n)/O(n). We investigate the feasibility of developing stochastic algorithms using the mean curvature flow.

关键词： Mean curvature flow gradient flow Brownian motion Riemannian submersion random matrix eigenvalue processes geometry of positive definite matrices stochastic algorithm control theory on homogeneous space

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：