检索结果-内蒙古大学图书馆

2010 IEEE World Congress on Computational Intelligence

作者： Pilat, Martin Neruda, Roman Charles Univ Prague Fac Math & Phys Dept Theoret Comp Sci & Math Log Prague 1 Czech Republic Acad Sci Czech Republ Inst Comp Sci Prague 8 Czech Republic

ISBN: (纸本)9781424481262

The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.

关键词： genetic algorithms heterogeneous island model multiobjective genetic algorithm multiobjective optimization problem parallel hybrid algorithm single-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Effects of diversity control in single-objective and multi-objective genetic algorithms

引用

JOURNAL OF HEURISTICS 2007年第1期13卷 1-34页

作者： Chaiyaratana, Nachol Piroonratana, Theera Sangkawelert, Nuntapon King Mongkuts Inst Technol N Bangkok Res & Dev Ctr Intelligent Syst Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Prod Engn Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Elect Engn Bangkok 10800 Thailand

This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R-1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.

关键词： benchmarking diversity control multi-objective genetic algorithm single-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Effect of splitter plate on passive control and drag reduction for fluid flow past an elliptic cylinder

引用

OCEAN ENGINEERING 2017年第Sep.1期141卷 351-374页

作者： Soumya, S. Prakash, K. Arul Indian Inst Technol Dept Appl Mech Fluid Mech Lab Madras 600036 Tamil Nadu India

In this study, the effect of splitter plate on fluid flow characteristics past an elliptic cylinder of different axis ratios (AR = 1.0 - 0.5) is numerically investigated for various Reynolds number (Re = 50-200). The equations governing fluid flow are discretized using Streamline Upwind/Petrov-Galerldn (SUPG) based finite element method (FEM). The presence of a splitter plate alters the wake region where vortex shedding is suppressed. Depending on AR and Re two critical lengths of splitter plate are defined, one for suppression of vortex shedding and the other when shear layer interaction in the wake is inhibited. The correlations for the critical lengths of splitter plate as a function of AR and Re are derived using regression analysis. It is observed that, the variation of integral parameters like drag, lift and St are not monotonic with increasing plate length. From the results it is concluded that for a particular combination of Re and AR with suitable splitter plate length, the total drag is minimized approximately from 2% to 38%. Finally, C-davg is selected as an objective function and modelled using Response Surface Approximation (RSA). Furthermore, a single-objective genetic algorithm has been implemented to obtain optimum configuration for minimum C-davg.

关键词： Elliptic cylinder Splitter plate SUPG - finite element method Vortex shedding suppression Response Surface Approximation single-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of NSGA-II algorithm to single-objective Transmission Constrained Generation Expansion Planning

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2009年第4期24卷 1790-1797页

作者： Murugan, P. Kannan, S. Baskar, S. Arulmigu Kalasalingam Coll Engn Elect & Commun Engn Dept Krishnankoil 626190 Tamil Nadu India Kalasalingam Univ Dept Elect Engn Krishnankoil 626190 Tamil Nadu India Thiagarajar Coll Engn Dept Elect Engn Madurai 625015 Tamil Nadu India

This paper presents an application of elitist nondominated sorting genetic algorithm version II (NSGA-II), a multi-objective algorithm to a constrained single objective optimization problem, the transmission constrained generation expansion planning (TC-GEP) problem. The TC-GEP problem is a large scale and challenging problem for the decision makers (to decide upon site, capacity, type of fuel, etc.) as there exist a large number of combinations. Normally the TC-GEP problem has an objective and a set of constraints. To use NSGA-II, the problem is treated as a two-objective problem. The first objective is the minimization of cost and the second objective is to minimize the sum of normalized soft constraints violation. The hard constraints (must satisfy constraints) are treated as constraints only. To improve the performance of the NSGA-II, two modifications are proposed. In problem formulation the modification is virtual mapping procedure (VMP), and in NSGA-II algorithm, controlled elitism is introduced. The NSGA-II is applied to solve TC-GEP problem for modified IEEE 30-bus test system for a planning horizon of six years. The results obtained by NSGA-II are compared and validated against single-objective genetic algorithm and dynamic programming. The effectiveness of each proposed approach has also been discussed in detail.

关键词： Combinatorial optimization dynamic programming multiobjective nondominated sorting genetic algorithm (NSGA-II) single-objective genetic algorithm success rate transmission constrained generation expansion planning virtual mapping procedure

来源：评论

学校读者我要写书评

暂无评论

Exploiting a Coevolutionary Approach to Concurrently Select Training Instances and Learn Rule Bases of Mamdani Fuzzy Systems

Exploiting a Coevolutionary Approach to Concurrently Select ...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Antonelli, Michela Ducange, Pietro Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Electtr Pisa Italy

ISBN: (纸本)9781424469208

When applied to high dimensional datasets, multi-objective evolutionary learning (MOEL) of fuzzy rule-based systems suffers from high computational costs, mainly due to the fitness evaluation. To use a reduced training set (TS) in place of the overall TS could considerably lessen the required effort. How this reduction should be performed, especially in the context of regression, is still an open issue. In this paper, we propose to adopt a co-evolutionary approach. In the execution of the MOEL, periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs. The SOGA aims to maximize a purposely-defined index which measures how much a reduced TS is representative of the overall TS in the context of the MOEL. We tested our approach on a real world high dimensional dataset. We show that the Pareto fronts generated by applying the MOEL with the overall and the reduced TSs are comparable, although the use of the reduced TS allows saving on average the 75% of the execution time.

关键词： MOEL Mamdani fuzzy systems Pareto fronts Pareto optimisation SOGA co-evolutionary approach fuzzy rule-based fuzzy systems genetic algorithms knowledge based systems learning (artificial intelligence) learning rule multiobjective evolutionary learning regression analysis regression analysis single-objective genetic algorithm training set

来源：评论

学校读者我要写书评

暂无评论

Modeling Analysis of Complex Deformation of Woven Coating Film during the Cyclic Tensile Process

引用

POLYMERS 2024年第12期16卷 1623-1623页

作者： Cai, Li Zhang, Zhengyan Cai, Deng'an Zhou, Guangming Wang, Xinwei Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Univ New South Wales Engn Fac Sydney 2052 Australia

It is difficult for the existing Burgers model to accurately depict the off-axis cyclic drawing process of woven coatings. In this paper, the mechanical deformation of woven PVC (polyvinyl chloride)-coated film at different temperatures is investigated. One-dimensional (1D) and two-dimensional (2D) constitutive models were established to characterize cyclic deformation processes. The 1D model is an improved Burgers model. The effects of the time dependence of the viscosity coefficient and the ratio of elastic to viscous deformation are considered simultaneously. The accuracy of the 1D model for predicting the cyclic nonlinear deformation at different temperatures and loading rates is improved. The 2D model is a nonlinear orthotropic model using polynomials. On the basis of the single-objective genetic algorithm, the inverse algorithm is used to obtain the shear polynomial coefficients in the tension phase and the shear modulus in the unloading phase, which circumvents performing the difficult shear test. UMAT subroutines of off-axis stretching and off-axis cyclic stretching are written separately. The intelligent inverse algorithm program consists of a single-objective genetic algorithm program, a finite element parametric modelling program, and a UMAT subroutine. The simulation results are compared with the off-axis cyclic tensile test data to validate the effectiveness and accuracy of the proposed 2D model for the analysis of the woven PVC-coated films in the tension-shear coupling state.

关键词： woven coated film cyclic stretching constitutive model intelligent inverse algorithm single-objective genetic algorithm numerical simulation

来源：评论

学校读者我要写书评

暂无评论

Shape optimization of airfoil in ground effect based on free-form deformation utilizing sensitivity analysis and surrogate model of artificial neural network

引用

OCEAN ENGINEERING 2022年 257卷

作者： Hu, Huan Zhang, Guiyong Li, Daochun Zhang, Zhifan Sun, Tiezhi Zong, Zhi Dalian Univ Technol Sch Naval Architecture Engn State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Deep Sea Explorat Collaborat Innovat Ctr Adv Ship Shanghai 200240 Peoples R China Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

Wings operate in proximity to surfaces for using ground effect to enhance lift-to-drag ratio, but the stability meets challenges. Changing airfoil shape could satisfy the requirement of stability and maximize lift-to-drag ratio, like performing single-objective optimization under a constraint. This research uses free-form deformation technique to adjust the airfoil curve by control points, and reduces the dimension of control variables by sensitivity analysis. Sampling airfoils and corresponding aerodynamics feeds an artificial neural network. Then, the neural network plays as a surrogate to predict fitness value for genetic algorithm execution. It is found that lift-to-drag ratio and static stability height are sensitive to vertical adjustments near the leading edge, one quarter chord point and trailing edge. The trained two-hidden-layer MLP generalizes well. The deformed optimum airfoil with S-shape camber line reduces lift-to-drag ratio to gain adequate static stability. The cause is that the aerodynamic center of pitch and that of altitude are moved upstream together, while the former's interval is longer than the latter's. The procedure provides reference for the optimization of airfoil under more states in ground effect zone, and the S-type deformation offers guidance for refinement on wing-in-ground stability.

关键词： Ground effect Free-form deformation Sensitivity analysis Artificial neural network Surrogate model single-objective genetic algorithm

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：