检索结果-内蒙古大学图书馆

Surrogate-assisted hierarchical particle swarm optimization

INFORMATION SCIENCES 2018年 454卷 59-72页

作者： Yu, Haibo Tan, Ying Zeng, Jianchao Sun, Chaoli Jin, Yaochu Taiyuan Univ Sci & Technol Dept Mech Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China North Univ China Dept Comp Sci & Control Engn Taiyuan 030051 Shanxi Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Meta-heuristic algorithms, which require a large number of fitness evaluations before locating the global optimum, are often prevented from being applied to computationally expensive real-world problems where one fitness evaluation may take from minutes to hours, or even days. Although many surrogate-assisted meta-heuristic optimization algorithms have been proposed, most of them were developed for solving expensive problems up to 30 dimensions. In this paper, we propose a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle swarm optimization algorithm (SL-PSO), where the PSO and SL-PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model. Our experimental results on seven benchmark functions of dimensions 30, 50 and 100 demonstrate that the proposed method is competitive compared with the state-of-the-art algorithms under a limited computational budget. (C) 2018 Elsevier Inc. All rights reserved.

关键词： computationally expensive problems Surrogate model Radial basis function Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Classification-Assisted Differential Evolution for computationally expensive problems

Classification-Assisted Differential Evolution for Computati...

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Lu, Xiaofen Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781424478354

Like most Evolutionary Algorithms (EAs), Differential Evolution (DE) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This is an obstacle for applying DE to computationally expensive problems. Many previous studies have been carried out to develop surrogate-assisted approaches for EAs to reduce the number of real fitness evaluations. Existing methods typically build surrogates with either regression or ranking methods. However, due to the pairwise selection scheme of DE, it is more appropriate to formulate the construction of surrogate as a classification problem rather than a regression or ranking problem. Hence, we propose a classification-assisted DE in this paper. Experimental studies showed that the classification-assisted DE has great potential when compared to the DE that uses regression or ranking techniques to build surrogates.

关键词： Surrogate Models Differential Evolution computationally expensive problems Classification

来源：评论

学校读者我要写书评

暂无评论

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional expensive problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2017年第4期21卷 644-660页

作者： Sun, Chaoli Jin, Yaochu Cheng, Ran Ding, Jinliang Zeng, Jianchao Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England North Univ China Sch Comp Sci & Control Engn Taiyuan 030051 Shanxi Peoples R China

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

关键词： computationally expensive problems fitness estimation strategy (FES) particle swarm optimization (PSO) radial-basis-function networks surrogate models

来源：评论

学校读者我要写书评

暂无评论

Dimensionality-reduction Frameworks for computationally expensive problems

Dimensionality-reduction Frameworks for Computationally Expe...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Tenne, Yoel Izui, Kazuhiro Nishiwaki, Shinji Kyoto Univ Fac Engn Dept Mech Engn & Sci Kyoto 6068501 Japan

ISBN: (纸本)9781424481262

Real-world design optimization problems are typically computationally-expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks for evolutionary model-assisted optimization: one uses variable-selection to identify an important subset of the original variables while the other uses topological mapping to project the high-dimensional data to a lower-dimension. Performance analysis with both mathematical test functions and a problem of airfoil shape optimization evaluates the efficacy of the frameworks.

关键词： aerospace components airfoil shape optimization complementary dimensionality-reduction computationally expensive problems design engineering dimensionality-reduction frameworks evolutionary computation evolutionary model-assisted optimization mathematical test functions model-assisted evolutionary frameworks optimisation optimization search performance analysis real-world design optimization problems topological mapping

来源：评论

学校读者我要写书评

暂无评论

Fitness Estimation Strategy Assisted Competitive Swarm Optimizer for High Dimensional expensive problems

Fitness Estimation Strategy Assisted Competitive Swarm Optim...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Sun, Chaoli Jin, Yaochu Ding, Jinliang Zeng, Jianchao Taiyuan Univ Sci & Technol Taiyuan 030024 Shanxi Peoples R China Univ Surrey Guildford GU2 7XH Surrey England Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China North Univ China Sch Comp Sci & Control Engn Taiyuan 030051 Shanxi Peoples R China

No abstract available.

ISBN: (纸本)9781450343237

No abstract available.

关键词： particle swarm optimization fitness approximation computationally expensive problems fitness estimation strategy

来源：评论

学校读者我要写书评

暂无评论

A new self-adaptation scheme for differential evolution

引用

NEUROCOMPUTING 2014年 146卷 2-16页

作者： Lu, Xiaofen Tang, Ke Sendhoff, Bernhard Yao, Xin Univ Sci & Technol China USTC Birmingham Joint Res Inst Intelligent Comput Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Honda Res Inst Europe GmbH D-63073 Offenbach Germany Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England

The performance of Differential Evolution (DE) largely depends on the choice of trial vector generation strategy and the values of its control parameters. In the past years, quite a few DE variants have been developed to adaptively adjust the strategy and control parameters during the search process. However, these variants may not perform satisfactorily when coping with computationally expensive problems (CEPs) for which a satisfying solution needs to be obtained with very limited fitness evaluations (FEs). In this paper, we demonstrate that not only can surrogate models be used to approximate the fitness function, they can also provide a good alternative method to adapt the strategy and control parameters of DE, and thus propose a framework called DE with Surrogate-assisted Self-Adaptation (DESSA). DESSA generates multiple trial vectors using different trial vector generation strategies and parameter settings, and then employs a surrogate model to identify the potentially best trial vector to undergo real fitness evaluation. As each trial vector corresponds to a unique combination of strategy and parameter setting, the surrogate model acts like a strategy/parameter setting selector that aims to identify the most suitable strategy and parameter setting for each target vector. Since DESSA can be easily combined with different DE variants, three concrete DE variants, namely DESSA-CoDE, DESSA-SaDE, and DESSA-CoDE*, are proposed. Comprehensive empirical studies demonstrate that DESSA can lead to superior performance over the compared adaptive DE variants. More importantly, it is shown that DESSA has the potential of accommodating more search strategies, which may lead to novel DE variants with even more competitive performance. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Differential evolution Self-adaptation computationally expensive problems Surrogate model

来源：评论

学校读者我要写书评

暂无评论

Evolution by Adapting Surrogates

引用

EVOLUTIONARY COMPUTATION 2013年第2期21卷 313-340页

作者： Le, Minh Nghia Ong, Yew Soon Menzel, Stefan Jin, Yaochu Sendhoff, Bernhard Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Honda Res Inst Europe GmbH D-63073 Offenbach Germany Univ Surrey Guildford Dept Comp Surrey GU27XH England

To deal with complex optimization problems plagued with computationally expensive fitness functions, the use of surrogates to replace the original functions within the evolutionary framework is becoming a common practice. However, the appropriate datacentric approximation methodology to use for the construction of surrogate model would depend largely on the nature of the problem of interest, which varies from fitness landscape and state of the evolutionary search, to the characteristics of search algorithm used. This has given rise to the plethora of surrogate-assisted evolutionary frameworks proposed in the literature with ad hoc approximation/surrogate modeling methodologies considered. Since prior knowledge on the suitability of the data centric approximation methodology to use in surrogate-assisted evolutionary optimization is typically unavailable beforehand, this paper presents a novel evolutionary framework with the evolvability learning of surrogates (EvoLS) operating on multiple diverse approximation methodologies in the search. Further, in contrast to the common use of fitness prediction error as a criterion for the selection of surrogates, the concept of evolvability to indicate the productivity or suitability of an approximation methodology that brings about fitness improvement in the evolutionary search is introduced as the basis for adaptation. The backbone of the proposed EvoLS is a statistical learning scheme to determine the evolvability of each approximation methodology while the search progresses online. For each individual solution, the most productive approximation methodology is inferred, that is, the method with highest evolvability measure. Fitness improving surrogates are subsequently constructed for use within a trust-region en-abled local search strategy, leading to the self-configuration of a surrogate-assisted memetic algorithm for solving computationally expensive problems. A numerical study of EvoLS on commonly used benchmark problems

关键词： Evolutionary algorithms memetic computing computationally expensive problems metamodelling surrogates adaptation evolvability

来源：评论

学校读者我要写书评

暂无评论

PAINT: Pareto front interpolation for nonlinear multiobjective optimization

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2012年第3期52卷 845-867页

作者： Hartikainen, Markus Miettinen, Kaisa Wiecek, Margaret M. Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland Clemson Univ Dept Math Sci Clemson SC USA

A method called PAINT is introduced for computationally expensive multiobjective optimization problems. The method interpolates between a given set of Pareto optimal outcomes. The interpolation provided by the PAINT method implies a mixed integer linear surrogate problem for the original problem which can be optimized with any interactive method to make decisions concerning the original problem. When the scalarizations of the interactive method used do not introduce nonlinearity to the problem (which is true e.g., for the synchronous NIMBUS method), the scalarizations of the surrogate problem can be optimized with available mixed integer linear solvers. Thus, the use of the interactive method is fast with the surrogate problem even though the problem is computationally expensive. Numerical examples of applying the PAINT method for interpolation are included.

关键词： Multiobjective optimization Interactive decision making computationally expensive problems Approximation

来源：评论

学校读者我要写书评

暂无评论

Generalizing Surrogate-Assisted Evolutionary Computation

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2010年第3期14卷 329-355页

作者： Lim, Dudy Jin, Yaochu Ong, Yew-Soon Sendhoff, Bernhard Nanyang Technol Univ Sch Comp Engn Ctr Computat Intelligence Singapore 639798 Singapore Honda Res Inst Europe GmbH D-63073 Offenbach Germany Nanyang Technol Univ Sch Comp Engn Div Informat Syst Singapore 639798 Singapore

Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget.

关键词： Approximation models computationally expensive problems memetic algorithms metamodels surrogate models surrogate-assisted evolutionary algorithms

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：