检索结果-内蒙古大学图书馆

Practical initialization of the Nelder-Mead method for computationally expensive optimization problems

optimization LETTERS 2023年第2期17卷 283-297页

作者： Takenaga, Shintaro Ozaki, Yoshihiko Onishi, Masaki Univ Tsukuba Ibaraki Japan AIST AI Res Ctr Tokyo Japan GREE Inc Tokyo Japan

Black-box optimization (BBO) algorithms are widely employed by practitioners to address computationally expensive real-world problems such as automatic tuning of machine learning models and evacuation route planning. The Nelder-Mead (NM) method is a well-known local search heuristic for BBO that has been applied to solve many real-world problems from way back because of its promising performance. However, this method has a strong dependence on initialization due to its local search tendency. Nevertheless, a discussion on the proper initialization of the NM method is limited to the recent study by Wessing (Optim Lett 13(4):847-856, 2019), which is solely based on an analysis using the simple sphere function. In this study, we take a further step to improve Wessing's result by massively investigating how the initialization affects the search performance in views of the initial simplex size and shape and a constraint handling method that is employed on 24 BBO benchmarking problems. Based on the numerical results, we present the empirical best practice for the initialization of the NM method for cases involving a limited evaluation budget.

关键词： Nelder-Mead method Initialization computationally expensive optimization Black-box optimization

来源：评论

学校读者我要写书评

暂无评论

Turning High-Dimensional optimization Into computationally expensive optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2018年第1期22卷 143-156页

作者： Yang, Peng Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Anhui Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple low-dimensional subproblems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and subproblems makes it nontrivial to precisely assess the quality of a candidate solution to a subproblem, which has been a major hurdle for applying the idea of DC to nonseparable high-dimensional optimization problems. In this paper, we suggest that searching a good solution to a subproblem can be viewed as a computationally expensive problem and can be addressed with the aid of meta-models. As a result, a novel approach, namely self-evaluation evolution (SEE) is proposed. Empirical studies have shown the advantages of SEE over four representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analyzed in the empirical studies.

关键词： computationally expensive optimization divide-and-conquer (DC) evolutionary algorithms (EAs) high-dimensional optimization

来源：评论

学校读者我要写书评

暂无评论

Multiobjective Tree-structured Parzen Estimator for computationally expensive optimization Problems

Multiobjective Tree-structured Parzen Estimator for Computat...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Ozaki, Yoshihiko Tanigaki, Yuki Watanabe, Shuhei Onishi, Masaki AIST AI Res Ctr Tokyo Japan GREE Inc Tokyo Japan

ISBN: (纸本)9781450371285

Practitioners often encounter computationally expensive multiobjective optimization problems to be solved in a variety of real-world applications. On the purpose of challenging these problems, we propose a new surrogate-based multiobjective optimization algorithm that does not require a large evaluation budget. It is called Multiobjective Tree-structured Parzen Estimator (MOTPE) and is an extension of the tree-structured Parzen estimator widely used to solve expensive single-objective optimization problems. Our empirical evidences reveal that MOTPE can approximate Pareto fronts of many benchmark problems better than existing methods with a limited budget. In this paper, we discuss furthermore the influence of MOTPE configurations to understand its behavior.

关键词： Multiobjective optimization Tree-structured Parzen Estimator computationally expensive optimization Bayesian optimization Surrogate Modeling Infill Criteria Machine Learning

来源：评论

学校读者我要写书评

暂无评论

An Iterative Two-Stage Multifidelity optimization Algorithm for computationally expensive Problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第3期27卷 520-534页

作者： Kenny, Angus Ray, Tapabrata Singh, Hemant Kumar Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2612 Australia

Engineering design optimization often involves use of numerical simulations to assess the performance of candidate designs. The simulations for computing high-fidelity (HF) performance estimates, such as finite element analysis or computational fluid dynamics, are typically computationally expensive. In some cases, it may also be possible to run an alternate or cheaper version of the simulation (through, e.g., use of a coarse mesh) to yield a low-fidelity (LF) performance estimate. Multifidelity optimization refers to the class of methods that aim to manage LF and HF evaluations efficiently to optimize computationally expensive problems within a limited computing budget. Among the prominent existing multifidelity approaches, some of them depend on a sufficiently dense a priori sampling;while others use unidirectional information exchange from LF to HF;both of which lead to a possibility of spending evaluation budget on unpromising search regions. This article proposes an improved multifidelity approach using an iterative, two-stage scheme (MFITS). It uses the collective information from the previously evaluated designs to determine a sampling neighborhood for LF evaluations. These samples are, in turn, used for building a co-kriging surrogate model that is then searched globally to identify a good candidate for HF evaluation. By restricting the LF sampling neighborhood, the computational budget can be used more efficiently, as the search is focused on regions that have historically produced good quality solutions. Numerical experiments and benchmarking are conducted on two suites of test problems and two practical design optimization problems to demonstrate the efficacy of MFITS.

关键词： Hafnium Computational modeling optimization Numerical models Numerical simulation Mathematical models Correlation computationally expensive optimization multifidelity optimization

来源：评论

学校读者我要写书评

暂无评论

Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems

引用

SWARM AND EVOLUTIONARY COMPUTATION 2020年 57卷 100713-100713页

作者： Dong, Huachao Dong, Zuomin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Victoria Dept Mech Engn Victoria BC Canada

In this paper, a Surrogate-Assisted Grey Wolf optimization (SAGWO) algorithm for high-dimensional and computationally expensive problems is presented, where Radial Basis Function (RBF) is employed as the surrogate model. SAGWO conducts the search in three phases, initial exploration, RBF-assisted meta-heuristic exploration, and knowledge mining on RBF. In the initial exploration, the Design of Experiments is carried out to generate a group of well-distributed samples based on which the original wolf pack and wolf leaders are sequentially identified to approximate the high-dimensional space roughly. The knowledge mining on RBF includes a global search that is carried out using the grey wolf optimization and a local search that is performed over a focused local region using a search strategy combining global and multi-start local exploration. In the proposed SAGWO, knowledge gained from the RBF model assists the generation of new wolf leaders in each cycle, and the positions of the wolf pack are iteratively changed following the wolf leaders, thus reaching balanced exploitation and exploration. The new SAGWO algorithm presents superior computation efficiency and robustness as demonstrated by comparison tests with ten representative global optimization algorithms on 30, 50 and 100 design variables.

关键词： computationally expensive optimization Grey wolf optimization Radial basis function Surrogate model High-dimensional optimization problems

来源：评论

学校读者我要写书评

暂无评论

SOCEMO: Surrogate optimization of computationally expensive Multiobjective Problems

引用

INFORMS JOURNAL ON COMPUTING 2017年第4期29卷 581-596页

作者： Muller, Juliane Lawrence Berkeley Natl Lab Ctr Computat Sci & Engn Berkeley CA 94720 USA

We present the algorithm SOCEMO for optimization problems that have multiple conflicting computationally expensive black-box objective functions. The computational expense arising from the objective function evaluations considerably restricts the number of evaluations that can be done to find Pareto-optimal solutions. Frequently used multiobjective optimization methods are based on evolutionary strategies and generally require a prohibitively large number of function evaluations to find a good approximation of the Pareto front. SOCEMO, in contrast, employs surrogate models to approximate the expensive objective functions. These surrogate models are used in the iterative sampling process to decide at which points in the variable domain the next expensive evaluations should be done. Therefore, fewer expensive objective function evaluations are needed, and a good approximation of the Pareto front can be found efficiently. Previous algorithms have generally been tested on problems with few variables (up to 10) and few objective functions (up to 5). In our numerical study, we show that our algorithm performs well for benchmark problems with up to 35 dimensions and up to 10 objective functions, as well as two engineering application problems. We compared the performance of SOCEMO to a variant of NSGA-II and show that SOCEMO's sophisticated search strategy is more efficient than NSGA-II when the number of allowable function evaluations is low.

关键词： multiobjective optimization surrogate models black-box optimization computationally expensive optimization radial basis function

来源：评论

学校读者我要写书评

暂无评论

Adaptive multi-surrogate and module-based optimization algorithm for high-dimensional and computationally expensive problems

引用

INFORMATION SCIENCES 2023年第1期645卷

作者： Wu, Mengtian Xu, Jin Wang, Lingling Zhang, Chengxiao Tang, Hongwu Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing Peoples R China Hohai Univ Key Lab Hydrol Cycle & Hydrodynam Syst Minist Water Resources Nanjing Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Hohai Univ Coll Agr Sci & Engn Nanjing Peoples R China China Three Gorges Corp Wuhan Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving computationally expensive optimization problems. However, the performance of SAEAs deteriorates drastically for high-dimensional problems since the accuracy of surrogates may decrease to an insupportably low level in high dimensions which profoundly affect subsequent evolution optimization. To overcome this issue, this paper proposes an adaptive multi-surrogate and module based optimization algorithm named AMSMO. Unlike existing algorithms that adopt sole surrogate models, AMSMO develops a real-time autoselection of kernels approach for building the most promising RBF. AMSMO makes use of five modules to promote the optimization quality, including: a. global search module;b. local search module;c. semi-local search module;d. convergence module;e. restart module. Specifically, we provide a novel distance-and objective value-based criterion for global search. The semi-local search module applies a dynamic search criterion that makes the algorithm explore at the early search stage and exploit at the final stage. A cluster-based restart criterion is proposed to address the stagnation of optimization. Several benchmark functions with dimensions varying from 30 to 100 and computationally intensive practical simulation-optimization problems are adopted to validate the proposed algorithm. The empirical experiments demonstrate that the proposed algorithm outperforms other state-of-theart algorithms in most cases.

关键词： Surrogate model Radial basis functions High-dimensional problems computationally expensive optimization problems

来源：评论

学校读者我要写书评

暂无评论

Biogeography-based optimization of a variable camshaft timing system

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 45卷 376-387页

作者： Thomas, George Simon, Dan Michelini, John Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA Ford Motor Co Dearborn MI 48121 USA

Automotive simulations often prohibit the use of traditional optimization techniques because these simulations are complex and computationally expensive. These two qualities motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to optimize radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7% over the original control parameters, and we find a tradeoff in population size, and an optimal value for mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the candidate solutions each generation to reduce computation effort and still obtain good BBO solutions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： computationally expensive optimization Biogeography-based optimization Automotive system optimization Design and analysis of computer experiments

来源：评论

学校读者我要写书评

暂无评论

A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design optimization

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2019年第2期3卷 93-105页

作者： Akinsolu, Mobayode O. Liu, Bo Grout, Vic Lazaridis, Pavlos, I Mognaschi, Maria Evelina Di Barba, Paolo Wrexham Glyndwr Univ Dept Comp Wrexham LL11 2AW Wales Univ Birmingham Sch Elect Elect & Syst Engn Birmingham B15 2TT W Midlands England Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England Univ Pavia Sch Elect Comp & Biomed Engn I-27100 Pavia Italy

optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogatemodel aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

关键词： Electromagnetic design optimization electromagnetic design surrogate-model-assisted evolutionary algorithm (SAEA) computationally expensive optimization Gaussian process differential evolution

来源：评论

学校读者我要写书评

暂无评论

A Microwave Filter Yield optimization Method Based on Off-Line Surrogate Model-Assisted Evolutionary Algorithm

引用

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2022年第6期70卷 2925-2934页

作者： Zhang, Zhen Liu, Bo Yu, Yang Cheng, Qingsha S. Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Univ Birmingham Sch Elect Elect & Syst Engn Birmingham B15 2TT W Midlands England

Most existing microwave filter yield optimization methods target a small number of sensitive design variables (e.g., around 5). However, for many real-world cases, more than ten sensitive design variables need to be considered. Due to the complexity, yield optimization quality and efficiency become challenges. Hence, a new method, called yield optimization for filters based on the surrogate model-assisted evolutionary algorithm (YSMA), is proposed. The fundamental idea of YSMA is to construct a single high-accuracy surrogate model offline, which fully replaces electromagnetic (EM) simulations in the entire yield optimization process. Global optimization is then enabled to find designs with substantial yield improvement efficiently using the surrogate model. To reduce the number of necessary samples (i.e., EM simulations) while obtaining the required prediction accuracy, a customized machine learning technique is proposed. The performance of YSMA is demonstrated by two real-world examples with 11 and 14 design variables, respectively. Experimental results show the advantages of YSMA compared to the current dominant sequential online surrogate model-based local optimization methods.

关键词： Microwave theory and techniques Microwave filters optimization methods Neurons Fabrication Computational modeling Support vector machines computationally expensive optimization microwave filters surrogate modeling yield optimization

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：