检索结果-内蒙古大学图书馆

An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization

INFORMATION SCIENCES 2020年第0期519卷 317-331页

作者： Wang, Xilu Jin, Yaochu Schmitt, Sebastian Olhofer, Markus Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Honda Res Inst Europe GmbH Carl Legien Str 30 D-63073 Offenbach Germany

surrogate models have been widely used for solving computationally expensive multi-objective optimization problems (MOPS). The efficient global optimization (EGO) algorithm, a Bayesian approach to surrogate-assisted optimization, has become very popular in surrogate-assisted evolutionary optimization. In this paper, we propose an adaptive Bayesian approach to surrogate-assisted evolutionary algorithm to solve expensive MOPs. The main idea is to tune the hyperparameter in the acquisition function according to the search dynamics to determine which candidate solutions are to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches between an angle based distance and an angle-penalized distance over the course of optimization to achieve a better balance between exploration and exploitation. The performance of the proposed algorithm is examined on a set of benchmark problems and an airfoil design optimization problem using a maximum of 300 real fitness evaluations. Our experimental results show that the proposed algorithm is competitive compared to four popular multi-objective evolutionary algorithms. (C) 2020 Published by Elsevier Inc.

关键词： Expensive multi-objective optimization surrogate-assisted evolutionary algorithm Bayesian optimization Acquisition function Reference vectors

来源：评论

学校读者我要写书评

暂无评论

Fast evolutionary Neural Architecture Search Based on Bayesian surrogate Model

Fast Evolutionary Neural Architecture Search Based on Bayesi...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Shi, Rui Luo, Jianping Liu, Qiqi Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

ISBN: (纸本)9781728183923

Neural Architecture Search (NAS) is studied to automatically design the deep neural network structure, freeing people from heavy network design tasks. Traditional NAS based on individual performance evaluation needs to train many networks generated by the search, and compare the performance of the networks according to their accuracy, which is very time-consuming. In this study, we propose to use a two-category comparator based random forest model as a surrogate to estimate the accuracy of the networks. thereby reducing heavy network training process and greatly saving search time. Instead of directly predicting the accuracy of each network, we propose to compare the relative performance between each two networks in our proposed two-category comparator. Furthermore, we implement the modeling process of the surrogate model in the sampling space of the original training data, which further accelerates the search process of the network in the NAS. Experimental results show that our proposed NAS framework can greatly reduce the search time, while the accuracy of the obtained network is comparable to that of other state-of-the art NAS algorithms.

关键词： Bayesian Optimization surrogate-assisted evolutionary algorithm Neural Architecture Search Deep Neural Network

来源：评论

学校读者我要写书评

暂无评论

ECdo: An Edge Computing Distributed Data-Driven evolutionary Optimization Platform

ECdo: An Edge Computing Distributed Data-Driven Evolutionary...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zeng, Qing-Ye Wei, Feng-Feng Guo, Xiao-Qi Chen, Wei-Neng School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

ISBN: (纸本)9798350337020

surrogate-assisted evolutionary algorithms (SAEAs) have become a popular method to solve data-driven optimization problems (DOPs), which are common in industry. However, with the development of the Internet of Things, data are collected, processed, and stored in a distributed manner, leading a new optimization paradigm for SAEAs. To make SAEAs adapt to these distributed DOPs, this paper employs the edge computing paradigm to develop a platform that provides technical support for SAEAs with distributed structures, named ECdo. Specifically, the platform utilizes KubeEdge, an open-source edge computing framework, to mount the cluster and combines microservice interface design with the containerization strategy to offer a flexible deployment approach for distributed SAEAs. In addition, an efficient and stable internal communication mechanism is designed for the interaction between distributed components within the platform. To demonstrate the application of ECdo, we take the examples of a class of distributed DOPs, in which the objective and constraints are expensive and need to be approximated by accumulated data. These problems are known as distributed and expensive constrained optimization problems (DECOPs). We implement a distributed SAEA on ECdo to address DECOPs in real-world scenarios. Experiments show that the ECdo can provide the expected implementation for distributed SAEAs with good network tolerance under tough network conditions. © 2023 IEEE.

关键词： distributed optimization edge computing expensive optimization surrogate-assisted evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

引用

Chinese Journal of Aeronautics 2023年第6期36卷 213-232页

作者： Zengcong LI Kuo TIAN Shu ZHANG Bo WANG State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116024China

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.

关键词： Covariance matrix adaptation evolution strategy Model management Multi-objective optimization surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Multi-Variable Large-Size Fan Aerodynamic Optimization by Using Multi-Model Ensemble Optimization algorithm

引用

Journal of Thermal Science 2024年第3期33卷 914-930页

作者： XIONG Jin GUO Penghua LI Jingyin School of Energy and Power Engineering Xi’an Jiaotong UniversityXi’an710049China

The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is *** this study,a new multi-model ensemble optimization algorithm is proposed to tackle such an expensive optimization *** multi-variable and multi-objective optimization are conducted with a new flexible multi-objective infill *** addition,the search direction is determined by the multi-model ensemble assisted evolutionary algorithm and the feature extraction by the principal component analysis is used to reduce the dimension of optimization ***,the proposed algorithm and other two optimization algorithms which prevail in fan optimizations were compared by using test *** the same number of objective function evaluations,the proposed algorithm shows a fast convergency rate on finding the optimal objective function ***,this algorithm was used to optimize the rotor and stator blades of a large axial fan,with the efficiencies as the objectives at three flow rates,the high,the design and the low flow ***-two variables were included in the optimization *** results show that compared with the prototype fan,the total pressure efficiencies of the optimized fan at the high,the design and the low flow rate were increased by 3.35%,3.07%and 2.89%,respectively,after CFD simulations for 500 fan candidates with the constraint for the design *** optimization results validate the effectiveness and feasibility of the proposed algorithm.

关键词： multi-objective optimization surrogate-assisted evolutionary algorithm axial fan computational fluid dynamics aerodynamic optimization

来源：评论

学校读者我要写书评

暂无评论

Lightweight design optimization of two-layer corrugated cored sandwich panel under blast loading using surrogate-assisted different evolution for mixed-integer variables

引用

ENGINEERING STRUCTURES 2024年 321卷

作者： Liu, Yuanhao Yang, Zan Jiang, Chen Xu, Danyang Qiu, Haobo Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Jiangxi Tellhow Sci Tech Co Ltd Nanchang 330031 Peoples R China Nanchang Univ Jiangxi Prov Key Lab Light Alloy Nanchang 330031 Peoples R China Natl Ctr Technol Innovat Intelligent Design & Nume Wuhan 430074 Peoples R China

The sandwich structure stands out as an ideal candidate for blast-resistant armor due to its excellent performance, of which the optimization typically involves time-consuming finite element simulations. Recently, surrogate models have emerged as a promising alternative for time-consuming simulations to accelerate the optimization process of blast-resistant sandwich panels. However, existing methods are limited to optimizing a handful of continuous variables, and often focus on single-layer core sandwich panels. This article introduces a novel constrained optimization model for Two-layer composite Corrugated cored Sandwich Panels (TCSP). The design variables include not only continuous variables such as structural component heights and thicknesses, but also integer variables such as material selection and the number of core unit-cells. To tackle this complex optimization problem with mixed-integer variables, a surrogate-assisted differential evolution (SADE-TCSP) is proposed. This algorithm utilizes the predictive capabilities of surrogate models to guide the optimization process towards a high-quality feasible solution within a very limited computational budget. In order to validate our approach, an optimization framework integrating MATLAB and ABAQUS is developed to conduct numerical experiments. Experimental results show that the proposed method can achieve a 51.04 % reduction in total structural mass while satisfying performance constraints, which is significantly better than existing methods.

关键词： Sandwich structure Expensive constrained optimization Blast resistance surrogate-assisted evolutionary algorithm Finite element Mixed-integer

来源：评论

学校读者我要写书评

暂无评论

Elite-driven surrogate-assisted CMA-ES algorithm by improved lower confidence bound method

引用

ENGINEERING WITH COMPUTERS 2023年第4期39卷 2543-2563页

作者： Li, Zengcong Gao, Tianhe Tian, Kuo Wang, Bo Dalian Univ Technol Dept Engn Mech Key Lab Digital Twin Ind Equipment State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

To relieve the computational burden and improve the global optimizing ability of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for real-world expensive problems, an elite-driven surrogate-assisted CMA-ES (ES-CMA-ES) algorithm by the improved Lower Confidence Bound (ILCB) method is proposed in this paper. Firstly, the ILCB method is established by introducing the step size, which captures the trend of exploration and exploitation in CMA-ES, to control the uncertainty term of the ILCB formula adaptively. Next, based on the ILCB method, a novel model management consisting of the efficient pre-screening strategy and the competitive chaotic operator is developed. In each generation of ES-CMA-ES, a large number of candidate points are sampled first, and then a few of them with better ILCB predicted values are screened out by the efficient pre-screening strategy, aiming to enhance the sampling quality and accelerate the optimization convergence. Moreover, the local search is performed on the best-performing screened sample points utilizing the competitive chaotic operator, with the purpose of increasing the diversity of populations in ES-CMA-ES and avoiding being trapped in the local optima. By means of the above procedures of the model management, the elite sample points are finally obtained which will be evaluated by true fitness function in each generation of ES-CMA-ES. To verify the effectiveness of ES-CMA-ES, five known black-box optimization algorithms are employed to make a comparison. Firstly, seven typical numerical examples of 10-dimensional and 20-dimensional benchmark functions are carried out, respectively. Furthermore, a 20-dimensional engineering example of the aerospace variable-stiffness composite shell under combined loadings is studied. Results indicate the outstanding efficiency, global optimizing ability and applicability of the proposed ES-CMA-ES compared to its counterpart algorithms.

关键词： Covariance matrix adaptation evolution strategy surrogate-assisted evolutionary algorithm Lower confidence bound Pre-screening strategy Chaotic operator

来源：评论

学校读者我要写书评

暂无评论

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第3期19卷 169-177页

作者： Gong, Jinyu Yan, Xuesong Hu, Chengyu China Univ Geosci Sch Comp Sci Wuhan Peoples R China

The safety of water is vital to residents' health. The water supply network system could be accidentally or intentionally infringe easily, leading to the water contamination. Sensor networks can obtain useful observations to identify contamination events by detecting pollution in water supply networks, which can help to quickly identify the location and other related characteristics of contamination source. This paper proposes an ensemble-surrogate assisted particle swarm optimisation algorithm to solve water contamination source identification problem. Firstly, a modified collaborative particle swarm optimisation algorithm is proposed according to the characteristics of decision variables, which divides the three components reasonably, and adopts different evolutionary strategies to different components. Secondly, this paper uses ensemble-surrogate to solve computing expensive problem. An offline-model management is proposed to use less EPANET simulation. At last, experimental results show that the proposed algorithm is effective and efficient.

关键词： water contamination source identification expensive optimisation surrogate-assisted evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第1-2期130卷 253-266页

作者： Wang, Xiaohan Zhang, Lin Laili, Yuanjun Liu, Yongkui Li, Feng Chen, Zhen Zhao, Chun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China KTH Royal Inst Technol Dept Prod Engn S-10044 Stockholm Sweden Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China

Manufacturing systems develop toward cloud-edge collaboration where manufacturing and computation are tightly coupled. Under this circumstance, large-scale hybrid tasks that include manufacturing and computational tasks need to be collaboratively scheduled among heterogeneous resources. This paper solves the hybrid task scheduling problem in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution (F-RDE). First, we establish a system model for hybrid task scheduling with the objective of minimizing the total makespan and energy consumption. This model includes four types of time constraints between the hybrid tasks. The scheduling of such hybrid tasks has received limited attention in existing research. Next, large-scale decision variables are encoded into the evolutionary chromosomes. To generate offspring chromosomes, we construct four differential evolution operators that are randomly selected during the search process. Furthermore, we propose the fully convolutional regression network (FCRN) as a novel surrogate model to accelerate fitness evaluation. To enhance the integration of FCRN and the differential evolution procedure, we employ three strategies: chromosome folding, top-K re-evaluation, and three training modes. The FCRN surrogate can effectively represent chromosomes with up to 12000 dimensions and achieve generalization across diverse scheduling cases. This leads to reduced solving time and enhanced fitness estimation accuracy. Numerical experiments on three hybrid task scheduling cases validate the superiority compared to the other twelve scheduling algorithms, and the proposed FCRN surrogate can save at most 43% of solving time.

关键词： Scheduling surrogate-assisted evolutionary algorithm Cloud manufacturing Intelligent manufacturing systems Cloud-edge collaboration

来源：评论

学校读者我要写书评

暂无评论

An adaptive surrogate-assisted particle swarm optimization for expensive problems

引用

SOFT COMPUTING 2021年第24期25卷 15051-15065页

作者： Li, Xuemei Li, Shaojun East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

关键词： surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm 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：