检索结果-内蒙古大学图书馆

Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116411-116411页

作者： Segura-Delgado, Alberto Anguita-Ruiz, Augusto Alcala, Rafael Alcala-Fdez, Jesus Univ Granada Dept Comp Sci & Artificial Intelligence DaSCI Granada 18071 Spain Barcelona Inst Global Hlth ISGlobal Barcelona 08003 Spain

High-utility sequential pattern mining techniques have demonstrated good performance in identifying associations between mRNA levels in microarray experiments taking into account both the biological context of each gene and the temporal characteristics of the dataset. However, these patterns do not provide information about how likely it is that the events in the pattern occur in the order indicated, therefore causal relationships cannot be established between of them. This reduces their predictive ability, making difficult its direct applicability to the field of gene expression dynamic modeling. An alternative to sequential patterns which takes the confidence of the forecast into account is the discovery of sequential rules. Their natural and seamless relation to human behavior makes them very suitable to understand complex models without missing the possibility of using the generated rules as a standalone prediction model. This contribution proposes an evolutionary algorithm optimizing multiple objectives for mining biologically relevant high average-utility sequential rules from longitudinal human gene expression data with a good compromise through average-utility and explainability. This proposal enhances the well-known NSGA-II to learn, by evolutionary optimization, the rules maximizing two objectives: Utility and Interestingness. Moreover, a restarting mechanism and an external population have been particularly designed and included in order to encourage diversity in the search process preserving all the rules found. The quality of our approaches has been analyzed using external biological resources, statistical analysis and comparing with other proposals from the literature.

关键词： High average-utility sequential rules multi-objective evolutionary algorithm eXplainable artificial intelligence Gene expression patterns Obesity

来源：评论

学校读者我要写书评

暂无评论

Parametric optimization of an inerter-based vibration absorber for wind-induced vibration mitigation of a tall building

引用

WIND AND STRUCTURES 2020年第3期31卷 241-253页

作者： Wang, Qinhua Qiao, Haoshuai Li, Wenji You, Yugen Fan, Zhun Tiwari, Nayandeep Shantou Univ Dept Civil & Environm Engn Shantou City Guangdong Peoples R China Shantou Univ Guangdong Engn Ctr Struct Safety & Hlth Monitorin Shantou 515063 Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou City Guangdong Peoples R China Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Guangdong Peoples R China

The inerter-based vibration absorber (IVA) is an enhanced variation of Tuned Mass Damper (TMD). The parametric optimization of absorbers in the previous research mainly considered only two decision variables, namely frequency ratio and damping ratio, and aimed to minimize peak displacement and acceleration individually under the excitation of the across-wind load. This paper extends these efforts by minimizing two conflicting objectives simultaneously, i.e., the extreme displacement and acceleration at the top floor, under the constraint of the physical mass. Six decision variables are optimized by adopting a constrained multi-objective evolutionary algorithm (CMOEA), i.e., NSGA-II, under fluctuating across- and along-wind loads, respectively. After obtaining a set of optimal individuals, a decision-making approach is employed to select one solution which corresponds to a Tuned Mass Damper Inerter/Tuned Inerter Damper (TMDI/TID). The optimization procedure is applied to parametric optimization of TMDI/TID installed in a 340-meter-high building under wind loads. The case study indicates that the optimally-designed TB) outperforms TMDI and TMD in terms of wind-induced vibration mitigation under different wind directions, and the better results are obtained by the CMOEA than those optimized by other formulae. The optimal TB) is proven to be robust against variations in the mass and damping of the host structure, and mitigation effects on acceleration responses are observed to be better than displacement control under different wind directions.

关键词： inerter-based vibration absorber multi-objective evolutionary algorithm decision-making approach wind-induced vibration high-rise buildings

来源：评论

学校读者我要写书评

暂无评论

An adaptive penalty-based boundary intersection method for many-objective optimization problem

引用

INFORMATION SCIENCES 2020年 509卷 356-375页

作者： Qi, Yutao Liu, Dazhuang Li, Xiaodong Lei, Jiaojiao Xu, Xiaoying Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

Compared with domination-based methods, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is less prone to the difficulty caused by an increase in the number of objectives. It is a promising algorithmic framework for solving many-objective optimization problems (MaOPs). In MOEA/D, the target MaOP is decomposed into a set of single-objective problems by using a scalarizing function with evenly specified weight vectors. Among the available scalarizing functions, penalty-based boundary intersection (PBI) with an appropriate penalty parameter is known to perform well. However, its performance is heavily influenced by the setting of the penalty factor (0), which can take a value from zero to +infinity. A limited amount of work has thus far considered the choice of an appropriate value of theta. This paper presents a comprehensive experimental study on WFG and WFG-extend problems featuring two to 15 objectives. A range of values of theta is investigated to understand its influence on the performance of the PBI-based MOEA/D (MOEA/D-PBI). Based on the observations, the range of values of theta are divided into three sub-regions, and a two-stage adaptive penalty scheme is proposed to adaptively choose an appropriate value from 0.001 to 8000 during an optimization run. The results of experiments show that, the robustness of MOEA/D-PBI can be significantly enhanced using the proposed scheme. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Many-objective optimization multi-objective evolutionary algorithm based on decomposition Penalty-based boundary intersection Adaptive penalty scheme

来源：评论

学校读者我要写书评

暂无评论

Intensifying the search-based optimization of product line architectures with crossover operators

引用

EMPIRICAL SOFTWARE ENGINEERING 2022年第7期27卷 166-166页

作者： da Silva, Diego Fernandes Okada, Luiz Fernando Assuncao, Wesley K. G. Colanzi, Thelma Elita State Univ Maringa UEM Maringa Parana Brazil DI Pontifical Catholic Univ Rio de Janeiro PUC Ri Rio De Janeiro Brazil PPGComp Western Parana State Univ Unioeste Cascavel Brazil

The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results.

关键词： multi-objective evolutionary algorithm Software product line Software architecture Recombination operators

来源：评论

学校读者我要写书评

暂无评论

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

引用

Chinese Journal of Aeronautics 2020年第5期33卷 1549-1561页

作者： Canfei SUN Youren WANG Guodong SUN College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Testing Center Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementShanghai 201601China

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the *** health condition monitoring,the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear *** to actual situation,this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the highdimensional original feature *** MCFFSA,a fault feature set of multiple domains,including time domain,frequency domain and wavelet domain,is first extracted from the raw vibration *** targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets,wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM).Further,Fmeasure is adopted to identify the optimal feature subset,which was employed for subsequent fault *** effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission *** experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA,and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.

关键词： Fault detection Feature selection F-measure Helicopter planetary gear train multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A multi-objective optimization approach in defining the decarbonization strategy of a refinery

引用

SMART ENERGY 2022年 6卷

作者： de Maigret, Jacopo Viesi, Diego Mahbub, Md Shahriar Testi, Matteo Cuonzo, Michele Thellufsen, Jakob Zinck Ostergaard, Poul Alberg Lund, Henrik Baratieri, Marco Crema, Luigi Fdn Bruno Kessler FBK Via Sommar 18 I-38123 Povo TN Italy Ahsanullah Univ Sci & Technol AUST Love Rd 141&142 Tejgaon Ind Area Dhaka 1208 Bangladesh Sonatrach Raffineria Italiana Srl Raffineria Augusta I-96011 Augusta Contrada Marcel Italy Aalborg Univ Rendsburggade 14 DK-9000 Aalborg Denmark Univ Bozen Bolzano Fac Sci & Technol Univ pl 5 Piazza Univ5 I-39100 Bolzano BZ Italy

Nowadays, nearly one quarter of global carbon dioxide emissions are attributable to energy use in in-dustry, making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a multi -objective evolutionary algorithm (MOEA), considering the minimization of annual cost and CO2 emis-sions as two potentially conflicting objectives and the energy technologies' capacities as decision vari-ables. For the target year 2025, EnergyPLAN + MOEA has allowed to model a range of 0e100% decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical, thermal, hydrogen feedstock and transport demand. A set of nine scenarios, with different land use availabilities and implementable technologies, each consisting of 100 optimal systems out of 10,000 simulated ones, has been evaluated. The results show, on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones, on the other, how the decar-bonization pathways strongly depend on the available land for solar thermal, photovoltaic and wind, as well as the presence of a biomass supply chain in the region.& COPY;2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Refining industry Energy system integration Renewable energy Low-carbon economy multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

multi-step time series prediction intervals using neuroevolution

引用

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 8939-8953页

作者： Cortez, Paulo Pereira, Pedro Jose Mendes, Rui Univ Minho ALGORITMI Ctr Dept Informat Syst P-4804533 Guimaraes Portugal Univ Minho Ctr Biol Engn ALGORITMI Ctr Dept Informat P-4710057 Braga Portugal

multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower-upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage-width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.

关键词： Prediction intervals Time series multi-objective evolutionary algorithm multilayer perceptron evolutionary neural network

来源：评论

学校读者我要写书评

暂无评论

A matheuristic for a bi-objective demand-side optimization for cooperative smart homes

引用

ELECTRICAL ENGINEERING 2020年第4期102卷 1913-1930页

作者： Garroussi, Zineb Ellaia, Rachid El-ghazali-Talbi Lucas, Jean-yves Mohammed V Univ Rabat Mohammadia Sch Engineers Engn Smart & Sustainable Syst Res Ctr E3S LERMA Lab Ibn Sina Ave 765 Rabat Morocco Inria Lille Nord Europe Res Ctr Big Optimizat & Ultra Scale Comp Team BONUS F-59655 Villeneuve Dascq France Elect France SA EDF R&D 1 Ave Gen de Gaulle F-92140 Clamart France

This paper proposes the demand-side management (C-DSM) of collaborative homes during one day. Some homes may have photovoltaic panels and batteries, other homes may have batteries, and the remaining homes are pure energy consumers. Homes are interconnected and connected to the main grid. The proposed C-DSM is formulated as a constrained bi-objective and mixed-integer linear optimization model with one objective related to the total net energy cost and the other related to discomfort caused by allowing flexibility of controllable loads within an acceptable comfort range. A matheuristic approach has been proposed to determine an efficient Pareto set for the problem, combining the non-dominated sorting genetic algorithm II (NSGAII) with an exact solver. In this approach, discrete decision variables are represented as partial chromosomes and an exact solver determines the continuous decision variables in an optimal way. A number of simulations are performed and compared with the weighted sum algorithm (WSA) under four cases for small to large number of homes. The results demonstrate the effectiveness of power cooperation among homes and show that our algorithm is able to obtain more Pareto solutions in a much shorter time that are far better than those obtained by the WSA. The proposed algorithm is suitable for large-sized C-DSM problem instances, promotes power cooperation between homes, reduces the dependency to the main grid and achieves individual fairness of energy net cost of each home without the need for installation of photovoltaic panels and batteries for all homes.

关键词： Microgrids Demand-side management of multiple homes multi-objective evolutionary algorithm Mixed-integer linear programming Matheuristic

来源：评论

学校读者我要写书评

暂无评论

A direct solution framework for structural optimization problems with interval uncertainties

引用

APPLIED MATHEMATICAL MODELLING 2020年 80卷 384-393页

作者： Fu, Chunming Liu, Zhiwen Deng, Jian Univ South China Coll Mech Engn Hengyang 421001 Peoples R China Univ South China Cooperat Innovat Ctr Nucl Fuel Cycle Technol & Eq Hengyang 421001 Peoples R China

A direct solution framework based on multi-objective evolutionary algorithm is developed to solve the structural optimization problems with interval uncertainties. The midpoint and radius of the uncertain original objective are treated as two equally important objectives, which are solved by a multi-objective evolutionary algorithm. The satisfaction value of interval possibility degree model is utilized to deal with nonlinear uncertain constraints and then the degree of constraint violation based on this model is calculated to judge the design vector individuals which one is feasible or infeasible. Subsequently, a selection strategy based on interval constrained-domination rule is utilized to realize the ranking of different design vectors. Finally, two numerical examples and the structural design of augmented reality glasses are investigated to verify the applicability and effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Interval optimization multi-objective evolutionary algorithm Uncertain structural design Constrained-domination

来源：评论

学校读者我要写书评

暂无评论

Design of a 162.5 MHz continuous-wave normal-conducting radiofrequency electron gun

引用

Nuclear Science and Techniques 2020年第11期31卷 50-59页

作者： Cheng Wang Zi-Han Zhu Zeng-Gong Jiang Qi-Sheng Tang Zhen-Tang Zhao Qiang Gu Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China

A high-gradient radiofrequency(RF)gun operated in continuous-wave(CW)mode is required in various accelerating *** to the high RF power loss,a traditional normal-conducting(NC)RF electron gun has difficulty meeting the requirement of generating a high-repetition-rate electron *** development of a scheme for a CW NC-RF gun is urgently *** as a photoinjector of a high-repetition-rate free-electron laser(FEL),an electron gun operated in CW mode and the VHF band is *** analysis of the reentrant gun cavity is presented in this paper to increase the gradient and decrease the power density and power *** to the analysis results,the design of a162.5 MHz gun cavity is optimized by a multi-objective evolutionary algorithm to achieve better performance in CW *** and thermal analyses are also deliberated in the design to coordinate with RF and mechanical *** optimized 162.5 MHz gun cavity can be operated in CW mode to generate a high-repetition-rate beam with voltage up to 1 MV and gradient up to 32.75 MV/m at the cathode.

关键词： High-repetition-rate beam Radiofrequency gun Continuous-wave mode multi-objective evolutionary 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：