检索结果-内蒙古大学图书馆

Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization

ENERGY REPORTS 2021年 7卷 5612-5626页

作者： She, Chen Jia, Rui Hu, Bei-Ning Zheng, Ze-Kun Xu, Yi-Peng Rodriguez, Dragan TianGong Univ Sch Econ & Management Tianjin 300387 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Dept Construct Management Beijing 100044 Peoples R China Hainan Univ Coll Civil Engn & Architecture Haikou 570100 Hainan Peoples R China Hangzhou HFI Hlth Network Technol Co Ltd Hangzhou Zhejiang Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA

The energy consumption in buildings is increasing, then, a new global multiple-objective optimization method for zero-energy buildings with the comprehensive assessment method is used in this study. The applied optimization design methods for renewable energy systems are the multiple-objective optimization by Enhanced Archimedes optimization Algorithm (EAOA) on the original design for energy costs and energy optimal models. The Hybrid Gray multiple-Level Comprehensive Assessment Method (HGMLCAM) is utilized to the optimal model including the solar energy use efficacy, energy conservation, economic and social factors. By the selected decision based on the global optimal model, the best solution is presented. For designing the practical building, the defined solution can be useful for the architects and decision-makers. Consequently, a multiple-objective design for a building located in Shanghai, in China, is studied. Based on the results, it can be concluded that the presented method is efficient to make decisions. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： multiple-objective optimization Archimedes optimization algorithm Zero-energy building Solar photovoltaic system Energy-efficiency measures Life cycle energy Life cycle cost

来源：评论

学校读者我要写书评

暂无评论

Enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air-fuel ratio control system

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第1期234卷 39-55页

作者： Li, Ziyang Zhou, Quan Zhang, Yunfan Li, Ji Xu, Hongming Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge-based controller has been developed to regulate air-fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge-based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air-fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge-based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge-based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm-based proportional-integral-like fuzzy knowledge-based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm-based proportional-int

关键词： Gasoline direct injection engine fuzzy control air-fuel ratio multiple-objective optimization particle swarm algorithm transient calibration

来源：评论

学校读者我要写书评

暂无评论

Bidding based generator maintenance scheduling with triple-objective optimization

引用

ELECTRIC POWER SYSTEMS RESEARCH 2012年 93卷 127-134页

作者： Zhang, Dabo Li, Wenyuan Xiong, Xiaofu BC Hydro & Power Author Vancouver BC V7X 1V5 Canada Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China

A new bidding based generating unit maintenance scheduling method under the electricity market environment is presented in this paper. The method can balance overall reliability and economy, and ensure the fairness to all generation companies (GENCOs). The unit maintenance scheduling is modeled as a triple-objective non-convex optimization problem with discrete variables and is solved using an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II). A non-inferior solution set is directly produced from huge combinations. A few candidate solutions are selected from the non-inferior set by independent system operator (ISO) based on the overall reliability and economy, whereas the final maintenance schedule is determined by voting of GENCOs. The shares of vote are calculated from the bidding information of GENCOs and the right of each GENCO is fairly guaranteed. A case study of 50 units demonstrates the effectiveness of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Electricity market Bidding Generation Maintenance multiple-objective optimization Reliability

来源：评论

学校读者我要写书评

暂无评论

Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第7期233卷 1698-1711页

作者： Zhang, Yunfan Zhou, Quan Li, Ziyang Li, Ji Xu, Hongming Univ Birmingham Sch Engn Dept Mech Engn Birmingham W Midlands England

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine's fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller's transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests.

关键词： Diesel engine multiple-objective optimization particle swarm algorithm transient calibration test bench validation

来源：评论

学校读者我要写书评

暂无评论

optimization models for training belief-rule-based systems

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2007年第4期37卷 569-585页

作者： Yang, Jian-Bo Liu, Jun Xu, Dong-Ling Wang, Jin Wang, Hongwei Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Hefei Univ Technol Hefei Peoples R China Univ Ulster Fac Engn Sch Comp & Math Jordanstown BT37 0QB Newtownabbey North Ireland Liverpool John Moores Univ Sch Engn Liverpool L3 3AF Merseyside England

A belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.

关键词： belief rule base (BRB) decision making evidential reasoning (ER) expert system inference multiple-objective optimization uncertainty

来源：评论

学校读者我要写书评

暂无评论

Integrated velocity optimization and energy management for FCHEV: An eco-driving approach based on deep reinforcement learning

引用

ENERGY CONVERSION AND MANAGEMENT 2023年 296卷

作者： Chen, Weiqi Peng, Jiankun Ren, Tinghui Zhang, Hailong He, Hongwen Ma, Chunye Southeast Univ Sch Transportat Nanjing 211102 Peoples R China North Univ China Coll Mech Engn Taiyuan 030051 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Ecological driving (eco-driving) is a promising technology for transportation sector to save energy and reduce emission, which works by improving vehicle behaviors in traffic scenarios. Fuel cell hybrid electric vehicles (FCHEV) are receiving extensive attentions due to global fossil energy crisis, but whose implementations for eco-driving result in multiple objective collaborative optimization problems. In this paper, an eco-driving framework for FCHEV is proposed based on deep deterministic policy gradient (DDPG) algorithm. And it combines adaptive cruise control (ACC) and energy management strategy (EMS) into an integrated architecture. Firstly, in order to achieve excellent balance between driving behaviors and fuel economy, an appropriate weight coefficient value is determined after adequate explorations. Secondly, power-varying equivalent hydrogen conversion coefficient function is constructed to save fuel consumption by 8.97%. Thirdly, ablation experiments for health state of fuel cell system present 19.95% decrease in terms of health degradation. Then, comparison experiments indicate that the DDPG-based eco-driving strategy can reach 94.16% of that of dynamic programming with respect to equivalent hydrogen consumption, meanwhile with best ride comfortability. Moreover, simulation results under validation driving cycle manifest its excellent adaptability.

关键词： Ecological driving Fuel cell hybrid electric vehicle multiple-objective optimization Deep deterministic policy gradient

来源：评论

学校读者我要写书评

暂无评论

Grey relational analysis-based composite coating property optimization fabricated by laser cladding

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期120卷 7985-7996页

作者： Lian, Guofu Liu, Zhaozhen Zhang, Yang Que, Linzhi Chen, Changrong Yue, Kun Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350118 Peoples R China Western Carolina Univ Sch Engn Technol Cullowhee NC 28723 USA

This paper aims at optimizing the surface hardness and wear resistance of composite coatings. The influence of Ni35A proportions in the composite mixing with TiC, WC, and W6Mo5Cr4V2 were explored based on mixed-level orthogonal experiment design. Experiments were conducted to examine the hardness and friction. The worn morphology was analysed by the white light interferometer. The multi-response optimization was achieved by the grey correlation analysis. The results show that both the powder ratio and powder type had significant impact on the micro-hardness and wear volume of the coatings, where powder ratio has more significance. Finally, the multi-index optimization was conducted with the target of maximizing the micro-hardness and minimizing the wear volume simultaneously by combining grey relational analysis and orthogonal experiment. The error rate is 1.122% between the prediction and experimental validation. Compared with the substrate, the micro-hardness of optimized cladding layer was improved from 15.2 to 68.8 HRC, while the wear volume was reduced from 15.28257 to 1.04831 center dot 10(-3) mm(3). The comparison of the optimal group in the orthogonal experiment indicates that the micro-hardness and wear resistance of optimized cladding layer were also improved. The optimized cladding layer has fine, dense, and evenly distributed grains with coarse and complete grain boundaries, suggesting that the micro-hardness and wear resistance were improved after optimization. The application of the mixed-level orthogonal experiment design combined with the grey correlation analysis allows to achieve the optimal selection of various hard-phase composite materials and the process optimization.

关键词： Laser cladding Composite coating Grey relation grade Micro-hardness Wear resistance multiple-objective optimization

来源：评论

学校读者我要写书评

暂无评论

The gap function for optimization problems in Banach spaces

引用

NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2008年第2期69卷 716-723页

作者： Soleimani-damaneh, M. Teacher Training Univ Dept Mat Tehran 15618 Iran

关键词： multiple-objective optimization gap function variational inequality Clarke's gradient preinvexity

来源：评论

学校读者我要写书评

暂无评论

Using multicriterion optimization to classify ecosystems from multitemporal imagery

引用

PHYSICAL GEOGRAPHY 1996年第3期17卷 282-293页

作者： Ridgley, MA Aerts, JCJH RESOURCE ANAL BV NL-2611 SJ DELFT NETHERLANDS

One element of research on climate change is modeling of the non-fossil carbon cycle. Non-fossil carbon models such as IMAGE-2 use ecosystem maps as inputs for calculating the production of carbon and the levels of carbon in the atmosphere. The NFOSEUR project aims to classify European ecosystems from remote sensing (RS) data. The data consist of monthly NDVI values for each year, where NDVI is a measure of the photosynthetic activity of the ecosystems. Each ecosystem has a characteristic NDVI curve throughout the year, yielding a characteristic ''fingerprint'' far each ecosystem. Until now classification of RS data has been based on various statistical procedures. This paper discusses the use of multicriterion optimization to determine the ''best'' classification. Coal programming and compromise goal programming models yield classifications that compare closely with those produced by conventional statistical approaches. Case studies include a classification of ecosystems far Germany and southern France. Various potential improvements to our models are discussed, including new formulations and the prospects for fuzzy linear and goal programming models.

关键词： multiple-objective optimization remote sensing classification ecosystems

来源：评论

学校读者我要写书评

暂无评论

WEIGHTING FACTOR RESULTS IN VECTOR optimization

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 1988年第3期58卷 411-430页

作者： DURIER, R 1.Laboratoire d'Analyse Numérique Université de Dijon Dijon France

This paper deals with the study, in a convex vector optimization problem, of the set of efficient solutions and the set of properly efficient solutions, the latter being obtained by a weighting factor technique. Relationships between these two sets are discussed; they are shown to be nonempty when the objective functions have no common direction of recession and to be closed and equal when, moreover, the objective functions are locally polyhedral. An example is provided where the set of efficient solutions is not included in the closure of the nonempty set of properly efficient solutions.

关键词： multiple-objective optimization efficient points properly efficient points recession cones recession functions

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：