检索结果-内蒙古大学图书馆

A chiller operation strategy based on multiple-objective optimization

Energy Procedia 2018年 152卷 318-323页

作者： Shunian Qiu Weijie Zhang Jiajie Li Jialiang Chen Zhenhai Li Zhengwei Li

30%-40% energy consumption of the HVAC system is caused by chillers. The energy consumption of chillers highly depends on the quality of the operation strategy. A chiller operation strategy based on multiple-objective optimization is proposed in this paper. The strategy consists of three steps: (1) With effectiveness functions, two indicators of the operation goodness, indoor comfort and energy consumption, are quantified to two indicators (2) These two indicators are integrated to one comprehensive indicator. (3) The optimal operation condition is determined by an optimization algorithm to maximize the comprehensive indicator. The parameters in this strategy is defined according to on-site surveys, to improve the application value of the strategy. The effectiveness of the proposed strategy is validated on TRNSYS compared with the 3 common operation strategies adopted in buildings of Shanghai. The simulation results suggest that the proposed strategy is able to save energy of HVAC system with limited loss of indoor comfort. Besides, due to the appropriate arrangement of operation order, the proposed strategy could balance the working time of each chiller to put off the discard of chillers.

关键词： Chiller Effectiveness functions Energy simulation multiple-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization of the aerodynamic performance of butterfly-shaped film cooling holes in rocket thrust chamber

引用

INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2025年第1期42卷 159-169页

作者： Yu, Ningning Wang, Zhongwei Lu, Yuming Xu, Weicheng Shan, Yiming Zhang, Hai Harbin Engn Univ Harbin 150001 Peoples R China

This study uses Multi-Island Genetic Algorithm (MIGA) and three-dimensional Computational Fluid Dynamics (CFD) software to optimize butterfly-shaped film cooling holes in the upper-stage rocket engine thrust chamber. The goal is to meet thermal protection and thrust requirements at high altitudes without re-ignition. To facilitate an all-encompassing worldwide search, the holes in the optimized design remain at set dimensions. Film continuity and stability at the nozzle outlet are greatly impacted by the hole structure. Inlet and divergence angles have little effect on thrust, according to regression research, but lip height (de) and outlet width (beta) have a big impact on cold gas ejection, which affects cooling and thrust. Optimized results lead to a 20.49 K decrease in the monitoring section's average wall temperature and a 52.8 N boost in thrust by reducing interference between supersonic airflow and extending film stability.

关键词： multiple-objective optimization film cooling butterfly-shaped film cooling hole temperature thrust

来源：评论

学校读者我要写书评

暂无评论

multiple-objective Packet Routing optimization for Aeronautical Ad-Hoc Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第1期72卷 1002-1016页

作者： Zhang, Jiankang Liu, Dong Chen, Sheng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos Bournemouth Univ Dept Comp & Informat Poole BH12 5BB England Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete $\epsilon$ multi-objective genetic algorithm ($\epsilon$-DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our $\epsilon$-DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

关键词： Aircraft mobility model aeronautical ad-hoc network adaptive coding and modulation routing multiple-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Stochastic Steepest Descent optimization of multiple-objective Mobile Sensor Coverage

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2012年第4期61卷 1810-1822页

作者： Ma, Chris Y. T. Yau, David K. Y. Yip, Nung Kwan Rao, Nageswara S. V. Chen, Jiming Adv Digital Sci Ctr Singapore 138632 Singapore Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Math W Lafayette IN 47907 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor's coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain, wherein the sensor at PoI i decides to go to the next PoI j with transition probability p(ij). We use steepest descent to compute the transition probabilities for optimal tradeoff among different performance goals with regard to the distributions of per-PoI coverage times and exposure times and the entropy and energy efficiency of sensor movement. For computational efficiency, we show how we can optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that the structure of our problem is complex, because there may exist surprisingly many local optima in the solution space, causing basic steepest descent to easily get stuck at a local optimum. To solve the problem, we show how proper incorporation of noise in the search process can get us out of the local optima with high probability. We provide simulation results to verify the accuracy of our analysis and show that our method can converge to the globally optimal control parameters under different assigned weights to the performance goals and different initial parameters.

关键词： Mobile sensor network multiple-objective optimization steepest descent

来源：评论

学校读者我要写书评

暂无评论

Utility function programs and optimization over the efficient set in multiple-objective decision making

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 1997年第3期92卷 605-631页

作者： Horst, R Thoai, NV INST MATH HANOI 10000VIETNAM

Natural basic concepts in multiple-objective optimization lead to difficult multiextremal global optimization problems. Examples include detection of efficient points when nonconvexities occur, and optimization of a linear function over the efficient set in the convex (even linear) case. Assuming that a utility function exists allows one to replace in general the multiple-objective program by a single, nonconvex optimization problem, which amounts to a minimization over the efficient set when the utility function is increasing. A new algorithm is discussed for this utility function program which, under natural mild conditions, converges to an E-approximate global solution in a finite number of iterations. Applications include linear, convex, indefinite quadratic, Lipschitz, and d.c. objectives and constraints.

关键词： multiple-objective optimization utility function programs global optimization branch-and-bound algorithms

来源：评论

学校读者我要写书评

暂无评论

optimization of constrained multiple-objective reliability problems using evolutionary algorithms

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2006年第9期91卷 1057-1070页

作者： Salazar, Daniel Rocco, Claudio M. Galvan, Blas J. Univ Las Palmas Gran Canaria Div Computac Evolut & Aplicac CEANI Inst Sistemas Inteligentes & Aplicac Numer Ingn Islas Canarias Spain Cent Univ Venezuela Fac Ingn Caracas Venezuela

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation multiple-objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： constrained optimization MOEA multiple-objective optimization redundancy allocation and reliability optimization

来源：评论

学校读者我要写书评

暂无评论

Performance modeling and optimization of multiple-objective cross-layer design in multi-flow ad-hoc networks

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第3期32卷 e3861-e3861页

作者： Mehta, Ridhima Lobiyal, Daya Krishan Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

In ad-hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end-to-end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple-objective cross-layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy-efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto-optimal solutions plotted in three-dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple-objective cross-layer design scheme, we compare it with the conventional multiple-objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives.

关键词： ad-hoc wireless network delay multiple-objective optimization pareto-optimality residual energy throughput

来源：评论

学校读者我要写书评

暂无评论

Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life

引用

Renewable Energy 2025年 252卷

作者： Kun Yang Mingming Zhang Shanghui Yang Yuwei Song Xinhui Dong Yanfei Deng Xiaowei Deng Department of Civil Engineering The University of Hong Kong Hong Kong China Institute of Green-Low Carbon Energy Innovative Technology Harbin Institute of Technology (Shenzhen) Shenzhen China

Layout optimization is a promising design process that will enhance the power efficiency of wind farms in the design stage. Single optimization targeting maximizing power production for wind farms has been studied for decades. Recently, multiple-objective layout optimization, considering more practical factors, has become urgently required with the rapid growth of wind energy. In this paper, we established a multiple-objective layout optimization framework that considers power performance and turbine fatigue life. Efficient power and fatigue life assessment approaches are adopted. Different scales of wind farms and typical wind roses are investigated. Pareto frontiers are drawn for each case to show the optimal solutions for multiple-objective optimizations. Notably, the deduction of wind speed and increase of turbulence intensity in the wake region have contrasting influences on the fatigue life. The turbines of optimal layouts are distant for higher overall power output but prefer to be closer for longer fatigue lives. Characteristic spacing describing the turbine density and the utilized area is suggested to be larger than 3.65 D for any case and no less than 4.85 D if the wind farm cannot afford the 85% power performance of the theoretical upper limit without wake loss.

关键词： Wind farm layout optimization multiple-objective optimization Fatigue life assessment Energy efficiency Pareto frontier

来源：评论

学校读者我要写书评

暂无评论

Leader identification and leader selection: its effect on a swarm's performance for multi-objective design optimization problems

引用

STRUCTURAL AND MULTIDISCIPLINARY optimization 2004年第2-3期28卷 156-169页

作者： Liew, KM Tan, PK Ray, T Nanyang Technol Univ Nanyang Ctr Supercomp & Visualisat Singapore 639798 Singapore Nanyang Technol Univ Sch Mech & Prod Engn Singapore 639798 Singapore Natl Univ Singapore Singapore MIT Alliance High Performance Computat Engineered Syst Program Singapore 117548 Singapore Natl Univ Singapore Temasek Labs Singapore 117508 Singapore

In this paper, three distinct swarm strategies for the optimization of engineering design problems with multiple objectives are presented. These strategies build upon the swarm algorithm of Ray et al. by incorporating new processes which improve the performance of their predecessor. The constraint-matching strategy calls for the mating of solutions based on constraint satisfaction characteristics. Local search entails the thorough exploration of regions in parametric space where good solutions potentially reside. Migrating leaders prescribes the exchange of information between the best performing members of the swarm. As proof of their utility, the strategies were used to solve two well-studied optimal engineering design problems. Solutions obtained by the strategies are comparable with those of other optimization approaches documented in the literature.

关键词： engineering design multiple-objective optimization swarm strategy

来源：评论

学校读者我要写书评

暂无评论

A Multi-objective optimization Approach for Question Routing in Community Question Answering Services

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2017年第9期29卷 1779-1792页

作者： Cheng, Xiang Zhu, Shuguang Su, Sen Chen, Gang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online, which enables people to help each other by sharing knowledge. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality of CQA services. To address this problem, question routing has been proposed which aims at routing new questions to suitable answerers, who have both high possibility and high ability to answer the questions. In this paper, we formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can simultaneously optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are relatively short and usually attached with tags, we first propose a tagword topic model (TTM) to derive topical representations of questions. Based on TTM, we then develop features for each question-user pair, which are captured at both platform level and thread level. In particular, the platform-level features summarize the information of a user from his/her history posts in the CQA platform, while the thread-level features model the pairwise competitions of a user with others in his/her answered threads. Finally, we extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Extensive experimental results on real-world datasets show that our MLQR can outperform state-of-the-art methods in terms of both answering possibility and answer quality.

关键词： Community question answering services question routing multiple-objective 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：