检索结果-内蒙古大学图书馆

Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design

引用

ENERGY AND BUILDINGS 2015年第Feb.期88卷 135-143页

作者： Yu, Wei Li, Baizhan Jia, Hongyuan Zhang, Ming Wang, Di Chongqing Univ Fac Urban Construct & Environm Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab Three Gorges Reservoir Reg Ecoenvironm Chongqing 400045 Peoples R China Chongqing Univ Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China State Key Lab Bldg Safety & Built Environm Beijing 100015 Peoples R China

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings;Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective genetic algorithm (NSGA-II);Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Building design Energy consumption Thermal comfort Artificial neural network multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期77卷 51-66页

作者： Rohaninejad, Mohamad Kheirkhah, Amirsaman Fattahi, Parviz Vahedi-Nouri, Behdin Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid metaheuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front.

关键词： Scheduling Flexible job shop multi-objective genetic algorithm ELECTRE method Pareto optimal front

来源：评论

学校读者我要写书评

暂无评论

Stochastic quality-cost optimization system hybridizing multi-objective genetic algorithm and quality function deployment

引用

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2015年第4期21卷 407-422页

作者： Lim, Tae-Kyung Jang, Won-Suk Choi, Jae-ho Lee, Dong-Eun Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea Yeungnam Univ Dept Civil Engn Gyeongsa Si 712749 Gyeongsangbuk D South Korea Dong A Univ Dept Civil Engn Pusan 604714 South Korea Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea

This paper introduces an automated tool, the stochastic quality-cost optimization (SQCO) system, that hybridizes multi-objective genetic algorithm (MOGA) and Quality Function Deployment (QFD). The system identifies the optimal trade-off between a construction owner's satisfaction and a contractor's satisfaction. It is important to reconcile the project participants' conflicting interests because the construction owner aims to maximize the quality of construction while the contractor aims to minimize the cost of construction. MOGA is used to optimize resource allocation when owner satisfaction and contractor satisfaction are pursued at the same time under a limited budget. multi-objective optimization is integrated with simulation to effectively deal with the uncertainties of the QFD input and the variability of the QFD output. This study is of value to practitioners because SQCO allows for the establishment of a quality plan that satisfies all of the multi project participants. The study is also of relevance to researchers in that it allows researchers to expeditiously identify an optimal design alternative of construction methods and operations. A test case implemented with a curtain-wall unit verifies the usability and validity of the system in practice.

关键词： quality multi-objective genetic algorithm optimization Quality Function Deployment (QFD) curtain-wall building envelope cost

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization of a STATCOM based on Cascaded multi-Level Converter Topology Using multi-objective genetic algorithm 23

Performance Optimization of a STATCOM based on Cascaded Mult...

引用

23rd Iranian Conference on Electrical Engineering

作者： Tousi, S. M. Reza Aznavi, Sima Bu Ali Sina Univ Fac Engn Dept Elect Engn Hamadan Iran

ISBN: (纸本)9781479919710

In this paper, a comprehensive study in component sizing of a cascaded multi-level inverter based static synchronous compensator (STATCOM) is presented. multi-objective genetic algorithm (MOGA) is utilized to optimize the performance of STATCOM. "Equivalent dynamic capacitance" concept is considered in order to simplify the complex structure of the multi-level inverter based STATCOM. Cascaded multi-level inverter parameters such as DC capacitance, initial capacitor charge, switching angles and also power system parameters including buffer inductance, transformer ratio and transformer inductance are to be found using optimization algorithm. In contrast with previous researches which have tried to optimize the objectives independently, in this paper the aim of using MOGA is optimizing losses, total harmonic distortion (THD) and maximum DC voltage ripple simultaneously, providing designing constraints with respect to the "equivalent dynamic capacitance" concept. The results show that better performance of STATCOM can be achieved via a thorough analysis by considering all parameters of STATCOM and components of power system as decision variables of a multi-objective optimization problem.

关键词： Static Synchronous Compensator (STATCOM) Cascaded multi-Level Converter Performance Optimization multi-objective genetic algorithm Equivalent Dynamic Capacitance

来源：评论

学校读者我要写书评

暂无评论

multi-objective genetic algorithm for voltage stability enhancement using rescheduling and FACTS devices

引用

AIN SHAMS ENGINEERING JOURNAL 2014年第3期5卷 789-801页

作者： Roselyn, J. Preetha Devaraj, D. Dash, Subhransu Sekhar SRM Univ Chennai Tamil Nadu India Kalasalingam Univ Srivilliputhur India

This paper presents the application of multi-objective genetic algorithm to solve the Voltage Stability Constrained Optimal Power Flow (VSCOPF) problem. Two different control strategies are proposed to improve voltage stability of the system under different operating conditions. The first approach is based on the corrective control in contingency state with minimization of voltage stability index and real power control variable adjustments as objectives. The second approach involves optimal placement and sizing of multi-type FACTS devices, Static VAR Compensator and Thyristor Controlled Series Capacitor along with generator rescheduling for minimization of voltage stability index and investment cost of FACTS devices. A fuzzy based approach is employed to get the best compromise solution from the trade off curve to aid the decision maker. The effectiveness of the proposed VSCOPF problem is demonstrated on two typical systems, IEEE 30-bus and IEEE 57 bus test systems. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.

关键词： Generation rescheduling Voltage stability multi-objective genetic algorithm Fuzzy decision making FACTS

来源：评论

学校读者我要写书评

暂无评论

Frequency Assignment for Aviation Navigation Stations Based on an Improved multi-objective genetic Local Search algorithm

引用

AEROSPACE 2025年第5期12卷 447-447页

作者： Hao, Boyang Xu, Yajun Gong, Ke Gao, Tianlu Gui, Yiling Liu, Minghui Zhang, Qiang Civil Aviat Flight Univ China Coll Air Traff Management Deyang 618307 Peoples R China

With the rapid development of both commercial and general aviation, the frequency assignment problem for aviation navigation stations has become increasingly important. This paper presents a general algorithm for frequency assignment at individual aviation navigation stations. Subsequently, a frequency assignment model for multiple civil aviation navigation stations is established to address large-scale frequency allocation challenges. To overcome the limitations of traditional multi-objective genetic algorithms, such as slow convergence speed and susceptibility to local optima, this study proposes several improved algorithms, including the multi-objective genetic algorithm with randomly assigned weights, the multi-objective genetic local search algorithm, and an improved multi-objective genetic local search algorithm, while optimizing key algorithm parameters. The problem involves multiple objectives, including minimizing interference in frequency assignment and reducing the total number of assigned frequencies. Experimental results demonstrate that the proposed improved multi-objective genetic algorithms-especially IMOGLSA-II-effectively address the frequency assignment problem for aviation navigation stations, achieving notable improvements in solution quality, convergence speed, and stability compared with other multi-objective genetic algorithms. In particular, although the time complexity of the proposed algorithm is slightly higher due to the incorporation of local search mechanisms, it exhibits clear advantages in reducing parameter sensitivity, simplifying algorithm structure, and enhancing engineering applicability. These characteristics make the proposed method not only well-suited to the static and constrained nature of aviation frequency assignment, but also more practical and effective than other mainstream multi-objective optimization algorithms in similar engineering scenarios. Furthermore, the proposed method offers a reliable approach that can be e

关键词： multi-objective genetic algorithm multi-objective optimization aviation navigation station frequency assignment adaptive genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Machine learning and genetic algorithm as tools for single and multi-objective shape optimization of micromixers with Cantor fractal structure

引用

BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第4期41卷 1327-1344页

作者： Cao, Jingzhi Chen, Xueye Ludong Univ Sch Chem & Mat Sci 186 Middle Hongqi Rd Yantai 264025 Shandong Peoples R China Ludong Univ Coll Transportat Yantai 264025 Shandong Peoples R China

Shape optimization of micromixers with Cantor fractal structures on the top and bottom walls has been conducted using a combination of three-dimensional Navier-Stokes analysis, Machine Learning (ML), and multi-objective genetic algorithm (MOGA) for both single and multi-objective optimization objectives. Numerical analysis has been systematically conducted employing two distinct working fluids, namely water and ethanol, while operating at a Reynolds number (Re) of unity (Re = 1). The optimization endeavor has been executed by manipulating two design variables that pertain to dimensionless geometric parameters. The single-objective optimization is centered on the selection of mixing effectiveness as the primary objective function, whereas the multi-objective optimization entails the consideration of two objective functions, specifically, the pressure drop and the mixing index at the exit. The Latin hypercube sampling (LHS) method is employed as an experimental design technique to systematically explore the parameter space within the design domain. It is utilized for the purpose of strategically selecting design points within this domain. Additionally, surrogate models for the objective functions are established through the application of ML. In the context of single-objective optimization, the Sequential Linear Programming (SLP) method is employed to iteratively derive the optimal objective function. Meanwhile, for multi-objective optimization, the genetic algorithm (GA) is applied to delineate the Pareto optimal frontier (POF) of the micromixer. Subsequently, K-means clustering is utilized as a technique for the purpose of classifying the optimized outcomes, followed by the selection of representative points within the solution space. In the context of single-objective optimization, it is observed that the micromixer featuring a Cantor fractal structure exhibits a noteworthy enhancement in mixing efficiency, surpassing that of the reference design (RD) by a substant

关键词： multi-objective genetic algorithm Machine learning Pareto optimal front Single and multi-objective shape optimization Cantor fractal structure

来源：评论

学校读者我要写书评

暂无评论

Augmented genetic algorithm v3 for multi-objective PDN Decap Optimization

Augmented Genetic Algorithm v3 for Multi-Objective PDN Decap...

引用

Joint International Symposium on Electromagnetic Compatibility, Signal & Power Integrity: EMC Japan/AsiaPacific International Symposium on Electromagnetic Compatibility (EMC/APEMC)

作者： Manoharan, Haran Juang, Jack Wang, Hanfeng Pan, Jingnan Gao, Xu Qiu, Kelvin Hwang, Chulsoon Missouri Univ Sci & Technol EMC Lab Rolla MO 65409 USA Google Inc Mountain View CA USA

ISBN: (纸本)9784885523472;9798350349498

This paper proposes a novel approach for generating the initial population, termed disproportionate initialization, to enhance the genetic algorithm's efficiency in optimizing the number of decaps. This method is specifically designed to accelerate convergence, improving the algorithm's ability to find optimal solutions. The algorithm is improved to simultaneously reduce the number of decoupling capacitors and achieve the lowest cost, aiming to identify the global minima for comprehensive PDN optimization.

关键词： multi-objective genetic algorithm disproportionate initial population population diversity decap optimization

来源：评论

学校读者我要写书评

暂无评论

Thermal-hydraulic characteristics and optimization of coiled-tube resistance furnace based on response surface method and multi-objective genetic algorithms

引用

CASE STUDIES IN THERMAL ENGINEERING 2025年 72卷

作者： Zhang, Ruochen Wu, Yinglong Xiao, Juan Wang, Simin Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China

The electrification transformation of the petrochemical industry is imminent, driven by the imperative to reduce carbon emissions. This paper presents a coiled-tube resistance furnace and investigates the flow and heat transfer performance of heavy residual oil within the tube through numerical simulation methods. The furnace's coiled-tube structure was optimized using a multi-objective genetic algorithm (MOGA) combined with a genetic aggregation response surface model, with inlet velocity, coil diameter, pitch, and tube diameter as design variables. The numerical model was experimentally validated and demonstrated the reliability of the simulation approach. Sensitivity analysis identified inlet velocity and coil diameter as the most influential parameters. Consequently, the optimal structural parameters were determined as a coil diameter of 200 mm, a pitch of 60 mm, and a tube diameter of 30 mm. Compared to the original structure, the optimized design led to a 63.7 % increase in the average heat transfer coefficient and a 44.6 % reduction in the average pressure drop. Additionally, a new correlation formula was developed by incorporating the Dean number, coil curvature, and dimensionless pitch as correction factors. The formula shows excellent fitting accuracy and robustness, providing a theoretical basis for the design of coiled tube resistance furnaces.

关键词： Coiled-tube Resistance furnace Heat transfer enhancement multi-objective genetic algorithm Fitting correlations

来源：评论

学校读者我要写书评

暂无评论

Optimization of Combined Scroll Profile Based on multi-objective genetic algorithm

Optimization of Combined Scroll Profile Based on Multi-objec...

引用

International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Tao Liu Fuyong Hou Lanzhou University of Technology

This In the design of scroll compressor,in order to improve the performance of the combined profile by involute of circle and high order curve,the mathematical model was established which takes the stroke volume and area utilization coefficient as the objective *** optimize it by the MATLAB genetic algorithm toolbox and have a contrast of the original parameters with the optimized *** optimal result shows that the result after optimization has higher stroke volume and area utilization coefficient.

关键词： combined profile stroke volume area utilization coefficient multi-objective genetic 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

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

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：