检索结果-内蒙古大学图书馆

evolutionary many-objective optimization for retrofit planning in public buildings: A comparative study

JOURNAL OF CLEANER PRODUCTION 2018年 190卷 403-410页

作者： Son, Hyojoo Kim, Changwan Chung Aug Univ Dept Architectural Engn Seoul 06974 South Korea

There has been an increasing movement toward retrofitting existing (in-use) buildings to achieve a significant reduction in energy consumption and greenhouse gas emissions in the building sector. When planning retrofits for public buildings, decision-makers are required to make rational decisions that will achieve four critical objectives: minimize energy consumption, reduce CO2 emissions, mitigate retrofit costs, and maximize thermal comfort. This study aims to solve this four-objective optimization problem (so-called the problem of many-objective optimization) for retrofit planning in public buildings via an evolutionary many-objective optimization (EO) algorithm that handles these objectives at the same time. This study involves the application of EO algorithms (NSGA-II, MOPSO, MOEA/D, and NSGA-III) and the evaluation of their performance. A description of these algorithms is presented, and each algorithm is implemented in a public-building retrofit project. The algorithms' performances were analyzed, and the results were compared based on two aspects: diversity and convergence. The results indicated that NSGA-III can be used to derive a comprehensive set of trade-off alternatives from possible retrofit scenarios, thereby serving as a useful reference for retrofit planners. These decision-makers can then utilize the provided references to select optimal retrofit strategies and satisfy stakeholders. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Energy-efficient retrofit CO2 emissions Energy consumption evolutionary many-objective optimization Public buildings

来源：评论

学校读者我要写书评

暂无评论

evolutionary many-objective optimization by NSGA-II and MOEA/D with Large Populations

Evolutionary Many-Objective Optimization by NSGA-II and MOEA...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ishibuchi, Hisao Sakane, Yuji Tsukamoto, Noritaka Nojima, Yusuke Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998534 Japan

ISBN: (纸本)9781424427932

evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximates the entire Pareto front of a multiobjective optimization problem. Whereas EMO algorithms usually work well on two-objective and three-objective problems, their search ability is degraded by the increase in the number of objectives. One difficulty in the handling of many-objective problems is the exponential increase in the number of non-dominated solutions necessary for approximating the entire Pareto front. A simple countermeasure to this difficulty is to use large populations in EMO algorithms. In this paper, we examine the behavior of EMO algorithms with large populations (e.g., with 10,000 individuals) through computational experiments on multiobjective and many-objective knapsack problems with two, four, six, eight and ten objectives. We examine two totally different algorithms: NSGA-II and MOEA/D. NSGA-II is a Pareto dominance-based algorithm while MOEA/D uses scalarizing functions. Their search ability is examined for various specifications of the population size under the fixed computation load. That is, we use the total number of examined solutions as the stopping condition of each algorithm. Thus the use of a very large population leads to the termination at an early generation (e.g., 20th generation). It is demonstrated through computational experiments that the use of too large populations makes NSGA-II very slow and inefficient. On the other hand, MOEA/D works well even when it is executed with a very large population. We also discuss why MOEA/D works well even when the population size is unusually large.

关键词： evolutionary multiobjective optimization (EMO) evolutionary many-objective optimization NSGA-II MOEA/D cellular genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Preference-Based evolutionary many-objective optimization for Agile Satellite Mission Planning

引用

IEEE ACCESS 2018年 6卷 40963-40978页

作者： Li, Longmei Chen, Hao Li, Jun Jing, Ning Emmerich, Michael Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

With the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, and observation timeliness. In this paper, a five-objective mixedinteger optimization problem is formulated for agile satellite mission planning. Preference-based multiobjective evolutionary algorithms, i.e., T-MOEA/D-TCH, T-MOEA/D-PBI, and T-NSGA-III are applied to solve the problem. Problem-specific coding and decoding approaches are proposed based on heuristic rules. Experiments have shown the advantage of integrating preferences in many-objective satellite mission planning. A comparative study is conducted with other state-of-the-art preference-based methods (T-NSGA-II, T-RVEA, and MOEA/D-c). Results have demonstrated that the proposed T-MOEA/D-TCH has the best performance with regard to IGD and elapsed runtime. An interactive framework is also proposed for the decision maker to adjust preferences during the search. We have exemplified that a more satisfactory solution could be gained through the interactive approach.

关键词： Preferences evolutionary many-objective optimization EOS mission planning target region MOEA/D

来源：评论

学校读者我要写书评

暂无评论

Coordinated Adaptation of Reference Vectors and Scalarizing Functions in evolutionary many-objective optimization

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第2期53卷 763-775页

作者： Liu, Qiqi Jin, Yaochu Heiderich, Martin Rodemann, Tobias Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Bielefeld Univ Fac Technol D-33619 Bielefeld Germany Univ Surrey Dept Comp Sci Guildforld GU2 7XH England Honda R&D Europe Deutschland GmbH Dept Adv Vehicle Technol Res D-63073 Offenbach Germany Honda Res Inst Europe Dept Optimizat & Creat D-63073 Offenbach Germany

It is highly desirable to adapt the reference vectors to unknown Pareto fronts (PFs) in decomposition-based evolutionary many-objective optimization. While adapting the reference vectors enhances the diversity of the achieved solutions, it often decelerates the convergence performance. To address this dilemma, we propose to adapt the reference vectors and the scalarizing functions in a coordinated way. On the one hand, the adaptation of the reference vectors is based on a local angle threshold, making the adaptation better tuned to the distribution of the solutions. On the other hand, the weights of the scalarizing functions are adjusted according to the local angle thresholds and the reference vectors' age, which is calculated by counting the number of generations in which one reference vector has at least one solution assigned to it. Such coordinated adaptation enables the algorithm to achieve a better balance between diversity and convergence, regardless of the shape of the PFs. Experimental studies on MaF, DTLZ, and DPF test suites demonstrate the effectiveness of the proposed algorithm in solving problems with both regular and irregular PFs.

关键词： Convergence Shape optimization Statistics Sociology Stars Solids evolutionary many-objective optimization irregular Pareto fronts (PFs) reference vector scalarizing function

来源：评论

学校读者我要写书评

暂无评论

Adaptation of Reference Vectors for evolutionary many-objective optimization of Problems with Irregular Pareto Fronts

Adaptation of Reference Vectors for Evolutionary Many-object...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Liu, Qiqi Jin, Yaochu Heiderich, Martin Rodemann, Tobias Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Honda R&D Europe Deutschland GmbH Carl Legien Str 30 D-63073 Offenbach Germany Honda Res Inst Europe Carl Legien Str 30 D-63073 Offenbach Germany

ISBN: (纸本)9781728121536

For problems with irregular Pareto fronts, only part of the objective space is covered by optimal solutions. Most decomposition based evolutionary many-objective algorithms, however, predefine uniformly distributed weight or reference vectors, making them less suited for problems with irregular Pareto fronts, since many weight or reference vectors will be wasted. To address the above issue, this paper proposes a variant of the reference vector guided evolutionary algorithm by adjusting reference vectors according to the distribution of the solutions in the current population to make sure that most reference vectors are associated with solutions. A secondary selection criterion based on the dominance relationship is adopted in addition to the angle penalized distance based selection so that a sufficient number of solutions can survive and be passed to the next generation. Experiments on 12 irregular test problems with 60 instances show that the proposed algorithm is competitive compared to the state-of-the-art algorithms for solving problems with irregular Pareto fronts.

关键词： evolutionary many-objective optimization reference vector irregular Pareto front

来源：评论

学校读者我要写书评

暂无评论

Distribution network management under electricity deregulation using evolutionary many-objective optimization

引用

JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2018年第1-2期25卷 17-30页

作者： Sekizaki, Shinya Nishizaki, Ichiro Hayashida, Tomohiro Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima Japan

A distribution network operator can reconfigure the network topology by operating section switches to improve multiple objective functions, that is, the line losses and the lifespan of transformers. In the context of long term network management, the impact of future electricity deregulation should be taken into account. Because the related literature has not addressed the above problems, this paper presents a multicriteria optimization approach for efficient distribution network management under electricity market deregulation. The main contributions of this paper can be summarized as follows: (a) the time-variable (time dependent) and nonconvex issues for the distribution network management are formulated such that the time series variations of the line losses and the transformers mechanical operations are modelled, (b) an electricity consumption model under the deregulation, in which consumers flexibly respond to electricity prices, is taken into account, and (c) from the practical viewpoint, the proposed evolutionary many-objective optimization approach is applied to find approximated Pareto optimal solutions of the nonconvex large-scale problem within practical operating time. It can be seen from the computational experiment that the proposed method enables the network operator to easily identify a better network topology, which adequately meets the multiple requirements of the operator.

关键词： distribution network management electricity market deregulation energy management evolutionary many-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Comparing Solution Sets of Different Size in evolutionary many-objective optimization

Comparing Solution Sets of Different Size in Evolutionary Ma...

引用

IEEE Congress on evolutionary Computation (CEC)

作者： Ishibuchi, Hisao Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479974924

When the performance of different evolutionary multiobjective optimization (EMO) algorithms is compared, the same population size is usually used for all EMO algorithms in computer simulations. This setting is to obtain a solution set of the same size from a different algorithm. However, in general, each algorithm may have its own best parameter specifications for each test problem. Thus, it may be difficult to appropriately specify the same population size for all algorithms for their fair comparison. A different algorithm may be evaluated as being the best for a different specification of the population size. An alternative setting is to allow each algorithm to use its own best population size. In this setting, a solution set of different size is obtained from each algorithm. It may be difficult to perform fair comparison using solution sets of different size. In this paper, we discuss the difficulty in comparing EMO algorithms under these two settings of the population size: the same specification for all algorithms and a different specification for each algorithm. First, we discuss the effect of the number of non-dominated solutions on some performance indicators. Next we show the difficulty in the first setting: Performance of each algorithm depends on the population size. Then we discuss the difficulty in the second setting: The size of a solution set obtained by each algorithm is not the same. In this setting, we examine the use of solution selection as a post-processing procedure to choose the same number of solutions from each solution set of different size. The selected solutions are used for performance comparison.

关键词： evolutionary many-objective optimization solution selection multiobjective knapsack problem performance indicator

来源：评论

学校读者我要写书评

暂无评论

Performance limits of the single screw expander in organic Rankine cycle with ensemble learning and hyperdimensional evolutionary many-objective optimization algorithm intervention

引用

ENERGY 2022年第0期245卷 123254-123254页

作者： Ping, Xu Yang, Fubin Zhang, Hongguang Wang, Yan Lei, Biao Wu, Yuting Beijing Univ Technol Fac Environm & Life Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers Beijing 100124 Peoples R China

The operating performance of the expander, as the core component of the ORC system, directly determines the comprehensive performance of the system. Single screw expander (SSE) has received widespread attention due to its advantages such as large single-stage expansion pressure ratio, insensitivity to liquid, wide working range, and balanced forces. This study fully considers the strong coupling, high nonlinearity, large time variability and uncertainty between SSE performance evaluation indexes and operating parameters. On this basis, a scatter-bilinear interpolation hybrid feature selection approach is proposed, and the feature selection and analysis of the correlation degree among SSE performance evaluation indexes are conducted. In the high-dimensional space, the influence of operating parameters on different performance evaluation indexes is analyzed. Then, this study proposes the ensemble learning-driven non-dominated sorting genetic algorithm-III (NSGA-III) approach. An ensemble learning-driven NSGA-III model for SSE performance limit optimization is constructed on the basis of the scatter-bilinear interpolation hybrid feature selection method and the ensemble learningdriven NSGA-III approach. In the hyperdimensional space, the performance limits of SSE are obtained. When the comprehensive performance of SSE reaches its limit, the shaft efficiency, expansion pressure ratio, and power output are 53.71%, 7.76, and 10.83 kW, respectively. This research provides a reliable reference for analyzing, designing, and optimizing the SSE performance limits, and it also provides a direct guidance for obtaining the SSE performance limits in the ORC system. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Organic rankine cycle Single screw expander Performance limits Ensemble learning evolutionary many-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Thermodynamic analysis and high-dimensional evolutionary many-objective optimization of dual loop organic Rankine cycle (DORC) for CNG engine waste heat recovery

引用

ENERGY 2021年 236卷 121508-121508页

作者： Ping, Xu Yao, Baofeng Zhang, Hongguang Yang, Fubin Beijing Univ Technol Fac Environm & Life Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers Beijing 100124 Peoples R China

The complex and changeable working conditions of compressed natural gas (CNG) engines have brought great challenges to the efficient recovery of waste heat energy. The dual loop organic Rankine cycle (DORC) system can effectively recover and utilize CNG engine waste heat due to its structural advantages. Determining the nonlinear variations between operating parameters and thermodynamic performance serves as the basis for obtaining the thermodynamic performance limits of the DORC system. However, traditional analysis methods have obvious limitations in this area. Based on the bilinear interpolation algorithm, this paper comprehensively analyzes and evaluates the nonlinear and strong coupling characteristics between operating parameters and net power output, thermal efficiency, and exergy destruction. In addition, the comprehensive thermodynamic performance limits of the DORC system have a critical effect on its engineering application yet have not been thoroughly and comprehensively optimized. Therefore, this paper proposes an equilibrium-based thermodynamic high-dimensional evolutionary many-objective optimization (EMO) method for the DORC system. The optimal thermal efficiency, net power output, and total exergy destruction of the DORC system reached 37.11 kW, 14.27%, and 104.58 kW, respectively. Based on the optimization results under equilibrium and unequilibrium weight, the dominant relationship between the different thermodynamic performances of the DORC system is then analyzed. This research can provide a direct reference for analyzing and optimizing the comprehensive thermodynamic performance of the DORC system. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： CNG engine Dual loop organic Rankine cycle Thermodynamic analysis evolutionary many-objective optimization NSGA-III

来源：评论

学校读者我要写书评

暂无评论

Adaptive Control of Dominance Area of Solutions in evolutionary many-objective optimization

引用

NEW MATHEMATICS AND NATURAL COMPUTATION 2015年第2期11卷 135-150页

作者： Tomita, Kouhei Miyakawa, Minami Sato, Hiroyuki Univ Electrocommun Grad Sch Informat & Engn 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

Controlling the dominance area of solutions (CDAS) relaxes the concept of Pareto dominance with an user- defined parameter S. CDAS with S < 0: 5 expands the dominance area and improves the search performance of multi-objective evolutionary algorithms (MOEAs) especially in many-objective optimization problems (MaOPs) by enhancing convergence of solutions toward the optimal Pareto front. However, there is a problem that CDAS with an expanded dominance area (S < 0: 5) generally cannot approximate entire Pareto front. To overcome this problem we propose an adaptive CDAS (A-CDAS) that adaptively controls the dominance area of solutions during the solutions search. Our method improves the search performance in MaOPs by approximating the entire Pareto front while keeping high convergence. In early generations, A-CDAS tries to converge solutions toward the optimal Pareto front by using an expanded dominance area with S < 0: 5. When we detect convergence of solutions, we gradually increase S and contract the dominance area of solutions to obtain Pareto optimal solutions (POS) covering the entire optimal Pareto front. We verify the effectiveness and the search performance of the proposed A-CDAS on concave and convex DTLZ3 benchmark problems with 2-8 objectives, and show that the proposed A-CDAS achieves higher search performance than conventional nondominated sorting genetic algorithm II (NSGA-II) and CDAS with an expanded dominance area.

关键词： evolutionary many-objective optimization adaptive control of dominance area of solutions many-objective real parameter 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：