检索结果-内蒙古大学图书馆

evolutionary multi-objective optimization: some current research trends and topics that remain to be explored

中国高等学校学术文摘·计算机科学 2009年第1期3卷 18-30页

作者： Carlos A. COELLO COELLO Evolutionary Computation Group Departamento de Computcai(o)n Ginvestav-IPN México D. F. 07300 México UMI-LAFMIA 3175 CNRS México D. F. 07300 México

This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which,from the author's perspective, constitute good potential areas for future research, namely, constraint-handling techniques,incorporation of user's preferences and parameter control,This information is expected to be useful for those interested in pursuing research in this area.

关键词： evolutionary multi-objective optimization evolutionary algorithms multi-objective optimization metaheuristics

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective optimization for mesh simplification of 3D open models

引用

INTEGRATED COMPUTER-AIDED ENGINEERING 2013年第4期20卷 375-390页

作者： Rosario Campomanes-Alvarez, B. Cordon, Oscar Damas, Sergio European Ctr Soft Comp Mieres 33600 Asturias Spain Univ Granada Dept Comp Sci & Artif Intelligence Granada Spain Univ Granada Res Ctr Informat & Commun Technol CITIC UGR Granada Spain

Polygonal surface models are typically used in three dimensional (3D) visualizations and simulations. They are obtained by laser scanners, computer vision systems or medical imaging devices to model highly detailed object surfaces. Surface mesh simplification aims to reduce the number of faces used in a 3D model while keeping the overall shape, boundaries and volume. In this work, we propose to deal with the 3D open model mesh simplification problem from an evolutionary multi-objective viewpoint. The quality of a solution is defined by two conflicting objectives: the accuracy and the simplicity of the model. We adapted the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the multi-objective evolutionary Algorithm Based on Decomposition (MOEA/D) to tackle the problem. We compare their performance with two classic approaches and two single-objective implementations. The comparison has been carried out using six different datasets from six corresponding real-world objects. Experimental results have demonstrated that NSGA-II and MOEA/D performs similarly and obtain the best solutions for the studied problem.

关键词： 3D modeling mesh simplification evolutionary multi-objective optimization NSGA-II MOEA/D

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective optimization for landscape system design

引用

JOURNAL OF GEOGRAPHICAL SYSTEMS 2011年第3期13卷 299-326页

作者： Roberts, S. A. Hall, G. B. Calamai, P. H. Wilfrid Laurier Univ Dept Geog & Environm Studies Waterloo ON N2L 3C5 Canada Univ Otago Sch Surveying Dunedin New Zealand Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to analyze qualitatively and quantitatively regional landscape structures in land-use planning and environmental policy implementation. This paper introduces an evolutionary multi-objective optimization (EMO) methodology to estimate the Pareto optimal set of landscape designs generated from a series of underlying ecological principles. The results of applying these principles via EMO to a study site are presented and a hierarchical clustering methodology is introduced to assist in evaluating the population of solutions generated.

关键词： evolutionary multi-objective optimization Landscape design

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective optimization in Building Retrofit Planning Problem

Evolutionary Multi-objective Optimization in Building Retrof...

引用

International Conference on Sustainable Design, Engineering and Construction (ICSDEC)

作者： Son, Hyojoo Kim, Changwan Chung Ang Univ Dept Architectural Engn 84 Heukseok Ro Seoul South Korea

Energy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption;(2) minimize CO2 emissions;(3) minimize retrofit costs;and (4) maximize thermal comfort;and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto-optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders' preferences. (C) 2016 Published by Elsevier Ltd.

关键词： Building retrofit CO2 emissions Energy consumption evolutionary multi-objective optimization Retrofit costs Thermal comfort

来源：评论

学校读者我要写书评

暂无评论

Interleaving Guidance in evolutionary multi-objective optimization

引用

Journal of Computer Science & Technology 2008年第1期23卷 44-63页

作者： Lam Thu Bui Kalyanmoy Deb Hussein A.Abbass Daryl Essam The Artificial Life and Adaptive Robotics Laboratory School of ITEEADFAUniversity of New South Wales Canberra Mechanical Engineering Department Indian Institute of TechnologyKanpur

In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres （local models） in the decision space. These spheres axe usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Paxeto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Paxeto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

关键词： evolutionary multi-objective optimization guided dominance local models

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective optimization Applied to Industrial Refrigeration Systems for Energy Efficiency

引用

ENERGIES 2022年第15期15卷 5575页

作者： Nedjah, Nadia Mourelle, Luiza de Macedo Dantas Lizarazu, Marcelo Silveira Univ Estado Rio De Janeiro Dept Elect Engn & Telecommun BR-20550900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Syst Engn & Computat BR-20550900 Rio De Janeiro Brazil

Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to provide the best trade-offs between two conflicting objectives: maximization of the effectiveness of the cooling towers and minimization of the overall power requirement of the refrigeration system. The optimization process respects the constraints to guarantee the correct and safe operation of the equipment when the evolved solution is implemented. In this work, we apply three evolutionary multi-objective algorithms: Non-dominated Sorting Genetic Algorithm (NSGA-II), Micro-Genetic Algorithm (Micro-GA) and Strength Pareto evolutionary Algorithm (SPEA2). The results obtained are analyzed under different scenarios and models of the cooling system's equipment, allowing for the selection of the best algorithm and best equipment's model to achieve energy efficiency of the studied refrigeration system.

关键词： energy efficiency cooling towers chillers evolutionary multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

A Secure Federated Data-Driven evolutionary multi-objective optimization Algorithm

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 191-205页

作者： Liu, Qiqi Yan, Yuping Ligeti, Peter Jin, Yaochu Westlake Univ Sch Engn Trustworthy & Gen AI Lab Hangzhou 310024 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany Eotvos Lorand Univ Fac Informat Dept Computeralgebra H-1053 Budapest Hungary E Grp ICT Software Zrt Smart Data Grp H-1027 Budapest Hungary Univ Surrey Dept Comp Sci Guildford GU2 7XH England

Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this article proposes a secure federated data-driven evolutionary multi-objective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellman-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization.

关键词： evolutionary computation evolutionary multi-objective optimization Diffie-Hellman privacy-preserving federated Bayesian optimization data-driven evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期233卷 674-688页

作者： Sinha, Ankur Korhonen, Pekka Wallenius, Jyrki Deb, Kalyanmoy Aalto Univ Sch Business Dept Informat & Serv Econ Helsinki 00076 Finland Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48823 USA

This paper presents a preference-based method to handle optimization problems with multiple objectives. With an increase in the number of objectives the computational cost in solving a multi-objective optimization problem rises exponentially, and it becomes increasingly difficult for evolutionary multi-objective techniques to produce the entire Pareto-optimal front. In this paper, an evolutionary multi-objective procedure is combined with preference information from the decision maker during the intermediate stages of the algorithm leading to the most preferred point. The proposed approach is different from the existing approaches, as it tries to find the most preferred point with a limited budget of decision maker calls. In this paper, we incorporate the idea into a progressively interactive technique based on polyhedral cones. The idea is also tested on another progressively interactive approach based on value functions. Results are provided on two to five-objective unconstrained as well as constrained test problems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： evolutionary multi-objective optimization multiple criteria decision-making Interactive multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Simultaneous feature selection and weighting - An evolutionary multi-objective optimization approach

引用

PATTERN RECOGNITION LETTERS 2015年第Nov.1期65卷 51-59页

作者： Paul, Sujoy Das, Swagatam Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Selection of feature subset is a preprocessing step in computational learning, and it serves several purposes like reducing the dimensionality of a dataset, decreasing the computational time required for classification and enhancing the classification accuracy of a classifier by removing redundant and misleading or erroneous features. This paper presents a new feature selection and weighting method aided with the decomposition based evolutionary multi-objective algorithm called MOEA/D. The feature vectors are selected and weighted or scaled simultaneously to project the data points to such a hyper space, where the distance between data points of non-identical classes is increased, thus, making them easier to classify. The inter-class and intraclass distances are simultaneously optimized by using MOEA/D to obtain the optimal features and the scaling factor associated with them. Finally, k-NN (k-Nearest Neighbor) is used to classify the data points having the reduced and weighted feature set. The proposed algorithm is tested with several practical datasets from the well-known data repositories like UCI and LIBSVM. The results are compared with those obtained with the state-of-the-art algorithms to demonstrate the superiority of the proposed algorithm. (C) 2015 Published by Elsevier B.V.

关键词： Feature selection Feature weighting evolutionary multi-objective optimization MOEA/D Inter- and intra-class distances

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective optimization in Building Retrofit Planning Problem

引用

Procedia Engineering 2016年 145卷 565-570页

作者： Hyojoo Son Changwan Kim Department of Architectural Engineering Chung-Ang University 84 Heukseok-ro Dongjak-gu Seoul Korea

Energy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption; (2) minimize CO 2 emissions; (3) minimize retrofit costs; and (4) maximize thermal comfort; and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto -optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders’ preferences.

关键词： Building retrofit CO 2 emissions Energy consumption evolutionary multi-objective optimization Retrofit costs Thermal comfort

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：