检索结果-内蒙古大学图书馆

Many-objective de Novo water supply portfolio planning under deep uncertainty

ENVIRONMENTAL MODELLING & SOFTWARE 2012年 34卷 87-104页

作者： Kasprzyk, Joseph R. Reed, Patrick M. Characklis, Gregory W. Kirsch, Brian R. Penn State Univ Civil & Environm Engn University Pk PA 16802 USA

This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol' variance decomposition with multiobjective evolutionary algorithms (MOEAs) to generate planning alternatives and test their robustness to new modeling assumptions and scenarios. We explore these issues within the context of a risk-based water supply management problem, where a city seeks the most efficient use of a water market. The case study examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, using a suite of 6-objective problem formulations that have increasing decision complexity for both a 10-year planning horizon and an extreme single-year drought scenario. The de Novo planning framework demonstrated illustrates how to adaptively improve the value and robustness of our problem formulations by evolving our definition of optimality while discovering key tradeoffs. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multiobjective evolutionary algorithms Many-objective decision analytics Robust decision making Risk Uncertainty Decision support Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Maintain Optimal Configurations for Large Configurable Systems Using Multi-Objective Optimization

引用

Computers, Materials & Continua 2022年第11期73卷 4407-4422页

作者： Muhammad Abid Jamil Deafallah Alsadie Mohamed K.Nour Normi Sham Awang Abu Bakar Department of Computer Science Umm Al-Qura UniversityMakkahSaudi Arabia Kulliyyah of Information and Communication Technology International Islamic UniversityMalaysia

To improve the maintenance and quality of software product lines,efficient configurations techniques have been ***,due to the complexity of derived and configured products in a product line,the configuration process of the software product line(SPL)becomes timeconsuming and *** product line consists of a various number of feature models that need to be *** different approaches have been presented by Search-based software engineering(SBSE)to resolve the software engineering issues into computational solutions using some metaheuristic ***,multiobjective evolutionary algorithms help to optimize the configuration process of *** this paper,different multi-objective evolutionary algorithms like Non-Dominated Sorting Genetic algorithms II(NSGA-II)and NSGA-III and Indicator based evolutionary Algorithm(IBEA)are applied to different feature models to generate optimal results for large *** proposed approach is also used to generate the optimized test suites with the help of different multi-objective evolutionary algorithms(MOEAs).

关键词： Software product line search-based software engineering metaheuristic multiobjective evolutionary algorithms feature model

来源：评论

学校读者我要写书评

暂无评论

Time-stamped resampling for robust evolutionary portfolio optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10722-10730页

作者： Garcia, Sandra Quintana, David Galvan, Ines M. Isasi, Pedro Univ Carlos III Madrid Dept Comp Sci Leganes 28911 Spain

Traditional mean-variance financial portfolio optimization is based on two sets of parameters, estimates for the asset returns and the variance-covariance matrix. The allocations resulting from both traditional methods and heuristics are very dependent on these values. Given the unreliability of these forecasts, the expected risk and return for the portfolios in the efficient frontier often differ from the expected ones. In this work we present a resampling method based on time-stamping to control the problem. The approach, which is compatible with different evolutionary multiobjective algorithms, is tested with four different alternatives. We also introduce new metrics to assess the reliability of forecast efficient frontiers. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Financial portfolio optimization Robust portfolio multiobjective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

An Agent-Based System for the Design of New Products Using a Fuzzy Multicriteria Approach

引用

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第8期22卷 2691-2707页

作者： Figueroa-Perez, J. Francisco Leyva-Lopez, Juan C. Perez-Contreras, Edgar O. Sanchez, Pedro J. Ramirez-Noriega, Alan D. Univ Autonoma Occidente Blvd Lola Beltran & Blvd Rolando Arjona S-N Culiacan 80120 Sinaloa Mexico Univ Autonoma Sinaloa Fuente Poseidon & Angel Flores S-N Los Mochis 81223 Sinaloa Mexico Univ Jaen Campus Las Lagunillas S-N Jaen 23071 Spain

The intense competition of global markets stimulates a significant change in the way products are designed, manufactured, and delivered. Such a situation is forcing companies to consider the use of new tools to support this decision process. This paper describes an agent-based system implementing a novel consumer-based fuzzy multicriteria methodology to support the design of new products. It argues that a combination of marketing decision support systems, multicriteria and multiobjective methodologies, fuzzy models, and agent technologies could be a valuable tool to assist marketing managers in new product design applications. In the multi-agent system architecture, software agents were classified into types and organized in teams. The first includes interface, task, and information agents. The second reflects Simon's decision-making process, including intelligence, design, and choice teams. The communication between agents is carried out using an ontology. An example of system operation attempting to get the design of new corn oil is presented using sequence diagrams.

关键词： Marketing Decision support systems Agent technology New product design multiobjective evolutionary algorithms Multicriteria decision analysis

来源：评论

学校读者我要写书评

暂无评论

Incorporating expert knowledge into evolutionary algorithms with operators and constraints to design satellite systems

引用

APPLIED SOFT COMPUTING 2018年 66卷 330-345页

作者： Hitomi, Nozomi Selva, Daniel Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

Knowledge-intensive evolutionary algorithms (EA) and knowledge-driven optimization algorithms that leverage problem- or domain-specific knowledge have been shown to discover high-quality solutions with fewer function evaluations than knowledge-independent EAs. Knowledge-intensive EAs apply the available knowledge through (1) knowledge-dependent operators that modify specific decision variables by biasing them towards or away from predetermined values or (2) knowledge-dependent constraints that penalize or eliminate solutions that are inconsistent with the provided knowledge. While both knowledge-dependent operators and knowledge-dependent constraints have shown promise in improving the search performance of an EA, there are no known experiments comparing their efficacy. To address the lack of such comparative experiments, this paper benchmarks one EA using knowledge-dependent operators and two EAs using knowledge-dependent constraints against an analogous knowledge-independent EA on a design problem for a climate-monitoring satellite system. Each EA is evaluated for its ability to attain high-quality solutions with the fewest possible number of function evaluations. In addition, we analyze the extent to which each method can focus on applying the knowledge that improves the search performance, handle conflicting information that suggests improving solutions with opposing modifications, and balance the exploitation of the available knowledge with the exploration of the tradespace to prevent premature convergence on local optima. The results show promise for EAs applying knowledge-dependent operators with an adaptive operator selection strategy and reveal some limitations of methods that adaptively apply knowledge-dependent constraints. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Knowledge-intensive evolutionary algorithms multiobjective evolutionary algorithms Satellite system optimization

来源：评论

学校读者我要写书评

暂无评论

Clustering-based multipopulation approaches in MOEA/D for many-objective problems

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2022年第3期81卷 789-828页

作者： von Lucken, Christian Brizuela, Carlos A. Baran, Benjamin Univ Nacl Asuncion Fac Politecn Campus Univ San Lorenzo Central Paraguay CICESE Res Ctr Carretera Ensenada Tijuana 3918 Ensenada Baja California Mexico

This work presents a new multipopulation framework for the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In this case, clustering methods are used to reinforce mating restrictions by splitting the MOEA/D evolutionary population into multiple subpopulations of similar individuals for independent evolution. Using subpopulations leads to a natural parallel implementation by assigning each subpopulation to a different processor. The proposed multipopulation MOEA/D (mpMOEA/D) is evaluated using three clustering methods: k-Means, spectral-based clustering, and a method based on the shape of objective vectors. Additionally, a random partitioning approach is tested. Metrics measuring convergence, diversity and computation time are used to compare the results of the mpMOEA/D alternatives and the original MOEA/D using DTLZ and WFG problems with 3, 4, 8 and 10 objectives. Evaluation using the Wilcoxon test and the Friedman rank reveals the importance of using clustering procedures for population division, especially in cases with many objectives. The results show the viability of the clustering-based multipopulation approach in enhancing the performance of evolutionary methods for many-objective problems.

关键词： multiobjective evolutionary algorithms Many-objective optimization Multipopulation evolutionary algorithms Multipopulation multiobjective evolutionary algorithm based on decomposition

来源：评论

学校读者我要写书评

暂无评论

Working principles, behavior, and performance of MOEAs on MNK-landscapes

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期181卷 1670-1690页

作者： Aguirre, Hernan E. Tanaka, Kiyoshi Shinshu Univ Fac Engn Nagano 3808553 Japan

This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs. (C) 2006 Elsevier B.V. All rights reserved.

关键词： evolutionary computations multiobjective evolutionary algorithms multiobjective combinatorial optimization MNK-landscapes epistasis non-linear multiobjective fitness functions discrete binary search spaces selection drift mutation recombination

来源：评论

学校读者我要写书评

暂无评论

Extended mean-variance model for reliable evolutionary portfolio optimization

引用

AI COMMUNICATIONS 2014年第3期27卷 315-324页

作者： Garcia, Sandra Quintana, David Galvan, InS M. Isasi, Pedro Univ Carlos III Madrid Dept Comp Sci Leganes 28910 Spain

Real world optimization of financial portfolios pose a challenging multiobjective problem that can be tackled using evolutionary algorithms. The fact that the optimization process is subject to the presence of uncertainty concerning asset returns is likely to lead to unreliable solutions. This work suggests extending the classic mean-variance optimization problem with a third explicit robustness objective. This results on sets of portfolios that can be subsequently grouped together according to their reliability. This additional information allows for a better informed decision making regarding asset allocation.

关键词： multiobjective evolutionary algorithms financial portfolio optimization robustness

来源：评论

学校读者我要写书评

暂无评论

Solving a type of biobjective bilevel programming problem using NSGA-II

引用

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2010年第2期59卷 706-715页

作者： Li, Minqiang Lin, Dan Wang, Shouyang Tianjin Univ Dept Math Tianjin 300072 Peoples R China Tianjin Univ Sch Management Tianjin 300072 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China

This paper considers a type of biobjective bilevel programming problem, which is derived from a single objective bilevel programming problem via lifting the objective function at the lower level up to the upper level. The efficient solutions to such a model can be considered as candidates for the after optimization bargaining between the decision-makers at both levels who retain the original bilevel decision-making structure. We use a popular multiobjective evolutionary algorithm, NSGA-II, to solve this type of problem. The algorithm is tested on some small-dimensional benchmark problems from the literature. Computational results show that the NSGA-II algorithm is capable of solving the problems efficiently and effectively. Hence, it provides a promising visualization tool to help the decision-makers find the best trade-off in bargaining. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Bilevel programming Two-level optimization multiobjective evolutionary algorithms Decision-making Bargaining

来源：评论

学校读者我要写书评

暂无评论

AN ADAPTIVE evolutionary ALGORITHM FOR UWB MICROSTRIP ANTENNAS OPTIMIZATION USING A MACHINE LEARNING TECHNIQUE

引用

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2013年第8期55卷 1864-1868页

作者： Silva, Claudio R. M. Martins, Sinara R. Fed Univ Rio Grande Norte UFRN Dept Commun Engn BR-59072970 Natal RN Brazil

This article presents an application of a machine learning technique to enhance a multiobjective evolutionary algorithm to estimate fitness function behaviors from a set of experiments made in laboratory to analyze a microstrip antenna used in ultra wideband wireless devices. These function behaviors are related to three objectives: bandwidth, return loss, and central frequency deviation. Each objective is used inside an aggregate adaptive weighted fitness function that estimates the behavior in the algorithm. The machine learning technique enabled a dynamic estimation of an aggregated compound fitness function and made it possible to a prototype algorithm to learn and adapt with a set of experiments stored in a web system repository. The final results were then compared with the ones obtained with a similar antenna modeled in a simulator program and with the ones of a real prototype antenna built from the optimal values obtained after the optimization. (c) 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1864-1868, 2013

关键词： multiobjective evolutionary algorithms machine learning genetic algorithm 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：