检索结果-内蒙古大学图书馆

Investigation of a multi-objective optimization tool for engine calibration

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2008年第D2期222卷 235-249页

作者： Wu, M. H. Lin, W. Duan, S. Y. Univ Derby Sch Art Design & Technol Derby DE22 1GB England Univ Wales Inst Biol Sci Aberystwyth Dyfed Wales Land Rover Ltd Warwick England

Since multi-objective optimizations are becoming more important in engine calibration, the paper investigates multi-objective genetic algorithms in the application of engine optimization. Although several multi-objective genetic algorithms have been developed and some have been applied successfully in the automotive industry, it is difficult to determine which multi-objective genetic algorithm outperforms others in finding the set of optimal solutions or Pareto-optimal front in a practical multi-objective optimization problem. Based on some widely used multi-objective genetic algorithms, the paper proposes a combined scheme to deal with the difficulties in finding the optimal solution set during the engine calibration process. In the proposed approach the real-coded representation is employed in the genetic algorithm and the elitist strategy is applied for each multi-objective genetic algorithm used. To assess the proposed approach, two computational examples are given to minimize the brake specific fuel consumption and to maximize the output power torque simultaneously. The results show that the proposed approach is well suited to multi-objective optimization in engine calibration.

关键词： multi-objective optimization multi-objective genetic algorithms engine optimization engine calibration

来源：评论

学校读者我要写书评

暂无评论

A decision support tool coupling a causal model and a multi-objective genetic algorithm

引用

APPLIED INTELLIGENCE 2007年第2期26卷 125-137页

作者： Blecic, Ivan Cecchini, Arnaldo Trunfio, Giuseppe A. Univ Sassari Dept Architecture & Planning I-07041 Alghero SS Italy

A significant class of decision making problems consists of choosing actions, to be carried out simultaneously, in order to achieve a trade-off between different objectives. When such decisions concern complex systems, decision support tools including formal methods of reasoning and probabilistic models are of noteworthy helpfulness. These models are often built through learning procedures, based on an available knowledge base. Nevertheless, in many fields of application (e.g. when dealing with complex political, economic and social systems), it is frequently not possible to determine the model automatically, and this must then largely be derived from the opinions and value judgements expressed by domain experts. The BayMODE decision support tool (Bayesian multi objective Decision Environment), which we describe in this paper, operates precisely in such contexts. The principal component of the program is a multi-objective Decision Network, where actions are executed simultaneously. If the noisy-OR assumptions are applicable, such a the model has a reasonably small number of parameters, even when actions are represented as non-binary variables. This makes the model building procedure accessible and easy. Moreover, BayMODE operates with a multi-objective approach, which provides the decision maker with a set of non-dominated solutions, computed using a multi-objective genetic algorithm.

关键词： Bayesian networks decision networks influence diagrams multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Parallel multi-objective genetic Algorithm for Classification Rule Mining

引用

IETE JOURNAL OF RESEARCH 2007年第5期53卷 475-483页

作者： Dehuri, Satchidananda Ghosh, Ashish Mall, Rajib Fakir Mohan Univ Dept Informat & Commun Technol Vyasa Vihar 756019 Balasore India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

multi-objective genetic algorithms (MOGAs) are finding increasing popularity as researchers realize their potential for obtaining good solutions to mining problems in large databases. Parallel multi-objective genetic algorithms (pMOGAs) attempts to reduce the processing time needed for computing the fitness functions and to reach an acceptable solution. We propose two different master slave models of pMOGA. Our proposed models exploit both data parallelism by distributing the data being mined across various processors, and control parallelism by distributing the population of individuals across all available processors. These models are implemented through a cluster computing environment and we measure the speed up of pMOGA over its sequential counterpart.

关键词： Rule mining multi-objective genetic algorithms Parallel multi-objective genetic algorithms Cluster-computing

来源：评论

学校读者我要写书评

暂无评论

A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2007年第5期15卷 539-557页

作者： Alcala, R. Gacto, M. J. Herrera, F. Alcala-Fdez, J. Univ Granada Dept Comp Sci E-18071 Granada Spain Univ Jaen Dept Comp Sci E-23071 Jaen Spain

This work proposes the application of multi-objective genetic algorithms to obtain Fuzzy Rule-Based Systems with a better trade-off between interpretability and accuracy in linguistic fuzzy modelling problems. To do that, we present a new post-processing method that by considering selection of rules together with the tuning of membership functions gets solutions only in Pareto zone with the highest accuracy. This method is based on the well-known SPEA2 algorithm, applying approriate genetic operators and including some modifications to concentrate the search in the desired Pareto zone.

关键词： multi-objective genetic algorithms linguistic modelling interpretability-accuracy trade-off rule selection tuning of membership functions

来源：评论

学校读者我要写书评

暂无评论

Motif discovery using multi-objective genetic algorithm in biosequences

引用

7th International Symposium on Intelligent Data Analysis

作者： Kaya, Mehmet Firat Univ Dept Comp Engn Data Min & Bioinformat Lab TR-23119 Elazig Turkey

ISBN: (纸本)9783540748243

We propose an efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our approach is that a large number of tradeoff (i.e., nondorr nated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization, To the best of our knowledge, this is the first effort in this direction. MOGAMOD can be applied to any data set with a sequential character, Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker can understand the tradeoff between the objectives. We compare MOGAMOD with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime, the number of shaded samples and multiple motifs.

关键词： motif discovery multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Gene Finding and Rule Discovery With a multi-objective Neural-genetic Hybrid 07

Gene Finding and Rule Discovery With a Multi-Objective Neura...

引用

Annual Conference of genetic and Evolutionary Computation Conference

作者： Keedwell, Ed Narayanan, Ajit Univ Exeter Harrison BldgNorth Pk Rd Exeter EX4 4QF Devon England Univ Portsmouth Portsmouth PO1 3HE Hants England

In this paper, we describe a multi-objective neural-genetic gene finding technique.

ISBN: (纸本)9781595936974

In this paper, we describe a multi-objective neural-genetic gene finding technique.

关键词： Gene expression analysis multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Availability allocation and multi-objective optimization for parallel-series systems

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期180卷 1231-1244页

作者： Chiang, Cheng-Hslung Chen, Liang-Hsuan Natl Cheng Kung Univ Dept Ind & Informat Management Tainan 701 Taiwan Hsuan Chuang Univ Dept Comp Sci Hsinchu 300 Taiwan

Availability allocation is required when the manufacturer is obliged to allocate proper availability to various components in order to design an end product to meet specified requirements. This paper proposes a new multi-objective genetic algorithm, namely simulated annealing based multi-objective genetic algorithm (saMOGA), to resolve the availability allocation and optimization problems of a repairable system, specifically a parallel-series system. Compared with a general multi-objective genetic algorithm, the major feature of the saMOGA is that it can accept a poor solution with a small probability in order to enlarge the searching space and avoid the local optimum. The saMOGA aims to determine the optimal decision variables, i.e. failure rates, repair rates, and the number of components in each subsystem, according to multiple objectives, such as system availability, system cost and system net profit. The proposed saMOGA is compared with three other multi-objective genetic algorithms. Computational results showed that the proposed approach could provide higher solution quality and greater computing efficiency. (c) 2006 Elsevier B.V. All rights reserved.

关键词： availability allocation availability optimization multi-objective genetic algorithms simulated annealing parallel-series system

来源：评论

学校读者我要写书评

暂无评论

A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the face of catalyst deactivation

引用

CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2007年第12期46卷 1299-1309页

作者： Kordabadi, H. Jahanmiri, A. Shiraz Univ Sch Engn Dept Chem & Petr Engn Shiraz 71345 Iran

This paper presents an optimization investigation on methanol synthesis reactor in the face of catalyst deactivation using multi-objective genetic algorithms. Catalyst deactivation is a challenging problem in the operation of methanol synthesis reactor and has an important role on productivity of the reactor. Therefore, determination of the optimal temperature profile along the reactor could be a very important effort in order to cope with catalyst deactivation. Our previous studies clarify the benefits of a two-stage reactor over a single stage reactor. In this study, an optimal temperature trajectory is obtained for each stage of the corresponding two-stage reactor. Here, steady state optimization is performed in six different activity levels by maximizing the yield and minimizing the temperature of the first stage of the reactor. multi-objective genetic algorithms are used to solve this two-objective optimization. The set of optimal solutions obtained for six activity levels represents an optimal temperature trajectory for each stage, which has been extended and proposed as adynamic optimization. This optimization resulted in an additional 3.6% yield, during the course of 4-year process. (c) 2006 Elsevier B.V. All rights reserved.

关键词： optimization methanol synthesis reactor multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

The multi-objective Next Release Problem 07

The Multi-Objective Next Release Problem

引用

Annual Conference of genetic and Evolutionary Computation Conference

作者： Zhang, Yuanyuan Harman, Mark Mansouri, S. Afshin Kings Coll London London WC2R 2LS England

ISBN: (纸本)9781595936974

This paper is concerned with the multi-objective Next Release Problem (MONRP), a problem in search-based requirements engineering. Previous work has considered only single objective formulations. In the multi-objective formulation, there are at least two (possibly conflicting) objectives that, the software engineer wishes to optimize. It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance. The paper presents the results of ail empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSCA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP. The paper also provides benchmark data to indicate the size above which the MONRP becomes non-trivial.

关键词： Pareto optimality next release problem multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A multi-objective Approach To Search-Based Test Data Generation 07

A Multi-Objective Approach To Search-Based Test Data Generat...

引用

Annual Conference of genetic and Evolutionary Computation Conference

作者： Harman, Mark Lakhotia, Kiran McMinn, Phil Kings Coll London CREST London WC2R 2LS England Univ Sheffield Sheffield S1 4DP S Yorkshire England

ISBN: (纸本)9781595936974

There has been a considerable body of work on search-based test;data, generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic: testers will want, to find test;sets that meet. several objectives simultaneously in order to maximize the value obtained front the inherently expensive process of running the test, cases and examining the output they produce. This paper introduces multi-objective branch coverage. The paper presents results front a case study of the twin objectives of branch coverage, and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi objective evolutionary algorithms are Suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.

关键词： Automated test data generation evolutionary testing multi-objective genetic algorithms

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：