检索结果-内蒙古大学图书馆

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第7-8期27卷 804-815页

作者： Pasupathy, T Rajendran, C Suresh, RK Indian Inst Technol Dept Management Studies Madras 600036 Tamil Nadu India Amrita Inst Technol & Sci Dept Prod Engn Coimbatore 641105 Tamil Nadu India

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.

关键词： flow shop scheduling makespan multi-objective genetic algorithms total flow time

来源：评论

学校读者我要写书评

暂无评论

A multi-objective genetic algorithm approach to optimal allocation of multi-type FACTS devices for power systems security

A multi-objective genetic algorithm approach to optimal allo...

引用

General Meeting of the Power-Engineering-Society

作者： Radu, D. Besanger, Y. Univ Grenoble 1 LEG Power Engn Lab Grenoble INPG UMR 5529 CNRSENSIEG BP 46 F-38402 St Martin Dheres France

ISBN: (纸本)9781424404926

A multi-objective programming procedure is used for solving the problem of optimal allocation of Flexible AC Transmission Systems (FACTS) devices in a power system. The evolutionary approach consists of a multi-objective genetic algorithm (MOGA), which is used to characterize the Pareto Optimal Frontier (non-dominated solutions) and to provide to Decision Makers and engineers insightful information about the trade-offs to be made. In this paper, two technical and economical objective functions are considered: maximization of system security and minimization of investment cost for FACTS devices. The optimization process is focused on three parameters: the location of FACTS in the network, their types and their sizes. For these proposals, we employed a hybrid software developed in Matlab (TM) which uses the EUROSTAG (TM) software for load flow calculations. The proposed procedures are successfully tested on an IEEE 14-bus power system for several numbers of FACTS devices.

关键词： FACTS devices multi-objective genetic algorithms optimal location Pareto frontier

来源：评论

学校读者我要写书评

暂无评论

Effectiveness of neighborhood crossover in multiobjective genetic algorithm

Effectiveness of neighborhood crossover in multiobjective ge...

引用

2nd IASTED International Conference on Computational Intelligence

作者： Yoshii, Kengo Hiroyasu, Tomoyuki Miki, Mitsunori Hashimoto, Atsushi Doshisha Univ Grad Sch Engn Kyoto 602 Japan Doshisha Univ Deprt Knowledge Engn Kyoto 602 Japan

ISBN: (纸本)9780889866027

In this paper, the effectiveness of the neighborhood crossover of EMO algorithms is discussed through the numerical experiments. The neighborhood crossover chooses two parents which are close to each other in the objective space. All the individuals are sorted in order of proximity in the objective space, and then neighborhood shuffle is conducted, which randomly replaces individuals in the population at a fixed width interval of population size. This operation prevents crossing over repeatedly between the same pair of individuals. The width of neighborhood shuffle is the parameter of this operation and this parameter determines the range of the population where individuals are shuffled. Therefore, this parameter affects the quality of the solutions. We implemented the NSGA-II with the neighborhood crossover and examined the effect of the width of neighborhood shuffle to further investigate the effectiveness of neighborhood crossover. The results of the numerical experiment indicated that the effect of neighborhood crossover can be achieved by applying neighborhood crossover to the search population created through copy selection. In addition, the necessity of neighborhood shuffle and an appropriate width of neighborhood shuffle were reviewed.

关键词： multi-objective genetic algorithms neighborhood crossover neighborhood shuffle optimization

来源：评论

学校读者我要写书评

暂无评论

MOGA algorithm for multi-objective optimization of aircraft detection

MOGA algorithm for multi-objective optimization of aircraft ...

引用

20th Congress of the International-Commission-for-Optics

作者： Sun, HG Pan, YX Zhang, JB NE Normal Univ Coll Comp Changchun 130024 Peoples R China ChangChun Univ Sci & Technol Changchun 130022 Peoples R China

ISBN: (纸本)0819460583

This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied to a multi-objective optimization of aircraft, measure of fitness degree was discussed as an emphasis. The solutions were analyzed and compares with original BP neural networks algorithm, which is better than the network trained only on alternating momentum, which can performed well neural networks and have shown the superiority to the network structure. Based the pareto optimal approaches are equipped with a fast identifying ability in capturing the learned objects, and in the meantime it can adapt the new objects. The experiments with variety of image show that the method proposed is efficient and useful, the result demonstrates that convergence speed is faster than traditional algorithm;target was recognized by this algorithm and can increase recognition precision.

关键词： multi-objective genetic algorithms objective recognition Pareto Optimal BP algorithm

来源：评论

学校读者我要写书评

暂无评论

Tuning of generator excitation systems using meta-heuristics

Tuning of generator excitation systems using meta-heuristics

引用

General Meeting of the Power-Engineering-Society

作者： Viveros, E. R. C. Taranto, G. N. Falcao, D. M. Univ Fed Rio de Janeiro COPPE BR-21945 Rio De Janeiro Brazil

ISBN: (纸本)9781424404926

This paper describes an approach for coordinated tuning of AVRs and PSSs based on Meta-heuristics. Two methods, based on small variations of the standard genetic algorithms, are described. The basic difference between the methods is in the way they evaluate the Fitness Function of the multiobjective problem. Results obtained in the traditional New England power system indicated that the methodology is able to find solutions satisfying the design criteria.

关键词： power system controllers multi-objective genetic algorithms coordinated control

来源：评论

学校读者我要写书评

暂无评论

Conceptual design of long-span trusses using multi-stage heuristics

Conceptual design of long-span trusses using multi-stage heu...

引用

作者： Agarwal, Pranab Texas A&M University

A hybrid method that addresses the design and optimization of long-span steel trusses is presented. By utilizing advancements in present day computing and biologically inspired analysis and design, an effort has been made to automate the process of evolving optimal trusses in an unstructured problem domain. Topology, geometry and sizing optimization of trusses are simultaneously addressed using a three stage methodology. multi-objective genetic algorithms are used to optimize the member section sizes of truss topologies and geometries. Converting constraints into additional objectives provides a robust algorithm that results in improved convergence to the pareto-optimal set of solutions. In addition, the pareto-curve plotted based on how well the different objectives are satisfied helps in identifying the trade-offs that exist between these objectives, while also providing an efficient way to rank the population of solutions during the search process. A comparison study between multi-objective genetic algorithms, simulated annealing, and reactive taboo search is conducted to evaluate the efficiency of each method with relation to its overall performance, computational expense, sensitivity to initial parameter settings, and repeatability of finding near-global optimal designs. The benefit of using a three stage approach, and also implementing the entire model on parallel computers, is the high level of computational efficiency that is obtained for the entire process and the near-optimal solutions obtained. The overall efficiency and effectiveness of this method has been established by comparing the truss design results obtained using this method on bridge and roof truss benchmark problems with truss designs obtained by other researchers. One of the salient features of thisresearch is the large number of optimal trusses that are produced as the final result. The range of designs available provides the user with the flexibility to select the truss design that best matc

关键词： structural engineering design and analysis roof and bridge trusses multi-objective genetic algorithms heuristics simulated annealing reactive taboo search unstructured problem domain conceptual design Book Thesis

来源：评论

学校读者我要写书评

暂无评论

A multi-objective genetic Algorithm for mixed-model sequencing on JIT assembly lines

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2005年第3期167卷 696-716页

作者： Mansouri, SA Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper presents a multi-objective genetic Algorithm (MOGA) approach to a Just-In-Time (JIT) sequencing problem where variation of production rates and number of setups are to be optimized simultaneously. These two objectives are typically inversely correlated with each other, and therefore, simultaneously optimization of both is challenging. Moreover, this type of problem is NP-hard, hence attainment of IP/LP solutions, or solutions via Total Enumeration (TE) is computationally prohibitive. The MOGA approach searches for locally Pareto-optimal or locally non-dominated frontier where simultaneous minimization of the production rates variation and the number of setups is desired. Performance of the proposed MOGA was compared against a TE scheme in small problems and also against three other search heuristics in small, medium and large problems. Experimental results show that the MOGA performs very well when compared against TE in a considerably shorter time. It also outperforms the comparator algorithms in terms of quality of solutions at the same level of diversity in reasonable amount of CPU time. (C) 2004 Elsevier B.V. All rights reserved.

关键词： mixed-model sequencing Just-In-Time Pareto-optimal frontier multi-objective genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A multi-objective genetic algorithm for robust design optimization 05

A multi-objective genetic algorithm for robust design optimi...

引用

genetic and Evolutionary Computation Conference

作者： Li, Mian Azarm, Shapour Aute, Vikrant Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (纸本)1595930108

Real-world multi-objective engineering design optimization problems often have parameters with uncontrollable variations. The aim of solving such problems is to obtain solutions that in terms of objectives and feasibility are as good as possible and at the same time are least sensitive to the parameter variations. Such solutions are said to be robust optimum solutions. In order to investigate the trade-off between the performance and robustness of optimum solutions, we present a new Robust multi-objective genetic Algorithm (RMOGA) that optimizes two objectives: a fitness value and a robustness index. The fitness value serves as a measure of performance of design solutions with respect to multiple objectives and feasibility of the original optimization problem. The robustness index, which is based on a non-gradient based parameter sensitivity estimation approach, is a measure that quantitatively evaluates the robustness of design solutions. RMOGA does not require a presumed probability distribution of uncontrollable parameters and also does not utilize the gradient information of these parameters. Three distance metrics are used to obtain the robustness index and robust solutions. To illustrate its application, RMOGA is applied to two well-studied engineering design problems from the literature.

关键词： multi-objective genetic algorithms robust design optimization performance and robustness trade-off

来源：评论

学校读者我要写书评

暂无评论

Evolving dynamic multi-objective optimization problems with objective replacement

引用

ARTIFICIAL INTELLIGENCE REVIEW 2005年第3期23卷 267-293页

作者： Guan, SU Chen, Q Mo, WT Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.

关键词： multi-objective genetic algorithms multi-objective problems multi-objective optimization non-stationary environment

来源：评论

学校读者我要写书评

暂无评论

multi-objective EVOLUTIONARY SCHEDULING OF DISTRIBUTED SUPPLY NETWORKS

引用

IFAC Proceedings Volumes 2005年第1期38卷 1-6页

作者： David Naso Michele Surico Dipartimento di Elettrotecnica ed Elettronica Politecnico di Bari – Via Re David 200 – 70125 Bari – Italy Fax: +39 – 080 5460 410

This paper considers the problem of scheduling a distributed network of production centers supplying a quickly perishable good that has to be produced just-in-time and delivered within customer-specified strict time windows. The problem includes several planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. Ideal solutions should provide a good compromise between production costs (resource utilization, delivery costs) and tolerance to stochastic perturbations (transport delays). We propose a novel multi-objective meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. The algorithm is designed to return a set of solutions with different cost and risk tradeoffs. The effectiveness of the approach is confirmed by a comparison with other recently proposed methods.

关键词： Supply Networks Scheduling Meta-Heuristics 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：