检索结果-内蒙古大学图书馆

The Ninth International Symposium on Distributed Computing and Applications to Business,Engineering and Science

作者： Yu Long, Wang Pan, Zhu Haoshen School of Automation Wuhan University of Technology Wuhan, P. R. China

In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.

关键词： multi-objective evolutionary algorithm crowding distance diversity preservation NSGA II

来源：评论

学校读者我要写书评

暂无评论

evolutionary multi-objective Approach to Control of Mobile Robot in Unknown Environment

Evolutionary Multi-Objective Approach to Control of Mobile R...

引用

22nd International Symposium on Information Communication and Automation Technologies

作者： Konjicija, Samim Avdagic, Zikrija Fac Elect Engn Dept Control & Elect Sarajevo Bosnia & Herceg Fac Elect Engn Dept Informat Technol Sarajevo Bosnia & Herceg

ISBN: (纸本)9781424442201

This paper describes how a non-stationary multi-objective optimization model can be used for synthesis of control of mobile robot in unknown environment. The modelled problem is solved using multi-objective genetic algorithm (MOGA). Results of experiments conducted in simulation environment demonstrate the application of the described approach.

关键词： mobile robot multi-objective evolutionary algorithm dynamic optimization

来源：评论

学校读者我要写书评

暂无评论

Robustness Analysis in evolutionary multi-objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Robustness Analysis in Evolutionary Multi-Objective Optimiza...

引用

9th European Conference on evolutionary Computation in Combinatorial Optimization

作者： Barrico, Carlos Antunes, Carlos Henggeler Pires, Dulce Fernao INESC Rua Antero Quental 199 P-3000033 Coimbra Portugal

ISBN: (纸本)9783642010088

In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated Solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionary algorithm, which intervenes in the Computation of the fitness value assigned to Solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power How, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.

关键词： robustness analysis multi-objective evolutionary algorithm reactive power compensation problem

来源：评论

学校读者我要写书评

暂无评论

multi-objective genetic local search algorithm using Kohonen's neural map

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2009年第4期56卷 1566-1576页

作者： Hakimi-Asiabar, Mehrdad Ghodsypour, Seyyed Hassan Kerachian, Reza Amirkabir Univ Technonol Dept Ind Eng Tehran Iran Univ Tehran Ctr Excellence Infrastruct Engn & Management Fac Civil Engn Tehran Iran

Genetic algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAS is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations. In this paper, a new method called SOM-Based multi-objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the evolutionary algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objectiv

关键词： multi-objective genetic local search Self organizing maps Variable Neighborhood Search (VNS) multi-objective evolutionary algorithm Learning multi-reservoir operation management

来源：评论

学校读者我要写书评

暂无评论

Diversity Maintenance Strategy Based on Global Crowding

引用

6th International Symposium on Neural Networks

作者： Chen, Qiong Xiong, Shengwu Liu, Hongbing Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9783642015120

In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses Of uniform distribution than traditional diversity maintenance strategies.

关键词： Global Crowding Diversity Maintenance Strategies Entropy Metrics multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A Filter Banks Design Using a multi-objective Genetic algorithm for Embedded Image Coding Scheme

A Filter Banks Design Using a Multi-objective Genetic Algori...

引用

International Symposium on Image and Signal Processing and Analysis

作者： A. Boukhobza A. Taleb Ahmed A. Bounoua N. Taleb RCAM Laboratory of research University of Sidi-Bel Abbes LAMIH Laboratory of research University of Valenciennes Valenciennes RCAM Laboratory of research University of Sidi-BelAbbes

ISBN: (纸本)9781424456499

A multi-objective genetic algorithm for the design of biorthogonal filter banks for embedded image coding application is presented. To be effective, the filter bank would satisfy multiple requirements related to such application. Flexibility in the design is introduced by imposing Near Perfect Reconstruction (N-PR) condition instead of entire PR condition as in conventional designs. Especially for embedded coding purposes, the filter banks are designed to be near-orthogonal. This can only be made possible by minimizing the deviation from the orthogonality in the optimization process. The optimization problem is formulated as a constrained multi-objective problem and solved using a constrained Non-dominated sorting genetic algorithm (C-NSGA) by searching solutions that achieve the best compromise between the different objective criteria, these solutions are known as Pareto Optimal Solutions. Experiment results show that our designed filter banks lead to improved performances of image coding compared to those achieved by the 9/7 filter bank of JPEG2000.

关键词： Filter banks embedded image coding Genetic algorithm (GA) Banks algorithm design JPEG image coding orthogonality Optimization Procedure multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal protection of complex networks exposed to a terrorist hazard: A multi-objective evolutionary approach

引用

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2008年第3期222卷 327-335页

作者： Salazar a, D.E. Rocco s, C.M. Zio, E. Universidad de Las Palmas de Gran Canaria Spain Universidad Central de Venezuela United States Politecnico di Milano United States

The present paper proposes an approach for prioritizing the protection of a network system exposed to a terrorist attack. The approach is based on a multi-objective optimization (MO) formulation for finding Pareto optimal solutions with respect to two indicators measuring the damage that the attack may cause: the time to reach all network destination nodes (TTRAD) and the average number of persons affected (ANPA). The MO is tackled by means of a multi-objective evolutionary algorithm (MOEA) that combines the basic concepts of dominance with the general characteristics of evolutionary algorithms. Within this optimization scheme, the goodness of each alternative protection scheme is quantified by a combination of cellular automata (CA) and Monte Carlo (MC) simulation. Numerical examples illustrate how the approach is capable of identifying effective protection schemes. © 2008, Institution of Mechanical Engineers. All rights reserved.

关键词： cellular automata critical infrastructures Monte Carlo simulation multi-objective evolutionary algorithm multi-objective optimization network systems security

来源：评论

学校读者我要写书评

暂无评论

multi-objective carrier chaotic evolutionary algorithm for DNA sequences design

引用

Progress in Natural Science:Materials International 2007年第12期17卷 1515-1520页

作者： Xiao Jianhua, Xu Jin, Geng Xiutang and Pan Linqiang(Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China) Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.

关键词： DNA computing carrier chaotic search multi-objective evolutionary algorithm DNA sequences design.

来源：评论

学校读者我要写书评

暂无评论

Shape optimization of an axial compressor blade by multi-objective genetic algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2008年第A6期222卷 599-611页

作者： Samad, A. Kim, K-Y Inha Univ Dept Mech Engn Inchon 402751 South Korea

In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sorting of genetic algorithm-II has been implemented and confidence check has been performed at k-means clustered points among all the Pareto-optimal solutions. Reynolds-averaged Navier-Stokes equations are solved to obtain the objective function and flow field inside the compressor annulus. The objective functions are used to generate Pareto-optimal front. The design variables are selected from blade lean and thickness through the Bezier polynomial formulation. By this optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 per cent, respectively, when two extreme clustered points are considered as optimal designs.

关键词： non-dominated sorting of genetic algorithm-II multi-objective evolutionary algorithm axial compressor blade efficiency total pressure response surface method

来源：评论

学校读者我要写书评

暂无评论

Improving NSGA-II algorithm Based on Minimum Spanning Tree

Improving NSGA-II Algorithm Based on Minimum Spanning Tree

引用

7th International Conference on Simulated Evolution and Learning

作者： Li, Miqing Zheng, Jinhua Wu, Jun Xiangtan Univ Inst Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540896937

Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.

关键词： multi-objective optimization multi-objective evolutionary algorithm Crowding distance Minimum spanning tree

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：