检索结果-内蒙古大学图书馆

A New dynamical evolutionary algorithm Based on Statistical Mechanics

Journal of Computer Science & Technology 2003年第3期18卷 361-368页

作者：李元香邹秀芬康立山 Zbigniew Michalewicz Software Engineering Laboratory Wuhan University Wuhan 430072 P.R. China Computer Science Department University of North Carolina at Charlotte Charlotte NC 28223-0001 USA

In this paper, a new dynamical evolutionary algorithm (DEA) is presented basedon the theory of statistical mechanics. The novelty of this kind of dynamical evolutionary algorithmis that all individuals in a population (called particles in a dynamical system) are running andsearching with their population evolving driven by a nev selecting mechanism. This mechanismsimulates the principle of molecular dynamics, which is easy to design and implement. A basictheoretical analysis for the dynamical evolutionary algorithm is given and as a consequence twostopping criteria of the algorithm are derived from the principle of energy minimization and the lawof entropy increasing. In order to verify the effectiveness of the scheme, DEA is applied to solvingsome typical numerical function minimization problems which are poorly solved by traditionalevolutionary algorithms. The experimental results show that DEA is fast and reliable.

关键词： dynamical evolutionary algorithm statistical mechanics stopping criterion dynamical system

来源：评论

学校读者我要写书评

暂无评论

An efficient dynamical evolutionary algorithm for global optimization

引用

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2003年第11期80卷 1429-1436页

作者： Zou, XF Li, YX Kang, LS Wu, ZJ Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Wuhan Univ Coll Math & Stat Wuhan 430072 Peoples R China

In this paper, we introduce a new dynamical evolutionary algorithm (DEA) that aims to find the global optimum and give the theoretical explanation from statistical mechanics. The algorithm has been evaluated numerically using a wide set of test functions which are nonlinear, multimodal and multidimensional. The numerical results show that it is possible to obtain global optimum or more accurate solutions than other methods for the investigated hard problems.

关键词： dynamical evolutionary algorithm statistical mechanics global optimization genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

引用

SOFT COMPUTING 2018年第1期22卷 137-146页

作者： Yue, Xuezhi Guo, Zhaolu Yin, Yali Liu, Xiaosheng Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Sch Architectural & Surveying & Mapping Engn Ganzhou 341000 Peoples R China

In evolutionary multi-objective optimization, achieving a balance between convergence speed and population diversity remains a challenging topic especially for many-objective optimization problems (MaOPs). To accelerate convergence toward the Pareto front and maintain a high degree of diversity for MaOPs, we propose a new many-objective dynamical evolutionary algorithm based on E-dominance and adaptive-grid strategies (EDAGEA). In EDAGEA, it incorporates the E_dominance and adaptive strategies to enhance the search ability. Instead of the Pareto dominance mechanism in the traditional dynamical evolutionary algorithm, EDAGEA employs the E-dominance strategy to improve the selective pressure and to accelerate the convergence speed. Moreover, EDAGEA incorporates the adaptive-grid strategy to promote the uniformity and diversity of the population. In the experiments, the proposed EDAGEA algorithm is tested on DTLZ series problems under 3-8 objectives with diverse characteristics and is compared with two excellent many-objective evolutionary algorithms. Experimental results demonstrate that the proposed EDAGEA algorithm exhibits competitive performance in terms of both convergence speed and diversity of population.

关键词： Many-objective dynamical evolutionary algorithm Adaptive-grid E-dominance

来源：评论

学校读者我要写书评

暂无评论

An improved dynamical evolutionary algorithm based on chaotic

An improved dynamical evolutionary algorithm based on chaoti...

引用

7th International Conference on Machine Learning and Cybernetics

作者： Jiang, Yi Wang, Ling Chen, Li Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

ISBN: (纸本)9781424420957

An improved dynamical evolutionary algorithm based on the chaotic is proposed for optimizing. The new algorithm makes full use of initial value sensitivity and track ergodicity of chaos, overcoming the disadvantage of big searching dead zone existed in conventional chaotic mutation model. To achieve high performance in optimizing, the chaotic search mechanism is embedded in the standard dynamical evolutionary algorithm adaptively to avoid the stagnancy of population and increase the speed of convergence. The method keeps balance between the global search and the local search. It has been compared with other methods. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results.

关键词： dynamical evolutionary algorithm chaotic search optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid dynamical evolutionary algorithm and Time Complexity Analysis

Hybrid Dynamical Evolutionary Algorithm and Time Complexity ...

引用

International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Li, Huiying Zhang, Yilai Xu, Xing Jingdezhen Ceram Inst Sch Informat Engn Jingdezhen Peoples R China

ISBN: (纸本)9783037855744

The dynamical evolutionary algorithm (DEA) is a new evolutionary algorithm based on the theory of statistical mechanics, however, DEA converges slowly and often converge at local optima for some function optimization problems. In this paper, a hybrid dynamical evolutionary algorithm (HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal solutions. Moreover, the population of HDEA is initialized by chaos. In order to confirm the effectiveness of our algorithm, HDEA is applied to solve the typical numerical function minimization problems. The computational complexity of HDEA is analyzed, and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision, even the two algorithms have the similar time complexity.

关键词： dynamical evolutionary algorithm chaos multi-parent crossover differential evolution time complexity

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation for Asymptotic Regression Model by dynamical evolutionary algorithm

Parameter Estimation for Asymptotic Regression Model by Dyna...

引用

International Conference on Innovative Computing and Communication/Asia-Pacific Conference on Information Technology and Ocean Engineering

作者： Ye Hai-peng Hu Hao Wenzhou Vocat Coll Sci & Technol Dept Comp Wenzhou Zhejiang Peoples R China Jiangsu Polytech Univ Sch Informat Sci & Engn Changzhou Peoples R China

ISBN: (纸本)9780769539423

The dynamical evolutionary algorithm (DEA) is a novel evolutionary computation technology, which is based on the theory of statistical mechanics. In this paper, an improved dynamical evolutionary algorithm (IDEA) with multi-parent crossover and differential evolution mutation is proposed and IDEA is applied to estimate parameters for asymptotic regression model for the first time. In order to confirm performance of our algorithm, IDEA is verified on six groups of actual data and several sets of random sampling data, and then how sampling range and data with Gaussian noise influence on the performance of IDEA is considered. Experimental results show that IDEA is a stable, reliable and effective method in parameter estimation for asymptotic regression model and it's robust to noise.

关键词： dynamical evolutionary algorithm asymptotic regression model parameter estimation improved algorithm

来源：评论

学校读者我要写书评

暂无评论

The Tracking dynamical evolutionary algorithm for Dynamic Environments

The Tracking Dynamical Evolutionary Algorithm for Dynamic En...

引用

International Symposium on Intelligent Ubiquitous Computing and Education

作者： Liu, Suming Zhao, Qiang Zhang, Yumei Hebei Univ Engn Sch Mech Engn Handan 056038 Peoples R China Hebei Univ Engn Sch Human Handan 056038 Peoples R China

ISBN: (纸本)9780769536194

In this paper, we proposed the Tracking dynamical evolutionary algorithm (TDEA) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDEA, the particle's structure is different from traditional DEA. Each particle's knowledge is applied an "evaporation constant" to gradually weaken the knowledge's validity. Through this mechanism, the knowledge of each particle will be gradually updated in a dynamically changing environment. Compared with the traditional DEA, TDEA can quickly converge to the area of the goal and maintain the shortest distance from the goal.

关键词： dynamical evolutionary algorithm Dynamic Environments

来源：评论

学校读者我要写书评

暂无评论

Finding Global Minima with a New dynamical evolutionary algorithm

引用

Wuhan University Journal of Natural Sciences 2002年第2期7卷 157-160页

作者： Zou Xiu-fen Kang Li-shan Li Yuan-xiang Chen Yu-ping State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China School of Mathematics and Statistics Wuhan University Wuhan Hubei China

A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel features are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results.

关键词： dynamical evolutionary algorithm statistical mechanics global optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid dynamical evolutionary algorithm and Time Complexity Analysis

Hybrid Dynamical Evolutionary Algorithm and Time Complexity ...

引用

2010 International Conference on Circuit and Signal Processing (ICCSP 2010) & 2010 Second IITA International Joint Conference on Artificial Intelligence (IITA-JCAI 2010)

作者： Huiying LI School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen,China

The dynamical evolutionary algorithm(DEA) is a new evolutionary algorithm based on the theory of statistical mechanics,however,DEA converges slowly and often converge at local optima for some function optimization *** this paper,a hybrid dynamical evolutionary algorithm(HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal ***,the population of HDEA is initialized by *** order to confirm the effectiveness of our algorithm,HDEA is applied to solve the typical numerical function minimization *** computational complexity of HDEA is analyzed,and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision,even the two algorithms have the similar time complexity.

关键词： dynamical evolutionary algorithm chaos multi-parent crossover differential evolution time complexity

来源：评论

学校读者我要写书评

暂无评论

A New dynamical evolutionary algorithm Based on the Principle of Minimal Free Energy

A New Dynamical Evolutionary Algorithm Based on the Principl...

引用

智能计算及其应用国际会议

作者： HU Ting1, LI Yuanxiang1, 2, DING Wei1 (1. Computer School, Wuhan University, Wuhan 430079, China) (2. The State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China)

In this paper, a new dynamical evolutionary algorithm is proposed based on principle of minimal free energy from the statistical mechanics. Also, a definition of the entropy of the particle system is

关键词： dynamical evolutionary algorithm statistical mechanics information theory global optimization simulated annealing

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：