检索结果-内蒙古大学图书馆

A copula-based estimation of distribution algorithm for calibration of microscopic traffic models

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2019年 98卷 449-470页

作者： Fard, Mehdi Rafati Mohaymany, Afshin Shariat Iran Univ Sci & Technol Sch Civil Engn Traff Res Lab Tehran Iran

The importance of calibration of microscopic traffic models as the main core of traffic simulation software results from the need for more realistic traffic behaviors. The latent essence of several parameters in such models as well as the uncertainties resulting from the noise in the data, make the process of calibration much more complex. Usually, the calibration process is formulated as an optimization problem. Selecting the appropriate solution algorithm due to nonlinear and non convex nature of the problem is crucial. The importance of the issue is more significant when the matter of calibrating the medium or large-scale simulation model is considered. This is mainly due to the expensive cost that running the simulation models impose. Therefore, applying the current algorithms in which finding the appropriate solutions requires a large number of simulation runs is not deemed suitable. In this paper, an estimation of distribution algorithm based on copula theory has been suggested. In contrast with traditional solution algorithms, in the proposed algorithm complex interaction between parameters of a model has been considered by constructing and sampling from a copula-based probabilistic model. Copulas are functions that describe the dependence structure of a set of random variables and connect multivariate distribution functions to one-dimensional marginal distribution functions. The results indicate that applying an explicit probabilistic model based on copula helps the estimation of distribution algorithm to explore the search space more effectively and efficiently as well as provides the possibility of extracting the knowledge with regard to the structure of the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Furthermore, this new algorithm has been compared with the genetic algorithm and kernel-based cross-entropy method on synthetic and real trajectory data. The results confirm that the propos

关键词： Calibration estimation of distribution algorithms Copula Dependency structure Static metrics

来源：评论

学校读者我要写书评

暂无评论

Structure-control design of a mechatronic system with parallelogram mechanism using an estimation of distribution algorithm

引用

MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2016年第1-2期44卷 58-71页

作者： Ivvan Valdez, S. Chavez-Conde, E. Hernandez, Eusebio E. Ceccarelli, M. Ctr Res Math CIMAT Dept Comp Sci Guanajuato Mexico Univ Papaloapan Campus Loma Bonita Loma Bonita Mexico ESIME UPT Natl Polytech Inst Sect Grad Studies & Res Av Ticoman Mexico City 07340 DF Mexico Univ Cassino & Southern Lazio Lab Robot & Mechatron Cassino Italy

In this paper, a structure-control design methodology for simultaneously optimizing both mechanical structure and control of a parallelogram linkage robot is proposed. It takes into count the dynamical model and the mechanical parameters for the optimization process along with the controller. Thus, proportional-integral-derivative (PID) control and geometric variables are optimized in a simultaneously way. Through the concurrent procedure an optimal combination of the robot structure and controller gains is obtained. The global optimization problem is tackled by using an estimation of distribution algorithm (EDA) based on the Boltzmann distribution. The EDA seeks for the global optimum by estimating and sampling a probability distribution. The proposed methodology is verified through simulation experiments and applied to the design process of a parallelogram linkage system. The results obtained in experiments show the effectiveness of the proposal. This approach is generic and could be applied to other mechanisms in similar way when for concurrent process both kinematic and dynamic models are available along with the controller. In particular, the results are promising when the optimization parameters are uncorrelated, namely control and mechanical parameters.

关键词： Concurrent design estimation of distribution algorithms optimization parallelogram linkage

来源：评论

学校读者我要写书评

暂无评论

An estimation of distribution algorithm for nurse scheduling

引用

ANNALS OF OPERATIONS RESEARCH 2007年第1期155卷 289-309页

作者： Aickelin, Uwe Li, Jingpeng Univ Nottingham Sch Comp Sci & Informat Technol Nottingham NG8 1BB England

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an estimation of distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

关键词： estimation of distribution algorithms bayesian network nurse scheduling

来源：评论

学校读者我要写书评

暂无评论

A hybrid immune-estimation distribution of algorithm for mining thyroid gland data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2066-2071页

作者： Chang, Wei-Wen Yeh, Wei-Chang Huang, Pei-Chiao Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan

In this paper we combine the main concepts of estimation of distribution algorithms (EDAs) and immune algorithms (IAs) to be a hybrid algorithm called immune-estimation of distribution algorithms (IEDA). Both EDAs and IAs are extended from genetic algorithms (GAs). EDAs eliminate the genetic operation including crossover and mutation from the GAs and places more emphasis on the relation between gene loci. It adopts the distribution of selected individuals in search space and models the probability distributions to generate the next population. However, the primary gap of EDAs is lock of diversity between individuals. Hence, we introduce the IAs that is a new branch in computational intelligence. The main concepts of IAs are suppression and hypermutation that make the individuals be more diversity. Moreover, the primary gap of IAs is to pay no attention to the relation between individuals. Therefore, we combine the main concepts of two algorithms to improve the gaps each other. The classification risk of data mining is applied by this paper and compares the results between IEDA and general GAs in the experiments. We adopt the thyroid gland data set from LICI databases. Based on the obtained results, our research absolute is better than general GAs including accuracy, type I error and type 11 error. The results show not only the excellence of accuracy but also the robustness of the proposed algorithm. In this paper we have got high quality results which can be used as reference for hospital decision making and research workers. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithms Immune algorithms Genetic algorithms Classification rules

来源：评论

学校读者我要写书评

暂无评论

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

引用

JOURNAL OF GLOBAL OPTIMIZATION 2016年第4期66卷 729-768页

作者： Marti, Luis Garcia, Jesus Berlanga, Antonio Molina, Jose M. Univ Fed Flumense Inst Comp Ave Gal Milton Tavares de Souza BR-24210346 Rio De Janeiro Brazil Univ Carlos III Madrid Grp Appl Artificial Intelligence Dept Informat Ave Univ Carlos 322 Madrid 28270 Spain

The extension of estimation of distribution algorithms (EDAs) to the multi-objective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding "standard" multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the model-building issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.

关键词： Multi-objective optimization problems estimation of distribution algorithms Model-building problem Neural networks Growing neural gas

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Evolutionary Multi-Objective estimation of distribution Algorithm and Its Application to Multi-UAV Path Planning

引用

IEEE ACCESS 2023年 11卷 50038-50051页

作者： Ren, Yuhang Zhang, Liang Wuhan Univ Technol Sch Sci Dept Math Wuhan Peoples R China

This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.

关键词： Path planning Optimization Costs Planning Autonomous aerial vehicles Statistics Social factors Evolutionary algorithms Multiple UAVs collaborative path planning multi-objective optimization estimation of distribution algorithms evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization by estimation of distribution with DEUM Framework Based on Markov Random Fields

引用

International Journal of Automation and computing 2007年第3期4卷 262-272页

作者： Siddhartha Shakya John McCall Intelligent Systems Research Center British Telicom Adastral ParkIpswich IP5 3REUK School of Computing The Robert Gordon University Aberdeen AB25 1HGUK

This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields （DEUM）. DEUM is a subclass of estimation of distribution algorithms （EDAs） where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are： 1） use of MRF models, 2） fitness modeling approach to estimating the parameter of the model and 3） Monte Carlo approach to sampling from the model.

关键词： estimation of distribution algorithms evolutionary algorithms fitness modeling Markov random fields Gibbs distri-bution.

来源：评论

学校读者我要写书评

暂无评论

A HYBRID estimation OF distribution ALGORITHM FOR CDMA CELLULAR SYSTEM DESIGN

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2008年第2期7卷 187-200页

作者： Sun, Jianyong Zhang, Qingfu Li, Jin Yao, Xin Univ Birmingham Sch Comp Sci Cercia Birmingham B15 2TT W Midlands England Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

This paper proposes a hybrid estimation of distribution algorithm (HyEDA) to address the design problem of code division multiple access cellular system configuration. Given a service area, the problem is to find a set of optimal locations of base stations, associated with their corresponding powers and antenna heights in the area, in order to maximize call quality and service coverage, at the same time, to minimize the total cost of the system configuration. HyEDA is a two-stage hybrid approach which integrates an estimation of distribution algorithm, a K-means clustering method, and a simple local search algorithm. We have compared HyEDA with a simulated annealing method on a number of instances. Our simulation results have demonstrated that HyEDA outperforms the simulated annealing method in terms of the solution quality and computational cost.

关键词： CDMA cellular system configuration design hybrid evolutionary algorithm estimation of distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

estimation of distribution Algorithm for Grammar-Guided Genetic Programming

引用

EVOLUTIONARY COMPUTATION 2024年第4期32卷 339-370页

作者： Criado, Pablo Ramos Rolania, D. Barrios de la Hoz, David Manrique, Daniel Aturing Res Salamanca Spain Univ Politecn Madrid Dept Matemat Aplicada Ingn Ind ETSI Ind E-28006 Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial ETSI Informat Madrid Spain

Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This paper presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.

关键词： Grammar-guided genetic programming estimation of distribution algorithms genetic variation operators local search, locality search-space exploration

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

引用

ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2013年第4期68卷 247-273页

作者： Marti, Luis Garcia, Jesus Berlanga, Antonio Molina, Jose M. Univ Carlos III Madrid Grp Appl Artificial Intelligence Madrid 28270 Spain

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

关键词： Multi-objective optimization estimation of distribution algorithms Adaptive resonance theory

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：