2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献_第4页

An AIS algorithm for Web usage mining with directed mutation 一种面向有向突变的Web使用挖掘AIS算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631220

B. Helmi, Adel T. Rahmani

引用次数: 5

Agent-based social simulation based on cognitive economic efficiency 基于认知经济效率的主体社会模拟

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630932

Yu Yamamoto, A. Notsu, H. Ichihashi, Katsuhiro Honda

引用次数: 2

Building grid service on atmospheric radiative transfer simulation of remote sensing data 建立遥感数据大气辐射传输模拟网格服务

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631122

Z. Shaobin, Chen Shengbo, Bao Yunfei

{"title":"Building grid service on atmospheric radiative transfer simulation of remote sensing data","authors":"Z. Shaobin, Chen Shengbo, Bao Yunfei","doi":"10.1109/CEC.2008.4631122","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631122","url":null,"abstract":"The radiance leaving the Earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the Earth surface and each atmospheric level that are transmitted to the top of the atmosphere. It can be separated from the radiance at the top the atmospheric level measured by radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models (ARTM) have been developed to provide many options for modeling atmospheric radiation transport, the newly atmospheric ARTM, MODTRAN, will be researched after the atmospheric radiative transfer is described. And the simulation procedures and the applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented. At the same time, the powerful computing resource was required which the urgent requirement of disposal plentiful data. When test area of atmospheric radiative transfer extends the state, even all of country, the computing capability in single computer fall short of demand. So we introduce the conception- ldquoGrid Servicerdquo, it can solve the problem commendably.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116186566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Hyper-selection in dynamic environments 动态环境中的超选择

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631229

Shengxiang Yang, R. Tinós

{"title":"Hyper-selection in dynamic environments","authors":"Shengxiang Yang, R. Tinós","doi":"10.1109/CEC.2008.4631229","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631229","url":null,"abstract":"In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. This paper investigates the effect of the selection pressure on the performance of genetic algorithms in dynamic environments. A hyper-selection scheme is proposed for genetic algorithms, where the selection pressure is temporarily raised whenever the environment changes. The hyper-selection scheme can be combined with other approaches for genetic algorithms in dynamic environments. Experiments are carried out to investigate the effect of different selection pressures on the performance of genetic algorithms in dynamic environments and to investigate the effect of the hyper-selection scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. The experimental results indicate that the effect of the hyper-selection scheme depends on the problem under consideration and other schemes combined in genetic algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116306121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Online Quantity Flexibility contract model and its competitive analysis 在线数量弹性合同模型及其竞争分析

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631066

Chunlin Xin, Wei-Min Ma, Bin Liu

引用次数: 1

A genetic algorithm for fingerprint matching based on an integrated measure 基于综合测度的指纹匹配遗传算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630907

Weiguo Sheng, G. Howells, K. Harmer, M. Fairhurst, F. Deravi

引用次数: 0

Multiple Heterogeneous Ant Colonies with Information Exchange 具有信息交换的多异质蚁群

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631244

A. Arami, B. Rofoee, C. Lucas

{"title":"Multiple Heterogeneous Ant Colonies with Information Exchange","authors":"A. Arami, B. Rofoee, C. Lucas","doi":"10.1109/CEC.2008.4631244","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631244","url":null,"abstract":"The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple heterogeneous ant colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, control engineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114816496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Methods for decreasing the number of objective evaluations for independent computationally expensive objective problems 减少独立计算量大的客观问题的客观评价次数的方法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631245

Gregory Rohling

引用次数: 1

A simulated annealing algorithm for constrained Multi-Objective Optimization 约束多目标优化的模拟退火算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631013

H. Singh, A. Isaacs, T. Ray, W. Smith

引用次数: 19

PSO based stochastic programming model for risk management in Virtual Enterprise 基于粒子群算法的虚拟企业风险管理随机规划模型

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630869

Fuqiang Lu, Min Huang, Xingwei Wang

{"title":"PSO based stochastic programming model for risk management in Virtual Enterprise","authors":"Fuqiang Lu, Min Huang, Xingwei Wang","doi":"10.1109/CEC.2008.4630869","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630869","url":null,"abstract":"Risk management in a virtual enterprise (VE) is an important issue due to its agility and diversity of its members and its distributed characteristics. In this paper, a stochastic programming model of risk management is proposed. More specifically, we consider about the stochastic characters of the risk in VE, and then we build a stochastic programming model to deal with the stochastic characters of the risk. In detail, this is a chance constraint programming model. One of the great advantages of this class of model is that it can exactly describe the risk preference of the manager. In this model, the risk level of VE is obtained from a composite result of many risk factors. In order to reduce the risk level of VE, the manager has to select effective action for every risk factor. For each risk factor, there are several actions provided. Here we only select one action for a risk factor or do nothing with it. To solve this stochastic programming model, a particle swarm optimization (PSO) algorithm is designed. On the other hand, to deal with those stochastic variables, Monte Carlo simulation is combined with PSO algorithm. Finally, a numerical example is given to illustrate the effectiveness of the PSO algorithm and the result shows that the model is very useful for risk management in VE.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127999714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5