找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Knowledge Science, Engineering and Management; 11th International C Weiru Liu,Fausto Giunchiglia,Bo Yang Conference proceedings 2018 Spring

[复制链接]
楼主: ARSON
发表于 2025-3-30 09:25:43 | 显示全部楼层
发表于 2025-3-30 16:04:01 | 显示全部楼层
发表于 2025-3-30 18:56:40 | 显示全部楼层
A Multi-objective Optimization Algorithm Based on Preference Three-Way Decompositionated set of the three sub-problems, a set of external preservation sets are formed so as to get the optimal set that the DM is interested in. Experimental results show that the proposed method can reduce the workload of the DM and obtain more accurately converge to the optimal frontiers of the optimization problems.
发表于 2025-3-30 21:55:02 | 显示全部楼层
A Community-Division Based Algorithm for Finding Relations Among Linear Constraintslations among constraints in the same community through search. Experimental results show that the algorithm can effectively process large set of constraints, reduce time cost and find relations with higher quality.
发表于 2025-3-31 03:52:57 | 显示全部楼层
A Parthenogenetic Algorithm for Deploying the Roadside Units in Vehicle NetworksPGA is proposed to solve the deployment problem. Compared with algorithms Delta-r and Delta-GA, in many .-Deployments, the Delta-uc and UCPGA algorithms respectively required fewer RSUs, which were proved by the experiments on the realistic mobility trace of Cologne, Germany.
发表于 2025-3-31 05:39:27 | 显示全部楼层
发表于 2025-3-31 09:52:55 | 显示全部楼层
ROSIE: Runtime Optimization of SPARQL Queries over RDF Using Incremental Evaluationn, as well as a mechanism to detect cardinality estimation error at runtime, ROSIE relieves the problem of biased cardinality propagation in an efficient way. Extensive experiments on real and benchmark data have shown that, compared to the state-of-the-arts, ROSIE consistently outperformed on complex queries by orders of magnitude.
发表于 2025-3-31 14:41:32 | 显示全部楼层
发表于 2025-3-31 18:26:04 | 显示全部楼层
发表于 2025-3-31 23:43:26 | 显示全部楼层
The New Adaptive ETLBO Algorithms with K-Armed Bandit Model-KAB algorithm is effective and brings dramatic improvement compared with TLBO and ETLBO. Furthermore, a new perturbation strategy—discussion group strategy is proposed. And the experimental results indicate that the efficiency of AETLBO-KAB with discussion group algorithm exceeds AETLBO-KAB algorithm.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 吾爱论文网 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
QQ|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-8-11 21:35
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表