检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

141 篇 期刊文献
44 篇 会议

馆藏范围

185 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

181 篇 工学
- 150 篇 计算机科学与技术...
- 35 篇 电气工程
- 18 篇 控制科学与工程
- 9 篇 信息与通信工程
- 8 篇 化学工程与技术
- 7 篇 石油与天然气工程
- 7 篇 软件工程
- 6 篇 动力工程及工程热...
- 4 篇 机械工程
- 3 篇 仪器科学与技术
- 3 篇 交通运输工程
- 2 篇 冶金工程
- 2 篇 土木工程
- 2 篇 船舶与海洋工程
- 1 篇 光学工程
- 1 篇 材料科学与工程（可...
- 1 篇 航空宇航科学与技...
- 1 篇 环境科学与工程（可...
- 1 篇 食品科学与工程（可...
47 篇 管理学
- 47 篇 管理科学与工程(可...
19 篇 理学
- 10 篇 生物学
- 6 篇 数学
- 2 篇 物理学
- 2 篇 化学
- 1 篇 海洋科学
- 1 篇 统计学（可授理学、...
2 篇 法学
- 2 篇 法学
1 篇 医学
- 1 篇 临床医学

主题

185 篇 dynamic multi-ob...
33 篇 evolutionary alg...
22 篇 prediction
22 篇 evolutionary alg...
19 篇 transfer learnin...
7 篇 decomposition
6 篇 optimization
5 篇 memory
4 篇 dynamic optimiza...
4 篇 diversity
4 篇 prediction strat...
4 篇 particle swarm o...
4 篇 local search
4 篇 evolutionary com...
3 篇 simulation
3 篇 high speed railw...
3 篇 support vector m...
3 篇 constrained opti...
3 篇 convergence
3 篇 differential evo...

机构

12 篇 de montfort univ...
7 篇 huaibei normal u...
7 篇 oklahoma state u...
6 篇 huaibei normal u...
5 篇 yanshan univ key...
5 篇 east china univ ...
4 篇 xiangtan univ ke...
4 篇 beijing univ tec...
4 篇 xiangtan univ sc...
4 篇 hengyang normal ...
4 篇 city univ hong k...
4 篇 xiamen univ sch ...
3 篇 tiangong univ sc...
3 篇 hunan univ sci &...
3 篇 southern univ sc...
3 篇 hefei comprehens...
3 篇 nanjing univ sta...
3 篇 zhengzhou univ l...
3 篇 anhui engn res c...
3 篇 univ surrey dept...

作者

18 篇 yang shengxiang
13 篇 zou juan
13 篇 zheng jinhua
11 篇 hu yaru
9 篇 chen debao
9 篇 ou junwei
7 篇 sun hao
7 篇 yen gary g.
7 篇 hu ziyu
6 篇 wei lixin
6 篇 ruan gan
6 篇 liu ruochen
6 篇 zou feng
6 篇 ma yongjie
6 篇 zhang xi
5 篇 jiang min
5 篇 yao xin
5 篇 jin yaochu
5 篇 ge fangzhen
4 篇 gong dunwei

语言

179 篇 英文
5 篇 其他
1 篇 中文

检索条件"主题词=Dynamic Multi-objective Optimization"

共 185 条记录，以下是61-70 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A Regional Local Search and Memory based Evolutionary Algorithm for dynamic multi-objective optimization 39

A Regional Local Search and Memory based Evolutionary Algori...

引用

39th Chinese Control Conference (CCC)

作者： Li, Sanyi Wang, Yanfeng Yue, Weichao Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China

ISBN: (纸本)9789881563903

This paper presents a novel dynamic multi-objective optimization algorithm based on region local search and memory (DMOA-RLSM). Firstly, the NSGA2-DM stores useful information (memory) to guide population initialization in the future;secondly, in the stage of population regeneration, DMOA-RLSM get corner points and sparse point according to the results of non-dominated sorting of current populations, define these points as the centers of border areas and sparse area respectively;thirdly, search around the corner points and sparse point locally. DMOA-RLSM adopts extreme optimization strategy and random search strategy simultaneously to improve the quality of solutions and convergence rate. Performance of DMOA-RLSM is compared with two reported dynamic multi-objective optimization algorithms (DMOAs) for dMOP functions and FDA functions. Results show that the DMOA-RLSM performs better than the compared algorithms because of its low computational complexity.

关键词： dynamic multi-objective optimization local search memory

来源：评论

学校读者我要写书评

暂无评论

When and How to Transfer Knowledge in dynamic multi-objective optimization

When and How to Transfer Knowledge in Dynamic Multi-objectiv...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ruan, Gan Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham W Midlands England Honda Res Inst Europe GmbH D-63073 Offenbach Germany Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728124858

Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.

关键词： evolutionary algorithms transfer learning dynamic multi-objective optimization prediction-based method

来源：评论

学校读者我要写书评

暂无评论

A Novel Scalable Framework For Constructing dynamic multi-objective optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tan, Qingshan Li, Changhe Xia, Hai Zeng, Sanyou Yang, Shengxiang China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Hubei Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

ISBN: (纸本)9781728183923

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized;and the number of conflicting variables should be independent from the number of objectives;the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： dynamic multi-objective optimization Benchmark design Time-linkage

来源：评论

学校读者我要写书评

暂无评论

Adaptive dynamic environment response based evolutionary algorithm for dynamic multi-objective optimization 34

Adaptive dynamic environment response based evolutionary alg...

引用

34th Chinese Control and Decision Conference (CCDC)

作者： Liu, Kanrong Liu, Jianchang Tan, Shubin Li, Fei Zheng, Tianzi Liu, Yuanchao Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Anhui Univ Technol Dept Elect & Informat Engn Maanshan 243032 Anhui Peoples R China Anhui Univ Technol Anhui Prov Key Lab Special Heavy Load Robot Maanshan 243032 Anhui Peoples R China

ISBN: (纸本)9781665478960

In dynamic multi-objective optimization problems (DMOPs), multiple conflicting objectives vary over time. Therefore, a core issue for the DMOPs is to respond the dynamic environments, when the environment change has been detected. To effectively address DMOPs, a novel method, called adaptive dynamic environment response based dynamic multi-objective evolutionary algorithm (ADER-DMOEA), is proposed in this paper. The proposed algorithm can adaptively respond the dynamic environment based on the detected environment change severity. First, a novel environment change severity detection mechanism is developed, where C-metric is introduced to measure the performance of different population obtained by different types of response strategies. Second, according to the measurement results, the algorithm adaptively adopts different strategies for environment response. Therefore, it can ensure that the proposed method has a good initial population for each new environment. The proposed method is compared with three algorithms, and the experiment results have demonstrated that ADER-DMOEA is competitive in dealing with dynamic environments.

关键词： Adaptive dynamic environment response dynamic multi-objective optimization Prediction Memory Response mechanism C-metric

来源：评论

学校读者我要写书评

暂无评论

Knee Points based Transfer dynamic multi-objective optimization Evolutionary Algorithm

Knee Points based Transfer Dynamic Multi-objective Optimizat...

引用

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Wang, Zhenzhong Mei, Zhongrui Jiang, Min Yen, Gary Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781728169293

When dynamic multi-objective optimization evolutionary algorithms (DMOEA) are used to solve real world problems, these are not only required to be able to find the Pareto-Optimal Set (POS) quickly, but also the results obtained can be easily used by decision makers. The classic DMOEAs have much room for improvement in both aspects. Recently, the transfer learning based DMOEAs have been proved that these methods can significantly improve the quality of the solution, but there are still too many individuals in the POS obtained by these algorithms. The resulting problems are twofold: this not only consumes a lot of computing resources to those solutions that will not be used, but also makes it more difficult for decision makers to choose. In this paper, we proposed a dynamic multi objective optimization evolutionary algorithm which combines knee solutions with transfer learning method, and the feature of the proposed method is that it only outputs a very small number of solutions, which can greatly improve the efficiency of decision making. The proposed algorithm divides the whole decision space into different subspaces, and find a local knee solutions in each subspace, then a transfer learning framework, Tr-DMOEA, is used to predict the knee solutions of the optimization problem at the next moment by using the local knee solutions and a global knee solution. The experimental results show the effectiveness of our design.

关键词： dynamic multi-objective optimization Knee solutions Transfer learning multi-criteria decision making

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Immigrants Strategy for dynamic multi-objective optimization 10

A Hybrid Immigrants Strategy for Dynamic Multi-objective Opt...

引用

10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Shi, Lulu Wu, Yan Zhou, Yan Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China Army Acad Border & Coastal Def Xian Shaanxi Peoples R China

ISBN: (纸本)9781538643624

dynamic multi-objective optimization problems exist in the real world widely, requiring the optimization algorithm that has the ability to trace the Pareto-optimal set as time goes on. They appeal many people's attention and are widely studied. The paper proposes a hybrid immigrants strategy to deal with dynamic multi-objective problems whose changes are undetected. Hybrid immigrants strategy contains two strategies. One is random immigrants strategy which can help to maintain population diversity. Another is information guided immigrants strategy which can improve the convergence speed by creating possible better solutions according to the guiding direction of the centers between current generation and previous one generation. The proposed algorithm is assessed on some different kinds of test instances. Empirical results show the efficiency and outperformance of our algorithm in maintaining diversity and tracking the Pareto optimal fronts.

关键词： dynamic multi-objective optimization hybrid immigrants information guided immigrants undetected changes

来源：评论

学校读者我要写书评

暂无评论

Improved Population Prediction Strategy for dynamic multi-objective optimization Algorithms Using Transfer Learning

Improved Population Prediction Strategy for Dynamic Multi-Ob...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Liu, Zhening Wang, Handing Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781728183923

Many real-world optimization problems have dynamic multiple objectives and constrains, such problems are called dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have been proposed to solve DMOPs, how to effectively track the optimal solutions in dynamic environments is still a major challenge for dynamic multi-objective optimization. Two classical DMOEAs, population prediction strategy (PPS) and transfer learning based DMOEA (Tr-DMOEA) are validated to have great performance because they integrate machine learning mechanism for optimization. However, there are still some disadvantages in both algorithms. In this paper, we propose a combined algorithm to make up the respective disadvantages of PPS and Tr-DMOEA. Our algorithm retains the prediction method of PPS considering sufficient historical information. Then, we improve the prediction strategy in Tr-DMOEA to further modify the solutions provided by PPS. These modified solutions finally construct the initial population for optimization in the new environment. The experiment results indicate that our algorithm has the overall best performance comparing with PPS and Tr-DMOEA on the test problems.

关键词： dynamic multi-objective optimization evolutionary algorithm transfer learning

来源：评论

学校读者我要写书评

暂无评论

Prediction Strategy Assisted by LSTM Neural Network for dynamic multi-objective optimization 6

Prediction Strategy Assisted by LSTM Neural Network for Dyna...

引用

6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Li, Suixun Zou, Feng Chen, Debao Huang, Huimei Huaibei Normal Univ Sch Phys & Elect Informat Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

dynamic multi-objective optimization problems (DMOPs) exist widely in real life, and the algorithm needs to track changing Pareto optima accurately and quickly after the environment changes when dealing with these DMOPs. In response to the above issues, this paper proposes a prediction strategy assisted by long short-term memory recurrent neural network (PS-LSTM) for DMOPs. In the proposed method, when environmental changes occur, the difference of the center point of Pareto solutions is predicted by long short-term memory recurrent neural network and then the evolutionary direction of Pareto solutions can be calculated to better track changing Pareto optima by selecting excellent individuals in a new environment. Moreover, the next initial population is predicted based on the difference of the center point and the evolutionary direction of Pareto solutions. Finally, the PS-LSTM strategy is compared with four representative strategies on 11 benchmark test problems. Experimental results indicate that the strategy has better performance.

关键词： dynamic multi-objective optimization LSTM New environmental information

来源：评论

学校读者我要写书评

暂无评论

An Evolutionary dynamic multi-objective optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance 8

An Evolutionary Dynamic Multi-objective Optimization Algorit...

引用

8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Zhou, Jianwei Zou, Juan Yang, Shengxiang Ruan, Gan Ou, Junwei Zheng, Jinhua Xiangtan Univ Sch Informat Engn Xiangtan 411105 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

ISBN: (纸本)9781538692769

dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems. the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.

关键词： dynamic multi-objective optimization autonomous guidance

来源：评论

学校读者我要写书评

暂无评论

Evolutionary dynamic multi-objective optimization via Regression Transfer Learning

Evolutionary Dynamic Multi-objective Optimization via Regres...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Wang, Zhenzhong Jiang, Min Gao, Xing Feng, Liang Hu, Weizhen Tan, Kay Chen Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728124858

dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity.

关键词： evolutionary algorithm dynamic multi-objective optimization transfer learning regression prediction

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共19页 << < 3 4 5 6 7 8 9 10 11 12 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：