检索结果-内蒙古大学图书馆

Explicable recommendation model based on a time-assisted knowledge graph and many-objective optimization algorithm

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第21期36卷 e8210-e8210页

作者： Zheng, Rui Wu, Linjie Cai, Xingjuan Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

Existing research on recommender systems primarily focuses on improving a single objective, such as prediction accuracy, often ignoring other crucial aspects of recommendation performance such as temporal factor, user satisfaction, and acceptance. To solve this problem, we proposed an explicable recommendation model using many-objective optimization and a time-assisted knowledge graph, which utilizes user interaction times within the graph to prioritize recommending recently frequently visited items and is further optimized using a many-objective optimization algorithm. In this model, the temporal weight of user actions at different times is first determined through a time decay function. Additionally, if a user clicks on the same item again, the current action's temporal weight is set to one. This strategy prioritizes recent user actions and frequently visited items, reflecting current interests and preferences better. Next, the created knowledge graph is used to create a list of potential recommendations. Embedding methods obtain the vectors for entities and relations in the path. These vectors, combined with the temporal weight of actions, quantify the explainability of user recommendations. Optimizing the rest of the recommendation performance with many objective algorithms while focusing on the user's recent frequent visits to the item. Finally, the outcomes of the research study indicate that, compared to other explicable recommended methods, our model, considering temporal factor, improved average accuracy by 11%, diversity by 1%, and explainability by 21% in the Useraction1 data set. Results in other data sets also indicate that the proposed model maintains accuracy, diversity, and novelty while enhancing explainability.

关键词： explainable recommendation knowledge graph many-objective optimization algorithm recommendation system time decay function

来源：评论

学校读者我要写书评

暂无评论

A Sharding Scheme-Based many-objective optimization algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第11期17卷 7650-7658页

作者： Cai, Xingjuan Geng, Shaojin Zhang, Jingbo Wu, Di Cui, Zhihua Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100864 Peoples R China Swinburne Univ Technol Comp Sci & Software Engn Hawthorn Vic 3122 Australia

While the industrial Internet of Things (IIoT) can support efficient control of the physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, in this article, we propose a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. Compared with other state-of-the-art many-objective optimization algorithms, MaOEA-DRP performs better on the DTLZ test suite. The simulation results demonstrate that our proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.

关键词： Blockchain Industrial Internet of Things Throughput optimization Scalability Load modeling Delays Blockchain industrial Internet of Things (IIoT) many-objective optimization algorithm privacy preservation scalability security

来源：评论

学校读者我要写书评

暂无评论

An many-objective optimization algorithm with Reference Point and Angle based on Non-Dominated Sorting Approach

International Core Journal of Engineering

引用

International Core Journal of Engineering 2024年第1期10卷 317-331页

作者： Suyu Yang Maowei He Hanning Chen

It is critical to improve convergence and diversity in many-objective evolutionary algorithms. It is found that NSGA-III uses reference points to improve the diversity of the population but the convergence is poor. An evolutionary many-objective optimization algorithm with reference point and angle based on non-dominated sorting approach (NA-NSGA-III) is proposed. It utilizes the core framework of NSGA-III. NA-NSGA-III uses a case-by-case discussion strategy to enhance the convergence of solutions, and uses the angular penalty distance method to improve algorithm performance. On 16 well-known benchmark problems, NA-NSGA-III outperforms four state-of-the-art algorithms.

关键词： many-objective optimization algorithm Non-Dominated Sorting Reference Point Case-by-case Discussion Angle Penalty Distance

来源：评论

学校读者我要写书评

暂无评论

Privacy protection based on many-objective optimization algorithm

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第20期31卷 e5342-e5342页

作者： Zhang, Jiangjiang Xue, Fei Cai, Xingjuan Cui, Zhihua Chang, Yu Zhang, Wensheng Li, Wuzhao Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Beijing Wuzi Univ Sch Informat Beijing Peoples R China Chinese Acad Sci State Key Lab Intelligent Control & Management Co Inst Automat Beijing Peoples R China Jiaxing Vocat Tech Coll Jiaxing Peoples R China

It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many-objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many-objective optimization problems (MaOPs) and compared with five state-of-the-art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.

关键词： achievement scalarizing function angle information index balanceable fitness estimation method many-objective optimization algorithm privacy protection

来源：评论

学校读者我要写书评

暂无评论

many-objective optimization-Based Task Scheduling in Hybrid Cloud Environments

引用

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2425-2450页

作者： Mengkai Zhao Zhixia Zhang Tian Fan Wanwan Guo Zhihua Cui The School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China The School of Electronic Information Engineering Taiyuan University of Science and TechnologyTaiyuan030024China

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize the hybrid ***,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other *** on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion ***,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model *** algorithm uses opposition-based learning to generate initial populations for faster ***,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search *** comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

关键词： Hybrid cloud environment task scheduling many-objective optimization model many-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

3MO-AHP: an inconsistency reduction approach through mono-, multi- or many-objective quality measures

引用

DATA TECHNOLOGIES AND APPLICATIONS 2022年第5期56卷 645-670页

作者： Floriano, Carla Martins Pereira, Valdecy Rodrigues, Brunno e Souza Univ Fed Fluminense Dept Prod Engn Niteroi RJ Brazil

Purpose - Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM). Design/methodology/approach - The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems. Findings - The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM;hence, the decision-maker should c

关键词： Analytic hierarchy process Inconsistency reduction many-objective optimization algorithm U-NSGA III

来源：评论

学校读者我要写书评

暂无评论

A many objective based feature selection model for software defect prediction

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第19期36卷 e8153-e8153页

作者： Mao, Qi Zhang, Jingbo Zhao, Tianhao Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many-objective SDPFS (MOSDPFS) model and the binary many-objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO-AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO-AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO-AR2 are tested on the NASA-MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity.

关键词： feature selection many-objective optimization algorithm software defect prediction swarm intelligent algorithm

来源：评论

学校读者我要写书评

暂无评论

A game theory based many-objective hybrid tensor decomposition for skin cancer prediction

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Cai, Jianghui Yang, Jinqian Wen, Jie Zhao, Haochen Cui, Zhihua Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China North Univ China Sch Comp Sci & Technol Taiyuan 030051 Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Peoples R China

The development of skin cancer can be influenced by the abnormal expression of certain microRNAs (miRNAs). Current prediction models for miRNA-skin cancer associations have difficulties in maintaining both accuracy and comprehensiveness. To construct a comprehensive and interpretable skin cancer prediction model, various advanced tensor decomposition methods are organically combined, and a many-objective hybrid tensor decomposition model is proposed. In addition, due to the high computational cost of tensor decomposition, a many-objective optimization algorithm based on game theory was designed to solve the model. The game theory was used to dynamically adjust the diversity and convergence of the population, alleviate the pressure of solution selection, and improve the performance of the algorithm. The performance of the proposed algorithm is tested on a benchmark, and the prediction results of the many-objective hybrid tensor decomposition model are evaluated by a fivefold test method, and a case study of the prediction results is also presented. Experimental findings reveal that the proposed model and algorithm enhance overall performance by approximately 5.3%, compared to current advanced models.

关键词： Game theory Tensor decomposition miRNA-skin cancer relationship prediction many-objective optimization algorithm many-objective hybrid tensor decomposition model

来源：评论

学校读者我要写书评

暂无评论

A many-objective evolutionary algorithm assisted by ideal hyperplane

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 84卷

作者： Zhang, Zhixia Shi, Xiangyu Zhang, Zhigang Cui, Zhihua Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

In many-objective optimization problems (MaOPs), balancing convergence and diversity while rapidly converging to the Pareto front is an arduous task for evolutionary algorithms. In addition, with the increase of the number of targets, the number of non-dominant solutions increases exponentially, and the individual selection pressure is insufficient. For this problem, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). To begin, the ideal hyperplane is built from the extremums of each dimension of objective space, guiding the individual to the Pareto front of search. Second, a parallel p-norm mating selection strategy based on the ideal hyperplane is proposed to improve convergence. In addition, two other factors are taken into account: (1) different p-norms are used to measure different spatial scales;and (2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Following that, the sum of objectives is applied to shift-based density estimation, which serves as an evaluation criterion in the environmental selection operation. This method increases the chances of solutions with high convergence and diversity entering the next generation, thereby increasing selection pressure. On three benchmark problems of DTLZ, WFG, and MaF, we compare MaOEA-IH with seven excellent algorithms. The results demonstrate that the MaOEA-IH proposed is highly competitive in solving MaOPs.

关键词： Evolutionary computations Hyperplane many-objective optimization algorithm p-norm measure Selection pressure

来源：评论

学校读者我要写书评

暂无评论

Cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithm

引用

INFORMATION SCIENCES 2023年第1期647卷

作者： Zhang, Zhixia Wang, Hui Zhang, Wensheng Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing Peoples R China

It is critical to maintain significant convergence and diversity in many-objective optimization problems (MaOPs) for the performance of many-objective evolutionary algorithms (MaOEAs). However, some issues pose serious challenges to MaOEAs, such as the intensification of the conflict between convergence and diversity, and the lack of Pareto selection pressure. To address the above issues, we propose a cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithms (MaOEA-GM). The algorithm is divided into two stages, such as competition and cooperative. In competitive game stage, a strategy pool is constructed, including angle penalty distance strategy and favorable convergence strategy. In addition, a new game utility function is designed to balance convergence and diversity. This method promotes the selection of genetically superior parents for inheritance, so that the population can quickly approach the true Pareto front. In cooperative game stage, individuals choose their preferred environmental selection mechanism by voting. The final scheme is determined using the criterion of minority obedience to the majority, thereby increasing the algorithm Pareto selection pressure. Experimental results demonstrate that compared with five advanced MaOEAs, The MaOEA-GM algorithm has not only preponderance in convergence and diversity indicators, but also higher competitiveness in solving MaOPs.

关键词： many-objective optimization algorithm Cooperative game Non-cooperative game Evolutionary algorithm

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

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

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：