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

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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

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

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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

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Privacy protection based on many-objective optimization algorithm

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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

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

International Core Journal of Engineering

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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

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

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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

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

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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

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A many-objective integrated evolutionary algorithm for feature selection in anomaly detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第22期32卷 e5861-e5861页

作者： Zhang, Zhixia Xie, Liping Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China

At present, irrelevant or redundant features in network traffic data occupy a lot of storage and computing resources, reducing the accuracy of network anomaly detection. Aiming at this problem, a many-objective feature selection model is proposed in this article. The model takes the number of selected feature, false alarm rate, detection rate, precision and accuracy as the optimization objectives, and characterizes the performance of the feature selection method from different perspectives. At the same time, a many-objective integration optimization algorithm (IN-MaOEA) is designed to solve this model. First, the algorithm will build the evolution strategy pool and the dominance strategy pool, then a random probability strategy selection mechanism is designed to improve the algorithm's convergence and diversity. At the same time, an anomaly detection simulation was performed using the NSL-KDD dataset. Experimental results show that the IN-MaOEA algorithm can effectively improve the performance of detection.

关键词： feature selection many-objective optimization algorithm network anomaly detection

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Orthogonal Waveform With Multiple Diversities for MIMO Radar

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IEEE SENSORS JOURNAL 2018年第11期18卷 4462-4476页

作者： Chang, Guanghong Liu, Aijun Yu, Changjun Ji, Yonggang Wang, Yiming Zhang, Jie Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Weihai Sch Informat & Elect Engn Weihai 264209 Peoples R China State Ocean Adm Lab Marine Phys & Remote Sensing Inst Oceanog 1 Qingdao 266061 Peoples R China

For multiple-inputmultiple-output radar employing the advantage of virtual array, the general assumption of the perfect orthogonality between multiple waveforms is proved to be unachievable. Therefore, to achieve good orthogonality, transmission schemes employing frequency diversity or chirp modulation diversity have been extensively investigated. Inspired by the idea of "diversity means superiority," approach which integrates multiple diversities in a single waveform might be used to improve the orthogonality. In this paper, a novel waveform named discrete frequency and chirp-rate coding waveform (DFCCW) consisting of both frequency and chirp modulation diversities is proposed. Based on the auto-and cross-ambiguity functions of the DFCCW, we prove that the proposed approach is able to decrease the cross ambiguity peak values. To generate and optimize a set of Pareto-optimal DFCCWs, a many-objective optimization algorithm named nondominated sorting genetic algorithm with differential evolution is presented. Besides, the optimal criteria considering Doppler effects ensure that the DFCCW possess good performance in presence of moving targets. Numerical results show that the DFCCW achieves better orthogonality than the waveforms that utilize only single diversity type.

关键词： Multiple-input multiple-output radar orthogonal waveform frequency diversity chirp modulation diversity many-objective optimization algorithm

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

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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

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A many objective based feature selection model for software defect prediction

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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

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