A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

Cui, Zhihua; Xue, Zhaoyu; Fan, Tian; Cai, Xingjuan; Zhang, Wensheng

doi:10.1016/j.swevo.2023.101244

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…The scheduling problem [8,9] occurs in various areas of the real world [10,11], so it has strong research value and practical significance [12,13]. The fast growth of science and technology has made the size of the Internet grow with each passing day [14,15], and the massive growth of scheduling tasks on the cloud has increased the load on cloud computing.…”

Section: Public Cloudmentioning

confidence: 99%

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

2023

KSII TIIS

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Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a largescale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

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Section: Public Cloudmentioning

confidence: 99%

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

2023

KSII TIIS

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“…The selection of relevant features can significantly improve the discriminative and generalization capabilities of the SDP model. In order to comprehensively consider the above four optimization objectives, this paper constructs a MOSDPFS model, and uses evolutionary optimization to solve it 22–25 …”

Section: Introductionmentioning

confidence: 99%

“…In order to comprehensively consider the above four optimization objectives, this paper constructs a MOSDPFS model, and uses evolutionary optimization to solve it. [22][23][24][25] Our contributions are summarized as follows:…”

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confidence: 99%

A many objective based feature selection model for software defect prediction

Mao,

Zhang,

Zhao

et al. 2024

Concurrency and Computation

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SummaryGiven 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.

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“…Compared with the traditional two-branch decision-making, the three-branch decision-making increases the steps of decision-making, but it reduces the difficulty of decision-making to a certain extent, and lays a solid foundation for making accurate decisions in the future. In a word, the theory of three branches of decision-making provides new ideas for the expansion of many fields and new research directions for countless scholars [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Three-Way Decision-Making Based on Incomplete Information System and Its Application

Xu,

Mao

2023

Fuzzy Systems and Data Mining IX

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In order to avoid decision makers from making wrong decisions as much as possible and reduce the losses caused by wrong decisions, a new aggregation method of interval loss function is proposed based on incomplete information system. Firstly, the missing values are filled according to their characteristics and combined with probability, and on this basis, the measurement method of similarity is given, and the horizontal similarity class of each object is obtained according to the similarity between objects. Secondly, according to the number of times each object in the similar class appears in its corresponding horizontal similar class, the corresponding weight is obtained; Then the interval loss function of similar class is defined as the aggregation of interval loss functions of all objects in similar class, and each interval is weighted according to the weight, so that multiple intervals in similar class are aggregated into an interval, and then the aggregated interval is converted into a single value by using the conversion function. Finally, according to the aggregation method, three decision rules are given to make decisions, and the effectiveness of the method is verified by a case.

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A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

Cited by 14 publications

References 41 publications

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

A many objective based feature selection model for software defect prediction

Three-Way Decision-Making Based on Incomplete Information System and Its Application

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