Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data

Liu, Shihu; Liu, Yingjie; Yang, Chunsheng; Deng, Li

doi:10.3390/e24081154

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Te entropy weight approach is commonly used to objectively assign weights to indicators based on the data [18,19]. First, determine the entropy.…”

Section: Weight Assignment Based On the Entropy Weight Methodmentioning

confidence: 99%

A Bi-Level Optimization Model for Flexible Capacity Allocation with Coordinated Planning and Operation

Yin,

Bian,

Zhu

et al. 2023

International Transactions on Electrical Energy Systems

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Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power system. Using collaborative optimization during the flexibility resource planning stage can significantly improve the overall economics and flexibility. Therefore, a bilevel operation-planning joint optimization model for flexible capacity allocation is proposed in this paper. The aim is to optimize the annual total cost and flexibility of the system. The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. On this basis, the lower level simulates production to estimate daily operating costs. In addition, the model is solved iteratively using the nondominated sorting genetic algorithm-II (NSGA-II) and the linear programming method to obtain the Pareto solutions. Case studies are carried out based on a practical town area, and the results verify the validity and rationality of the proposed bilevel capacity allocation model.

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“…Te entropy weight approach is commonly used to objectively assign weights to indicators based on the data [18,19]. First, determine the entropy.…”

Section: Weight Assignment Based On the Entropy Weight Methodmentioning

confidence: 99%

A Bi-Level Optimization Model for Flexible Capacity Allocation with Coordinated Planning and Operation

Yin,

Bian,

Zhu

et al. 2023

International Transactions on Electrical Energy Systems

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show abstract

“…Thus, the complexity of solving problems can be reduced, and the efficiency of solving problems can be improved by introducing the idea of GrC [24][25][26][27][28]. GrC has been applied to uncertainty handling in decision analysis [29][30][31][32][33] and solving graph theory [34][35][36][37][38][39]. For SCCs problem solving, granulation strategies based on SCC correlations have been introduced.…”

Section: Introductionmentioning

confidence: 99%

A Granulation Strategy-Based Algorithm for Computing Strongly Connected Components in Parallel

He,

Xu,

Chen

et al. 2024

Mathematics

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Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms.

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“…Entropy is one of the important parameters characterizing the state of matter in thermodynamics, and its physical significance is to describe the internal chaos of the system [14][15][16][17][18][19][20][21][22]. Generally, the entropy is an extensive measurement in the physical systems.…”

mentioning

confidence: 99%

Complexity of networks: A structural heterogeneity approach

Liu

Tian

Huang

et al. 2023

EPL

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The complexity measurement of networks is one of the hot topics in complex networks field. How to accurately describe the complexity difference between networks is helpful to the study of network structure. This paper proposes a method for measuring the network’s complexity considering local and global structural heterogeneity from the perspectives of structural heterogeneity. This method introduces the k-orderneighbors to examine the local structural property. Besides, the standard deviation is used to evaluate the global structural heterogeneity. Based on this, the complexity measurement model is established. In order to testify the efficiency of the method, the ER random networks, BA scale-free networks and real networks (including four social networks and two infrastructure networks) are used for experiments. The results show that the proposed method is sensitive and can effectively describe the subtle difference of structure complexity between networks.

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Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data

Cited by 3 publications

References 30 publications

A Bi-Level Optimization Model for Flexible Capacity Allocation with Coordinated Planning and Operation

A Bi-Level Optimization Model for Flexible Capacity Allocation with Coordinated Planning and Operation

A Granulation Strategy-Based Algorithm for Computing Strongly Connected Components in Parallel

Complexity of networks: A structural heterogeneity approach

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