WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Zhang, Xinliang; Zhou, Shibo

doi:10.1109/access.2023.3307412

Cited by 3 publications

(1 citation statement)

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“…In [38], the WOA algorithm was utilized to identify the control system parameters. In [39], a method based on the density-based spatial clustering of applications with noise and WOA (WOA-DBSCN) was proposed to select parameters for adaptive clustering. In [40], the WOA algorithm was utilized to solve the optimal reactive power allocation problem.…”

Section: Introductionmentioning

confidence: 99%

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Wang,

Zhou

2024

Electronics

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Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough set (NRS) is first utilized to simplify the redundant parameters of the controller to improve efficiency. Then, the methods of the Sobol sequence initialization population, nonlinear convergence factor, adaptive weight strategy, and random differential mutation strategy are introduced to improve the traditional whale optimization algorithm (WOA) algorithm. Finally, the improved whale optimization algorithm (IWOA) is utilized to optimize the remaining controller parameters. The simulation results show that the optimization parameters were reduced from 12 and 18 to 3 and 4 in the single-machine infinity bus system and dual-machine power system, and the optimization time was reduced by 74.5% and 42.8%, respectively. In addition, the proposed NRS-IWOA method exhibits more significant advantages in optimizing parameters and improving stability than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Wang,

Zhou

2024

Electronics

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Using clustering methods to analyze sales of auto parts at a truck service station

Evdokimova,

Novikova,

Novikov

2023

Modeling of Systems and Processes

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Clustering methods are widely used to divide goods into groups depending on sales volumes in order to build an optimal purchasing planning and inventory management strategy. Cluster analysis methods do not provide an unambiguous partition of the original set of objects, therefore, in the work, existing clustering methods were analyzed to study sales of auto parts at truck service stations. To solve the problem, the following methods were chosen: k-means, hierarchical agglomerative clustering and DBSCAN. Before using the k-means method, the elbow method found the optimal number of clusters. The DBSCAN method is based on object density and automatically determines the number of clusters. The initial data for cluster analysis was information on sales of spare parts at truck service stations for 3 years; clustering was applied to data by year. The DBSCAN algorithm showed unsatisfactory results, since most of the goods (86%) were identified in one cluster, while others contained units of goods. The k-means method gave the best partitioning result, each group has a different volume. The distribution of goods in clusters changes over three years, so managers should study the change in the affiliation of goods to one group or another. The obtained clustering results will help determine the real needs of spare parts at truck service stations and build an optimal procurement strategy.

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Identification of the mining accidents by a two-step clustering method for the mining-induced seismicity

Jian,

Zhao,

Wang

et al. 2024

Front. Earth Sci.

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Clustering methods aim to categorize data or samples into distinct groups based on their similarity. When applying clustering methods to earthquake events, it is crucial to establish a metric for quantifying the similarity between these events. Directly applying this clustering method to a catalog of mining-induced seismicity may lead to clustering earthquake events induced by different mining activities or accidents into the same group. To address this issue, a two-step clustering method has been proposed and applied for analyzing a catalog of mining-induced seismicity. The first step involves spatial distance-based clustering of seismic events, while the second step focuses on moment tensor analysis-based clustering of these events. The results obtained from the MT-based clustering method are visualized using Hudson Graphs, and box plots serve as an evaluation tool for assessing the quality of MT clustering. Most box plots demonstrate desirable quality in terms of MT cluster results, indicating successful outcomes. By the proposed two-step clustering method combined with actual mining activities, the potential accident locations and categories can be hypothesized while valuable recommendations provided for mining operations.

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WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Cited by 3 publications

References 40 publications

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Using clustering methods to analyze sales of auto parts at a truck service station

Identification of the mining accidents by a two-step clustering method for the mining-induced seismicity

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