Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms

Aggarwal, Shruti; Singh, Paramvir

doi:10.1007/s10586-018-2262-4

Cited by 26 publications

(7 citation statements)

References 30 publications

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“…The K -means++ algorithm is based on the traditional K -means algorithm, which makes improvements to the initial clustering center selection; assuming that n manufacturing cell centers have been selected (0 < n < K ), then when selecting the first n + 1 manufacturing cell centers, the more distant points from the current n manufacturing cell center have a higher probability to be selected as the first n + 1 manufacturing cell centers. Additionally, it overcomes the effect of random selection of the initial clustering centers of the traditional K -means algorithm and effectively improves the clarity and efficiency of manufacturing cell classification [ 27 ]. The specific processes of the K -means++ algorithm for classifying the manufacturing cells of complex aerospace components are summarized as follows: Input: product-equipment matrix X = { x 1 , x 2 , ⋯, x n } and number of manufacturing cells K Output: K manufacturing cells both product processing families C j and j = 1,2, ⋯, n Step 1: we randomly select a sample of points from the product-equipment matrix as the cluster center of the current manufacturing cell m r .…”

Section: Layout Planning Methodsmentioning

confidence: 99%

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

Zhao

Jiang

Tian

et al. 2022

Computational Intelligence and Neuroscience

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To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7%, the utilization rate of the workshop area is improved by 5.2%, and the value-added rate of products is increased by 6.6%, which verifies the effectiveness and feasibility of the proposed model and method.

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Section: Layout Planning Methodsmentioning

confidence: 99%

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

Zhao

Jiang

Tian

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…The K-Means++ approach ensures that centroids are initialized at distant places, reducing the chance of empty clusters or numerous clusters linked to a single centroid. Because of this initialization stage, the centroids are equally distributed over the data space, reducing the possibility that the algorithm may become caught in a specific minimum [77]. K-Means++ and the regular K-Means method are essentially identical, with the exception of the initial step.…”

Section: K-means++mentioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

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Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

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“…Each cluster has a center, called the center of mass, and the k-value needs to be given. The k-means clustering algorithm proceeds as follows [51]:…”

Section: Scenario Analysis Of Electric Heatingmentioning

confidence: 99%

Potential Analysis and Optimal Management of Winter Electric Heating in Rural China Based on V2H Technology

Gao

Chen

et al. 2023

Sustainability

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In order to improve the air pollution problem in northern China in winter, coal-to-electricity (CtE) projects are being vigorously implemented. Although the CtE project has a positive effect on alleviating air pollution and accelerating clean energy development, the economic benefits of electric heating are currently poor. In this study, a system based on vehicle-to-home (V2H) and photovoltaic power generation that can effectively improve the benefits of CtE projects is proposed. First, a V2H-based village microgrid is proposed. The winter temperature and direct radiation of the Beijing CtE project area are analyzed. Extreme operating conditions and typical operating conditions are constructed for potential analysis. After that, a bi-layer optimization model for energy management considering travel characteristics is proposed. The upper layer is a village-level microgrid energy-dispatching model considering meeting the heating load demand, and the lower layer is a multi-vehicle energy distribution model considering the battery degradation. The results show that the distribution grid expansion capacity of the electric heating system based on V2H and PV generation is reduced by 45.9%, and the residents’ electricity bills are reduced by 68.5%. The consumption of PV can be completed. This study has effectively increased the benefits of electric heating in northern China during winter. This helps the CtE project to be further promoted without leading to large subsidies from the government and the State Grid.

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Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms

Cited by 26 publications

References 30 publications

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Potential Analysis and Optimal Management of Winter Electric Heating in Rural China Based on V2H Technology

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