An artificial bee colony approach for clustering

Zhang, Changsheng; Ouyang, Dantong; Ning, Jiaxu

doi:10.1016/j.eswa.2009.11.003

Cited by 326 publications

(124 citation statements)

References 16 publications

(22 reference statements)

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“…Results gained illustrated that ABC outer class the aforesaid algorithms in calculations of processing time required and the quality of solution [22]. Fig 1 represents the flow chart for ABC algorithm applied on data clustering.…”

Section: Abc Approach For Data Clusteringmentioning

confidence: 99%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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Data clustering is a widespread data compression, vector quantization, data analysis and data mining technique. The principle objective of data clustering is to make clusters (or groups) such that data having high degree of similarity is gathered in the same cluster while data having high degree of dissimilarity is gathered in the different clusters and plays a key role for users to organize, summarize, and steer the data adequately. In this work Artificial Bee Colony (ABC) algorithm is applied to different size datasets. Results clearly show that ABC when applied on small size datasets were more effective than those of large size datasets in terms of intra-cluster distance, computation cycles and time required to complete those cycles.

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Section: Abc Approach For Data Clusteringmentioning

confidence: 99%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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“…Chuang et al (2011) proposed a data clustering using chaotic particle swarm optimisation for clustering, uses a gauss chaotic map to adopt a random sequence with a random starting point as a parameter. In Zhang et al (2010), an artificial bee colony approach for clustering is proposed to solve clustering problems. In Bonab et al (2015), a hybrid data clustering approach using k-means and flower pollination algorithm is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to overcome k-means limitations many heuristic based clustering algorithms proposed, heuristic algorithms such as genetic algorithm (GA), tabu search, and simulated annealing (Zhang et al, 2010). Nowadays, many swarm intelligence algorithms are successfully applied to clustering such as BA, ABC, PSO, etc Zhang et al, 2010. Bees algorithm (BA) is a meta-heuristic optimisation algorithm that mimics the food foraging behaviour of honey bees.…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Using Levy Flight and Local Memory Bees Algorithm

Shatnawi

2017

IJBIDM

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Abstract:One of the most popular clustering algorithms is K-means cluster due to its simplicity and efficiency. Although clustering using K-means algorithm is fast and produces good results, it still has a number of limitations including initial centroid selection and local optima. The purpose of this research is to develop a hybrid algorithm that address k-means clustering limitations and improve its performance by finding optimal cluster centre. In this paper, Lévy-flights or Lévy motion is one of non-Gaussian random processes used to solve the initial centroid problem. Bees algorithm is a population-based algorithm which has been proposed to overcome the local optima problem, used along with its local memory to enhance the efficiency of K-means. The proposed algorithm applied to different datasets and compared with K-means and basic Bess algorithm. The results show that the proposed algorithm gives better performance and avoid local optima problem.Keywords: clustering; k-means; Lévy-flights; bees algorithm.Reference to this paper should be made as follows: Shatnawi, N.M. (2017) 'Data clustering using Lévy flight and local memory bees algorithm', Int.

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“…Due to the advantages of the ABC algorithm, such as its simple concept, easy implementation, and fewer control parameters, it has been researched and utilized to solve different kinds of optimization problems by researchers around the world since 2005, such as data clustering [21,22], training ANNs [23], the leaf-constrained minimum spanning tree problem [24], designing infinite impulse response filters [25], and designing the optimal parameters of a power system stabilizer [26].…”

Section: Overview Of the Abc Algorithmmentioning

confidence: 99%

Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules

Zamani

Bijami²,

Sheikholeslam³

2014

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, an auto-tuned fuzzy load frequency controller (FLFC)-based artificial bee colony (ABC) algorithm is developed to quench the deviations in the frequency and tie-line power due to load disturbances in an interconnected power system. Optimal tuning of membership functions (MFs) and fuzzy control rules is very important to improve the design performance and achieve a satisfactory level of robustness for a particular operation. In this work, to reduce the fuzzy system design effort and take large parametric uncertainties into account, a new systematic and simultaneous tuning method is developed for designing MFs and fuzzy rules. For this, the designing problem is restructured as an optimization problem and the ABC algorithm is employed to solve it. This newly developed method provides some advantages such as a flexible controller with a simple structure and easy algorithm. For the purpose of the proposed method's evaluation, the designed controller is applied to a 2-area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic proportional-integral controller.Simulation results show better operation and improved system parameters, such as the settling time and step response rise time, using the proposed approach, in the presence of system parameter variations.

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An artificial bee colony approach for clustering

Cited by 326 publications

References 16 publications

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Data Clustering Using Levy Flight and Local Memory Bees Algorithm

Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules

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