An Optimized Artificial Bee Colony Algorithm for Clustering

Gong, An; Gao, Yun; Ma, Xingmin; Gong, Wenjuan; Li, Huayu; Gao, Zhen

doi:10.14257/ijca.2016.9.4.11

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 2 -Employed bees phase:IntheemployedbeesphaseoftheoriginalABC,anewfoodsourceis producedintheneighborhoodofthecurrentsolutionbychangingthevalueofasinglecomponent of the D-dimensional vector, which leads to low convergence of the algorithm (Gong et al, 2016).ToovercomethisproblemandachievebetterclusteringresultsbytheNBC-MOABC algorithm,newfoodsourcesaregeneratedbytheemployedbeestoexplorethesearchspace, wherethevalueoftheparameterkofasolutionjatthei th iterationisgeneratedintherangeof itsoldvalueusingthefollowingformula:…”

Section: The Main Steps Of the Algorithmmentioning

confidence: 99%

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

Boudane

Berrichi

2021

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

Although various clustering algorithms have been proposed, most of them cannot handle arbitrarily shaped clusters with varying density and depend on the user-defined parameters which are hard to set. In this paper, to address these issues, the authors propose an automatic neighborhood-based clustering approach using an extended multi-objective artificial bee colony (NBC-MOABC) algorithm. In this approach, the ABC algorithm is used as a parameter tuning tool for the NBC algorithm. NBC-MOABC is parameter-free and uses a density-based solution encoding scheme. Furthermore, solution search equations of the standard ABC are modified in NBC-MOABC, and a mutation operator is used to better explore the search space. For evaluation, two objectives, based on density concepts, have been defined to replace the conventional validity indices, which may fail in the case of arbitrarily shaped clusters. Experimental results demonstrate the superiority of the proposed approach over seven clustering methods.

show abstract

Section: The Main Steps Of the Algorithmmentioning

confidence: 99%

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

Boudane

Berrichi

2021

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

show abstract

“…In recent researches, authors presented data clustering algorithms by integrating the K-means data clustering algorithm with the population-based meta-heuristics algorithms, for example; Abdeyazdan presented an enhanced data clustering approach for that adopts the combination of the K-harmonic means algorithm (KHM) and a modified version of the Imperialist Competitive Algorithm (ICA) algorithm [43]. Gong et al presented an improved Artificial Bee Colony clustering algorithm by enhancing the initial clustering centres selection [44]. Mustafi et al presented an improved Genetic Algorithm (GA) data clustering algorithm to overcome the K-means clustering algorithm drawbacks [12].…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

et al. 2019

View full text Add to dashboard Cite

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

show abstract

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

2018

View full text Add to dashboard Cite

An Optimized Artificial Bee Colony Algorithm for Clustering

Cited by 6 publications

References 10 publications

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

Contact Info

Product

Resources

About