Dynamic Swarm Artificial Bee Colony Algorithm

Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Deep, Kusum

doi:10.4018/jaec.2012100102

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…x mutation ij the better one is chosen as a new food source, where b is a scaling parameter. The GABC method successfully applied to some complex optimization problems such as classification, clustering and numerical function optimizations function optimizations as well [28,29]. However, it cannot prove the effectiveness capabilities to get the exact desired optimal values for all given complex problems due to the same and random searching strategies used by employed and onlooker's bees sections.…”

Section: Global Artificial Bee Colony Search Algorithmmentioning

confidence: 99%

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

et al. 2018

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Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing.

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Section: Global Artificial Bee Colony Search Algorithmmentioning

confidence: 99%

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

et al. 2018

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“…Artificial Bee Colony algorithm applied in Distributed Environments by A. Banharnsakun et al [14]. Harish Sharma et al developed DSABC [15], Balanced ABC [16], MeABC [17], and LFABC [18]. S. Pandey et al developed a hybrid of ABC using crossover operation and applied it on TSP [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem

Kumar¹,

Sharma²,

Kumari³

2013

IJCA

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Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.

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“…K-means algorithm is the standout amongst the maximum widely recognized class of clustering algorithms (Selim & Alsultan, 1991) which is a fast, simple and center based algorithm.ThekeyworkingofK-meansalgorithmisthatitfindsoutthepartitionssothatthe squarederrorbetweenthepointsintheclusterandtheempiricalmeanofaclusterisreduced. Thisalgorithmhastheinsufficienciesthatitextremelyrelaysonthestartingconditionsand fromtheveryinitialpositionofsearch,convergestolocalminimaandwithreasonablequantity of computation effort it cannot find global solutions of large problems (Fathian, Amiri, & Maroosi,2007).Soastooverwhelmedlocaloptimaproblem,theresearchershavingvarious backgroundsofresearchareapplyingi.e.density-basedclustering,artificialintelligencebased clusteringmethods,partition-basedclusteringandhierarchicalclustering,forinstance:graph theory (Zahn,1971),statistics (Forgy,1965),expectation,evolutionaryalgorithms,artificial neural networks and swarm intelligence algorithms (Bakhta & Ghalem, 2014;Bouarara, Hamou,&Amine,2015;Cheng,Shi,&Qin,2011;Harish,JagdishChand,Arya,&Kusum, 2012;TarunKumar&Millie,2011). SimulatedAnnealingapproachhasbeendiscussedandprovedtheoreticallybySelimand Al-SultanthattheclusteringproblemofgettingstuckatlocalminimafacedbyK-meanscanbe resolved (Selim&Alsultan,1991).Thealgorithmdoesnot"stick"toalocaloptimalsolution, somewhatitobtainstheoptimumsolution.Adisadvantageofthesimulatedannealingapproach isthatnocharacterizationofanendingpointiscomputationallyoffered.Anotherdisadvantage isthatverifyingthatasetofdataisStandardDataismoredifficultthansolvingtheclustering problemitself.AnewalgorithmbasedonaTStechniqueisusedforsolvingthisproblem.For manytestproblemsthealgorithmaccomplishedpreferredoutcomesthanthefamousk-means andtheSAalgorithms (Al-Sultan,1995).…”

mentioning

confidence: 99%

Global Artificial Bee Colony Search Algorithm for Data Clustering

Danish

Shah

Tairan

et al. 2019

International Journal of Swarm Intelligence Research

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Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.

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Dynamic Swarm Artificial Bee Colony Algorithm

Cited by 10 publications

References 16 publications

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem

Global Artificial Bee Colony Search Algorithm for Data Clustering

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