A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences

Karaboğa, Derviş; Aslan, Selçuk

doi:10.4238/gmr.15028645

Cited by 26 publications

(15 citation statements)

References 18 publications

(45 reference statements)

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“…When the food source becomes valueless for exploiting no longer, the bees which previously worked at these sources become the scout bees. The scout bees search for the new food sources as randomly Karaboga and Akay (2009), Karaboga and Basturk (2007), Badem et al(2018) Badem et al (2017), Karaboga and Aslan (2016), Karaboga and Aslan (2018), Karaboga and Basturk (2008). The main motivations of the Artificial Bee Colony (ABC) optimization algorithm are the mentioned clever foraging behavior and communication mechanism between honey bees.…”

Section: The Abc Algorithmmentioning

confidence: 99%

“…The amount of the nectar of the food source is also represented to the solution of the fitness value. The employed, scoot and onlooker bees cooperate to optimize the food sources by the iterative manner in the ABC algorithm Karaboga and Akay (2009), Karaboga and Basturk (2007), Badem et al (2017), Karaboga and Aslan (2016), Karaboga and Aslan (2018), Badem et al (2018), Karaboga and Basturk (2008). The fundamental steps of ABC algorithm are presented in Fig.…”

Section: The Abc Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of the Pool Boiling Heat Transfer in the Region of the Isolated Bubbles using the ABC Algorithm

Alic¹,

Çermik²,

Tokgöz³

et al. 2019

JAFM

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The region of the isolated bubble regime, in which the bubbling starts, is a very significant process for boiling heat transfer. In this study, Artificial Bee Colony algorithm (ABC), which is mainly based on the searching optimum foods sources for the bees, has been used for the optimization of the pool boiling heat transfer calculation. The ABC algorithm is very handy for numerical analysis. The ABC algorithm has been compared with a genetic algorithm as well as other well-known correlation models for pool boiling heat transfer calculation. The ABC algorithm is found to be useful for any boundary conditions. The boundary conditions have been changed in order to improve the results. Results show that the ABC algorithm works faster than the genetic algorithm for the given problem. The ABC algorithm also predicts less average absolute error when compared with other well-known correlations as well as the optimization using the genetic algorithm.

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Section: The Abc Algorithmmentioning

confidence: 99%

Section: The Abc Algorithmmentioning

confidence: 99%

Optimization of the Pool Boiling Heat Transfer in the Region of the Isolated Bubbles using the ABC Algorithm

Alic¹,

Çermik²,

Tokgöz³

et al. 2019

JAFM

View full text Add to dashboard Cite

show abstract

“…One such discrete optimisation problem is the workplace flow scheduling problem, for which models for dynamic clustering processes in different types of datasets have been presented [20][21][22][23][24][25]. In addition, next-generation problems such as cloud service composition, DNA sequencing, and DNA three-dimensional structure prediction problems are present in the literature [26][27][28].…”

Section: Artificial Bee Colony (Abc) Algorithm For Aspmentioning

confidence: 99%

Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Özmen

Batbat

Özen

et al. 2018

Mathematical Problems in Engineering

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Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD) program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC) algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS). Specifically, the assembly sequences of three-dimensional (3D) parts prepared in the computer-aided design (CAD) software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

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“…It should be noted that v i is same with the x i food source except the jth parameter. x ij and x kj are the jth parameters of the x i and x k solutions, respectively [19][20][21][22][23]. Finally, θ is a random coefficient between −1 and 1.…”

Section: Abc Algorithm and Its Adaptation To Sensor Deployment Problemmentioning

confidence: 99%

Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms

Aslan

2018

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Increasing number of cores in a processor chip and decreasing cost of distributed memory based system setup have led to emerge of a new work theme in which the main concern focused on the parallelization of the well-known algorithmic approaches for utilizing the computational power of the current architectures. In this study, the performances of the conventional parallel and cooperative model based parallel Artificial Bee Colony (ABC) algorithms on the deployment problem related to the wireless sensor networks were investigated. The results obtained from the experimental studies showed that parallelized ABC algorithm with the cooperative model is capable of finding similar or better coverage ratios with the increased convergence speeds than its serial counterpart and parallelized implementation in which the emigrant is chosen as the best food source in the current subcolony.

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A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences

Cited by 26 publications

References 18 publications

Optimization of the Pool Boiling Heat Transfer in the Region of the Isolated Bubbles using the ABC Algorithm

Optimization of the Pool Boiling Heat Transfer in the Region of the Isolated Bubbles using the ABC Algorithm

Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms

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