A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization

Badem, Hasan; Baştürk, Alper; Çalışkan, Abdullah; Yüksel, Mehmet Emin

doi:10.1016/j.asoc.2018.06.010

Cited by 51 publications

(21 citation statements)

References 45 publications

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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

“…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%

“…(5) is higher than the f it(x i ) fitness value of the x i food source, x i food source is replaced with the v i food source and the trial counter trial i showing how many times the x i food source is not improved is set to zero. Otherwise, the same counter is incremented by one and its value is used to make a decision whether that food source is consumed or not [19][20][21][22][23].…”

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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“…Badem et al combined ABC and limited memory-based BFGS algorithms and used their new variant for training deep neural networks [22]. Badem et al also investigated the performance of their hybridized ABC algorithm on solving numerical benchmark problems [23].…”

Section: Introductionmentioning

confidence: 99%

Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

Aslan¹

2019

IJCIS

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Artificial Bee Colony (ABC) algorithm inspired by the intelligent source search and consumption characteristics of the real honey bees is one of the most powerful optimization techniques. Although the existing behaviours of the honey bees in standard ABC algorithm are capable of producing optimal or near optimal solutions for the vast majority of the problems, there are still some intelligent characteristics that are not modeled yet such as decision-making mechanism used by employed bees to determine when the dancing will be completed. In this study, a mechanism that adjusts the dancing durations of the employed bees according to the fitness values of the associated food sources is proposed and integrated to the standard ABC algorithm and its well-known variants. Experimental studies on a set of complex continuous numerical problems showed that the newly proposed dance scheduling approach significantly improved the search and consumption characteristics of the artificial bees of the standard ABC algorithm and its some variants.

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A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization

Cited by 51 publications

References 45 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

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

Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants

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