Search Results - (( _ relations ((bee algorithm) OR (bees algorithm)) ) OR ( data classification based algorithm ))*

Adaptive filtering of EEG/ERP through bounded range artificial Bee Colony (BR-ABC) algorithm by Ahirwal, M.K., Kumar, A., Singh, G.K.

“…In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). …”
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An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation by Alrosan, Ayat, Alomoush, Waleed, Norwawi, Norita, Alswaitti, Mohammed, Makhadmeh, Sharif Naser

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway by Ahmad Muhaimin, Ismail, Muhammad Akmal, Remli, Yee Wen, Choon, Nurul Athirah, Nasarudin, Norsyahidatul Nazirah, Ismail, Mohd Arfian, Ismail, Mohd Saberi, Mohamad

“…Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. …”
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Bee foraging behaviour techniques for grid scheduling problem by Alyaseri, Sana, Ku-Mahamud, Ku Ruhana

“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.These resources are collected together to make a huge computing power.Job scheduling problem is one of the key issues in grid computing and failing to look into grid scheduling results in uncompleted view of the grid computing.Achieving optimized performance of grid system, and matching application requirements with available computing resources, are the objectives of grid job scheduling.Bee colony approaches are more adaptive to grid scheduling due to high heterogeneous and dynamic nature of resources and applications in grid.These algorithms have shown encouraging results in terms of time and cost.This paper presents some resent research activities inspired by bee foraging behavior for grid job scheduling especially ABC and BCO approaches.Different original studies related to this area are briefly described along with their comparisons against them and results.The review summary of their derived algorithms and research efforts is done.…”
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics by Mohammad Jarrah, Mu'ath Ibrahim

“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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An artificial bee colony-based double layered neural network approach for solving quadratic Bi-level programming problems by Watada, J., Roy, A., Wang, B., Tan, S.C., Xu, B.

“…For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) algorithm, a Hopfield Network (HN), and a Boltzmann Machine (BM). …”
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Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone by Alaidi A.H., Der C.S., Leong Y.W.

“…The artificial bee colony (ABC) is a well-studied algorithm developed to solve continuous function optimization problems by Karboga and Akay in 2009. …”

Optimal distributed generation and load shedding scheme using artificial bee colony- hill climbing algorithm considering voltage stability and losses indices by Ali Abdallah, Ali Emhemed

“…The proposed solution is based on the optimization method developed from a combination of the Artificial Bee Colony and Hill Climbing algorithms (ABC-HC) to give the optimal placement and sizing of DG units to be deployed in the system. …”
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Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique by Lai V., Huang Y.F., Koo C.H., Ahmed A.N., Sherif M., El-Shafie A.

“…To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. …”

Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer by Ravindran, Nadarajan

“…This algorithm named JAABC5ROC is the enhancement of Artificial Bee Colony (ABC) variant, JA-ABC5 by combining with Rate of Change (ROC)\. …”
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Autonomous reconnaissance mission: development of an algorithm for collaborative multi robot communication by Ahmed, Mohiuddin, Khan, Md. Raisuddin, Billah, Md. Masum

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Identification of material properties of composite plates using Fourier-generated frequency response functions by Tam, Jun Hui, Ong, Zhi Chao, Lau, Chun Lek, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee

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Optimal timber transportation planning in tropical hill forest using bees algorithm by Jamaluddin, Jamhuri

“…This study proposed a multi-objective linear programming model with Bees algorithm (BA) to find an optimal cost TTP for extraction, forest road, and landing locations. …”
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Structural optimization of 4-DOF agricultural robot arm by Nurul Emylia Natasya Ahmad Zakey, Mohd Hairi Mohd Zaman, Mohd Faisal Ibrahim

“…This study studies various optimization algorithms to compare the performance of algorithms that can achieve the optimal length with minimum errors. …”
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A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting by Ibrahim K.S.M.H., Huang Y.F., Ahmed A.N., Koo C.H., El-Shafie A.

Artificial Bee Colony With Differential Evolution Algorithm For Feature Extraction And Selection Of Mass Spectrometry Data by Mohamed Yusoff, Syarifah Adilah

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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…Seven performance indexes were examined to evaluate the performance of the proposed Muskingum model integrated with IBA, with other models that were also based on the Muskingum Model with three-parameters but utilized different optimization algorithms. The results for the Wilson flood showed that the proposed model could reduce the Sum of Squared Deviations (SSD) value by 89%, 51%, 93%, 69%, and 88%, compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Pattern Search (PS) algorithm, Harmony Search (HS) algorithm, and Honey Bee Mating Optimization (HBMO), respectively. …”
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ABC-PSO for vertical handover in heterogeneous wireless networks by Goudarzi, Shidrokh, Hassan, Wan Haslina, Anisi, Mohammad Hossein, Soleymani, Seyed Ahmad, Sookhak, Mehdi, Khan, Muhammad Khurram, Hassan Abdalla Hashim, Aisha, Zareei, Mahdi

“…Numerical results demonstrate that the ABC-PSO algorithm comparing to the related work has lower cost and delay, higher available bandwidth and less number of handover.…”
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Intelligent Evolutionary Controller for Flexible Robotic Arm by Annisa, Jamali, Intan Z., Mat Darus

“…The controller algorithm has been formulated for trajectory planning control and vibration cancelation utilizing intelligent evolutionary algorithms such as Particle Swarm Algorithm and Artificial Bees Colony. …”
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An efficient multi join query optimization for relational database management system using swarm intelligence approaches by Alsaedi, Ahmed Khalaf Zager

“…In order to eschew trapping and slow coverage difficulties in the quest to discover the optimal QEP and slow query execution time, this work proposes a total of three optimization algorithm that are based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Two-Phase Artificial Bee Colony (TPAPC) to solve the optimization problem in RDBMS Framework. …”
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