scholarly journals Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
He Wang ◽  
Hongbin Liang ◽  
Lei Gao
Keyword(s):  
Parameter Estimation ◽  
Artificial Bee Colony ◽  
Flow Model ◽  
Nonlinear Least Squares ◽  
The Other ◽  
Practical Application ◽  
Unknown Parameters ◽  
Application Performance ◽  
Bee Colony ◽  
Grain Flow

An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM). To this end, an improved artificial bee colony (IABC) algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS), particle swarm optimization (PSO), and genetic algorithm (GA). The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.

scholarly journals An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

2020 ◽  
Vol 14 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Krittika Kantawong ◽  
Sakkayaphop Pravesjit
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Windows ◽  
Scatter Search ◽  
The Other ◽  
Routing Problem ◽  
Bee Colony ◽  
Problem Instances

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

scholarly journals Power Loss Minimization in Radial Distribution Systems with Simultaneous Placement and Sizing of Different Types of Distribution Generation Units Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 12 (3) ◽  
pp. 686-707
Author(s):  
Pavan Khetrapal ◽  
◽  
Jafarullakhan Pathan ◽  
Shivam Shrivastava ◽  
◽  
...  
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony Algorithm ◽  
Distribution Systems ◽  
Artificial Bee Colony ◽  
Economic Incentives ◽  
The Other ◽  
Loss Minimization ◽  
Bee Colony ◽  
Different Types ◽  
Distribution Generation

Poweraloss is considered as one of theaimportant indicators used toaquantify theaperformance of distributionsnetworks. Minimisation of power lossesawith integration of distributedsgeneration (DG) unitssin distributionisystems has gained significantimomentum due to the associateditechno – economic incentives. Inithis paper, a noveliImproved Artificial Bee ColonyiAlgorithm (IABC) is developed toirobustly detect the optimalisite and sizedof DG units for minimisation of total poweralosses without violatingathe equality anddinequality constraints. Theiproposed algorithm is simulated in MATLABienvironment, and theieffectiveness of theialgorithm is validatedron IEEE – 34 bus andtIEEE – 69 bus radialrdistribution systems. Therperformance of thetproposed techniquerhas been validated by comparingsthe results obtained fromsother competesalgorithms. Comparisons showithat the proposed technique is moreiefficient in terms of simulationiresults of power loss andiconvergence propertyithan the other reportedialgorithms, suggesting that theisolution obtained is a globalioptimum.

scholarly journals A Filtering Method for Grain Flow Signals Using EMD Thresholds Optimized by Artificial Bee Colony Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 575 ◽  
Author(s):  
He Wang ◽  
Hua Song
Keyword(s):  
Artificial Bee Colony ◽  
Noise Signal ◽  
Threshold Function ◽  
Neighborhood Search ◽  
Intrinsic Mode Functions ◽  
Abc Algorithm ◽  
Filtering Method ◽  
Flow Signal ◽  
Bee Colony ◽  
Grain Flow

For the purpose of reducing noise from grain flow signal, this paper proposes a filtering method that is on the basis of empirical mode decomposition (EMD) and artificial bee colony (ABC) algorithm. At first, decomposing noise signal is performed adaptively into intrinsic mode functions (IMFs). Then, ABC algorithm is utilized to determine a proper threshold shrinking IMF coefficients instead of traditional threshold function. Furthermore, a neighborhood search strategy is introduced into ABC algorithm to balance its exploration and exploitation ability. Simulation experiments are conducted on four benchmark signals, and a comparative study for the proposed method and state-of-the-art methods are carried out. The compared results demonstrate that signal to noise ratio (SNR) and root mean square error (RMSE) are obtained by the proposed method. The conduction of which is finished on actual grain flow signal that is with noise for the demonstration of the effect in actual practice.

scholarly journals The Artificial Bee Colony (ABC) Algorithm for Estimating Parameter of Epidemic Influenza Model

2020 ◽  
Vol 10 (1) ◽  
pp. 29-36
Author(s):  
Ririn Nirmalasari ◽  
◽  
Agus Suryanto ◽  
Syaiful Anam
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Error Function ◽  
Dynamical Behavior ◽  
Graphical Analysis ◽  
Unknown Parameters ◽  
Abc Algorithm ◽  
Bee Colony ◽  
The Mean ◽  
Best Parameter

The Artificial Bee Colony (ABC) is one of the stochastic algorithms that can be applied to solve many real-world optimization problems. In this paper, The ABC algorithm was used to estimate the parameter of the epidemic influenza model. This model consists of a differential system represented by variations of Susceptible (S), Exposed (E), Recovered (R), and Infected (I). The ABC processes explore the minimum value of the mean square error function in the current iteration to estimate the unknown parameters of the model. Estimating parameters were made using participation data containing influenza disease in Australia, 2017. The best parameter chosen from the ABC process matched the dynamical behavior of the influenza epidemic field data used. Graphical analysis was used to validate the model. The result shows that the ABC algorithm is efficient for estimating the parameter of the epidemic influenza model.

scholarly journals A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 865 ◽  
Author(s):  
Diego Oliva ◽  
Ahmed A. Ewees ◽  
Mohamed Abd El Aziz ◽  
Aboul Ella Hassanien ◽  
Marco Peréz-Cisneros
Keyword(s):  
Parameter Estimation ◽  
Artificial Bee Colony ◽  
Photovoltaic Cells ◽  
Bee Colony
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