Artificial Bee Colony with Levy Flights for Parameter Estimation of 3-p Weibull Distribution

2020 ◽  
Vol 44 (3) ◽  
pp. 851-864 ◽  
Author(s):  
Aynur Yonar ◽  
Nimet Yapici Pehlivan
Keyword(s):  
Parameter Estimation ◽  
Weibull Distribution ◽  
Artificial Bee Colony ◽  
Lévy Flights ◽  
Bee Colony ◽  
Levy Flights

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

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.

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