Bouc-Wen hysteresis parameter optimization for magnetorheological damper using Cuckoo search algorithm

2020 ◽  
Author(s):  
R. Rosli ◽  
M. Z. Mohamed ◽  
G. Priyandoko ◽  
M. F. F. A. Rashid
Keyword(s):  
Parameter Optimization ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Magnetorheological Damper ◽  
Hysteresis Parameter

Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

2020 ◽  
pp. 107754632095138
Author(s):  
Rosmazi Rosli ◽  
Zamri Mohamed
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Magnetorheological Damper ◽  
Size Factor ◽  
Mean Square

This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. An engineering optimization application was chosen to evaluate the performance of the algorithm in complex engineering applications. The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. The magnetorheological damper response was chosen as the objective function. The final value of the fitness function and the iteration number it took to converge were used as the qualifying indicator to the proposed cuckoo search algorithm efficiency. A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.

Application of Differential Evolution Cuckoo Search Algorithm in Parameter Optimization of VG Equation

2021 ◽  
Vol 35 (09) ◽  
pp. 2159033
Author(s):  
Bo Yuan ◽  
Deji Chen
Keyword(s):  
Soil Moisture ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Optimization Model ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Characteristic Curve ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Moisture Characteristic

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence.

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