scholarly journals A Novel Parametric Model for Nonlinear Hysteretic Behaviors with Strain Stiffening of Magnetorheological Gel Based on Fruit Fly Optimization Algorithm

2021 ◽  
Author(s):  
Guang Zhang ◽  
Zheng Zhang ◽  
Min Sun ◽  
Yang Yu ◽  
Jiong Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Parametric Model ◽  
Measure Data ◽  
Model Parameters ◽  
Fruit Fly Optimization Algorithm ◽  
Engineering Applications ◽  
Fruit Fly Optimization ◽  
Practical Engineering

Abstract Magnetorheological (MR) gel is a new branch of MR materials, which can overcome the phenomenon of particle agglomeration existing in MR fluid, thus improving the controllability of materials in engineering applications. In this paper, a novel parametric model for tracking the nonlinear hysteretic behaviors with strain stiffening of MR gel is constructed. The measure data in relative to the five current levels of 0A, 0.2A, 0.5A, 0.8A and 1A under the strain amplitude and frequency of 10% and 0.1Hz respectively are utilized to identify the parameters. The optimal solution for the model parameters is conduced employing the fruit fly optimization algorithm (FOA). The comparison study with two typical model such as Bouc-Wen model and viscoelastic-plastic model is conduced to to evaluate the effectiveness of the developed model. The model parameters are generalized with respect to the loading current, and the reliability of the generalized model is verified. The studies show that the proposed model can perfectly fit the strain stiffening and nonlinearity of sample, which can provide a theoretical basis for the semi-active control of MR gel in practical engineering applications.

Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression

2012 ◽  
Vol 614-615 ◽  
pp. 409-413 ◽  
Author(s):  
Zhi Biao Shi ◽  
Ying Miao
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Global Optimal Solution ◽  
Fruit Fly Optimization ◽  
Global Optimal

In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that the forecast results of FOA_SVR more accurate and the forecast speed is the fastest.

scholarly journals Optimization of Order-Picking Problems by Intelligent Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhong-huan Wu ◽  
Hong-jie Chen ◽  
Jia-jia Yang
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Order Picking ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Intelligent Optimization Algorithm ◽  
Random Weight ◽  
Weight Decrease ◽  
The Mathematical Model

To improve the efficiency of warehouse operations, reasonable optimization of picking operations has become an important task of the modern supply chain. For the purpose of optimization of order picking in warehouses, a new fruit fly optimization algorithm, particle swarm optimization, random weight, and weight decrease model are used to solve the mathematical model. Further optimization is achieved through the analysis of the warehouse shelves and screening of the optimal solution of the picking time. In addition, simulation experiments are conducted in the MATLAB environment through programming. The shortest picking time is found out and chosen as an optimized method by taking advantage of the effectiveness of these six algorithms in the picking optimization and comparing the data obtained under the simulation. The result shows that the optimization capacity of RWFOA is better and the picking efficiency is the best.

AFOA: An Adaptive Fruit Fly Optimization Algorithm with Global Optimizing Ability

2016 ◽  
Vol 25 (06) ◽  
pp. 1650032 ◽  
Author(s):  
Yiwen Zhang ◽  
Guangming Cui ◽  
Erzhou Zhu ◽  
Qiang He
Keyword(s):  
Optimization Algorithm ◽  
High Efficiency ◽  
Historical Memory ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Local Optimal Solution ◽  
Evolution Algorithms ◽  
Elitist Strategy

With the development of intelligent computation technology, the intelligent evolution algorithms have been widely applied to solve optimization problem in the real world. As a novel evolution algorithm, fruit fly optimization algorithm (FOA) has the advantages of simple operation and high efficiency. However, FOA also has some disadvantages, such as trapping into local optimal solution easily, failing to traverse the problem domain and limiting the universality. In order to cope with the disadvantages of FOA while retain it merits, this paper proposes AFOA, an adaptive fruit fly optimization algorithm. AFOA adjusts the swarm range parameter V dynamically and adaptively according to the historical memory of each iteration of the swarm, and adopts the more accurate elitist strategy, which is therefore very effective in both accelerating the convergence of the swarm to the global optimal front and maintaining diversity of the solutions. The convergence of the algorithm is firstly analyzed theoretically, and then 14 benchmark functions with different characteristics are executed to compare the performance among AFOA, PSO, FOA, and LGMS-FOA. The experimental results have shown that, AFOA algorithm is a new algorithm with global optimizing capability and high universality.

Adaptive chaos fruit fly optimization algorithm

2013 ◽  
Vol 33 (5) ◽  
pp. 1313-1316 ◽  
Author(s):  
Junying HAN ◽  
Chengzhong LIU
Keyword(s):  
Optimization Algorithm ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

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