A Kind of Diminishing Step Fruit Fly Optimization Algorithm

2014 ◽  
Vol 487 ◽  
pp. 687-691 ◽  
Author(s):  
Jun Yan Hou ◽  
Bing Wang
Keyword(s):  
Optimization Algorithm ◽  
Early Stage ◽  
Dynamic Balance ◽  
Step Length ◽  
Learning Performance ◽  
Support Vector ◽  
Model Parameters ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Svm Model

Parameters of Support Vector Machine are playing an important part in learning performance and generalization capability. The randomness and blindness in selecting SVM model parameters artificially could be eliminated by using group intelligent optimizing algorithm. FOA is a kind of group intelligent optimization algorithm. It has some advantages such as global convergence, connotative parallelism and fast operating speed. However, its optimum efficiency is very sensitive to the length of fixed step. In the course of optimizing, if the step is oversize, it will have preferable global optimizing performance and weak local optimizing capability. On the contrary, if the step is undersize, the local optimizing capability would be powerful and it will have the most probability to lapse into local extreme value. Therefore, a kind of algorithm named Diminishing Step FOA is proposed, the step length minishes progressively along with the process of foraging. So that it would have preferable global optimizing capability in early stage and preferable local optimizing capability in later period. And then, a dynamic balance will be achieved between global and local optimizing capability. The experimental results show that the SVM model using DS-FOA has optimal forecast precision and effect.

scholarly journals Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2226 ◽  
Author(s):  
Ming-Wei Li ◽  
Jing Geng ◽  
Wei-Chiang Hong ◽  
Yang Zhang
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Strong Nonlinearity ◽  
Electric Load ◽  
Large Power ◽  
Fruit Fly Optimization

Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population’s diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level.

scholarly journals Support Vecztor Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

2019 ◽  
Author(s):  
Saeed Samadianfard ◽  
Salar Jarhan ◽  
Ely Salwana ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
River Flow ◽  
Mean Squared Error ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Lake Urmia ◽  
River Flows ◽  
Fruit Fly Optimization ◽  
River Flow Forecasting

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

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 Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement

2019 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Adrienn Dineva ◽  
Amir Mosavi ◽  
Narjes Nabipour ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Service Life ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Flexible Pavement ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Remaining Service Life ◽  
Non Destructive

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

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