scholarly journals A Three-Dimensional Nonlinear Dynamic Numerical Optimization of the Risks of Stope Blasting Based on FOA-GRNN

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chengyu Xie ◽  
Jie Cao ◽  
Dongping Shi
Keyword(s):  
Nonlinear Dynamic ◽  
Mean Squared Error ◽  
Three Dimensional ◽  
Coupled Model ◽  
General Regression Neural Network ◽  
Vibration Velocity ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Hole Charge ◽  
Interval Ratio

The fruit fly optimization algorithm-general regression neural network (FOA-GRNN) coupled model and the Finite Element Method-Smoothed Particle Hydrodynamics (FEM-SPH) numerical calculation method are comprehensively used. The control problem of blasting vibration in the process of mining hidden resources under complex environmental conditions has been studied. Taking a lead-zinc mine as the engineering background, the development of hidden resources in the collapse area due to unreasonable mining was studied. Based on the establishment of the first mining stope and its mining method in this area, biosimulation and generalized neural networks were introduced to solve this problem, the coupling of blasting parameters was analyzed, and the 3D nonlinear dynamic coupling model was constructed for numerical simulation. The results show that the blasting parameters of deep-hole mining were optimized, including the values of six output quantities: hole distance, row spacing, side hole distance, explosive unit consumption, minimum resistance line, and interval ratio (the Root Mean Squared Error value is only 0.051). The error between the network optimization parameters and the empirically obtained values was controlled to within 0.05; five possible edge-hole charge structures were designed (the interval ratio is 0.696), and the vibration velocity peak and pressure peak variations with time after detonation are reflected by the simulation results. The dynamic evolution law of the rock mass velocity vector and the damage of the rock damage was revealed. According to the analysis in this paper, the smallest and optimal edge-hole charge structure of the surrounding rock was obtained.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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.

Prediction of microfibril angle in Dahurian larch wood using visible–near infrared spectroscopy and chemometric techniques

2019 ◽  
Vol 27 (4) ◽  
pp. 270-277
Author(s):  
Ying Li ◽  
Brian K Via ◽  
Qingzheng Cheng ◽  
Yaoxiang Li
Keyword(s):  
Neural Network ◽  
Stepwise Regression ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Coupled Model ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Generalized Regression

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis–generalized regression neural network coupled model. It was found that stepwise regression analysis–generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ([Formula: see text] = 0.90, RMSEP = 0.75, mean average percentage error ([Formula: see text]) = 0.05) outperforms original partial least squares model ([Formula: see text] = 0.86, RMSEP = 0.88, [Formula: see text] = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

scholarly journals Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm

2018 ◽  
Vol 14 (11) ◽  
pp. 202 ◽  
Author(s):  
Shaobo Li ◽  
Chenglong Zhang ◽  
Jinglei Qu
Keyword(s):  
Real Time ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Resource Location ◽  
Fruit Fly Optimization ◽  
Location Data ◽  
Manufacturing Resource ◽  
Manufacturing Resources

The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.

scholarly journals Cooperative 3D Path Planning of Multi-UAV Via Improved Fruit Fly Optimization

2021 ◽  
Author(s):  
Yongjie Mao ◽  
Deqing Huang ◽  
Na Qin ◽  
Lei Zhu ◽  
Jiaxi Zhao
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Solution Space ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multiple Uavs ◽  
Multi Uav

Abstract Path planning of multiple unmanned aerial vehicles (UAVs) is a crucial step in cooperative operation of multiple UAVs, whose main difficulties lie in the severe coupling of time and three-Dimensional (3D) space as well as the complexity of multi-objective optimization. For this purpose, the time stamp segmentation (TSS) model is first adopted to resolve the timespace coupling among multiple UAVs. Meanwhile, the solution space is reduced by transforming the multiobjective problem to a multi-constraint problem. In consequence, based on the elite retention strategy, a novel improved fruit fly optimization algorithm (NIFOA) is proposed for multi-UAV cooperative path planning, which overcomes the shortcomings of basic fruit fly optimization algorithm in slow convergence speed and the potentials to fall into local optima. In particular, the multi-subpopulations evolution mechanism is further designed to optimize the elite subpopulation. At last, the effectiveness of the proposed NIFOA has been verified by numerical experiments.

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