scholarly journals Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

2020 ◽  
Vol 10 (17) ◽  
pp. 6031
Author(s):  
Zhi Yu ◽  
Xiuzhi Shi ◽  
Jian Zhou ◽  
Rendong Huang ◽  
Yonggang Gou
Keyword(s):  
Particle Swarm Optimization ◽  
Gaussian Process ◽  
Evaluation Method ◽  
Mean Squared Error ◽  
Particle Swarm ◽  
Predictive Performance ◽  
Support Design ◽  
Swarm Optimization ◽  
Roadway Stability ◽  
Gp Model

A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

scholarly journals A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2286
Author(s):  
Xiaoman Cao ◽  
Hansheng Yan ◽  
Zhengyan Huang ◽  
Si Ai ◽  
Yongjun Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evaluation Method ◽  
Technical Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Optimal Evaluation

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

scholarly journals An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kai Hu ◽  
Aiguo Song ◽  
Min Xia ◽  
Zhiyong Fan ◽  
Xiaoying Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Evaluation Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Image Filter ◽  
Composite Image ◽  
Inverse Transform ◽  
Weighted Factor ◽  
Denoised Image

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR).

A Complicated Transfer Design Method Based on Particle Swarm Optimization

2015 ◽  
Vol 729 ◽  
pp. 208-212
Author(s):  
Xiao Ling Yao ◽  
Yan Ni Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Design Process ◽  
Design Problem ◽  
Evaluation Method ◽  
Design Method ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Function Design ◽  
Transfer Function Design

Based on the simple transfer function design, this thesis presents a technology for complicated transfer function design. It converts complicated transfer function design problem into the fusing of several simple transfer function. The keystone is to formulate the transfer function fusing problem into searching for optimal fusing proportion, and to generate the new fusing proportion using a similarity evaluation method, which is based on expectation fitness. To a large extent, it simplifies the design process of complicated transfer function.

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