scholarly journals Research on Intellectualized Location of Coal Gangue Logistics Nodes Based on Particle Swarm Optimization and Quasi-Newton Algorithm

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 162
Author(s):  
Shengli Yang ◽  
Junjie Wang ◽  
Ming Li ◽  
Hao Yue
Keyword(s):  
Objective Function ◽  
Coal Mine ◽  
Underground Mining ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Transportation System ◽  
Coal Gangue ◽  
Location Selection ◽  
Newton Algorithm ◽  
Quasi Newton

The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed.

Neural Network Assisted Particle Swarm Optimization of Machining Process

2013 ◽  
Vol 581 ◽  
pp. 511-516
Author(s):  
Uros Zuperl ◽  
Franci Cus
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Machining Process ◽  
Neural Network Modeling ◽  
Swarm Optimization ◽  
Turning Operation ◽  
Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

scholarly journals Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

scholarly journals Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2016 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies the certain constraint condition and causes the largest prediction error at the prediction time. The CNOP method has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that: (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model on the prediction variable will lead to failure of the ADJ-CNOP method; (2) when the objective function has multiple extreme values, ADJ-CNOP has large probability to produce local CNOPs, hence make false estimation on the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithms, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. Besides, to check the estimation accuracy of the LBMPT presented by the PSO-CNOP and the ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes, and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Optimal location of emergency stations in underground mine networks using a multiobjective mathematical model

2018 ◽  
Vol 25 (4) ◽  
pp. 264-272 ◽  
Author(s):  
Reza Lotfian ◽  
Mehdi Najafi
Keyword(s):  
Mathematical Model ◽  
Coal Mine ◽  
Emergency Services ◽  
Underground Mining ◽  
Optimal Location ◽  
Underground Mine ◽  
Underground Mines ◽  
Underground Coal Mine ◽  
Location Selection ◽  
The Right

Background Every year, many mining accidents occur in underground mines all over the world resulting in the death and maiming of many miners and heavy financial losses to mining companies. Underground mining accounts for an increasing share of these events due to their special circumstances and the risks of working therein. Thus, the optimal location of emergency stations within the network of an underground mine in order to provide medical first aid and transport injured people at the right time, plays an essential role in reducing deaths and disabilities caused by accidentsObjective The main objective of this study is to determine the location of emergency stations (ES) within the network of an underground coal mine in order to minimize the outreach time for the injured.Methods A three-objective mathematical model is presented for placement of ES facility location selection and allocation of facilities to the injured in various stopes.Results Taking into account the radius of influence for each ES, the proposed model is capable to reduce the maximum time for provision of emergency services in the event of accident for each stope. In addition, the coverage or lack of coverage of each stope by any of the emergency facility is determined by means of Floyd-Warshall algorithm and graph. To solve the problem, a global criterion method using GAMS software is used to evaluate the accuracy and efficiency of the model.Conclusions 7 locations were selected from among 46 candidates for the establishment of emergency facilities in Tabas underground coal mine.

scholarly journals Improved Wireless Sensor Location Algorithm Based on Combined Particle Swarm-Quasi-Newton with Threshold N

2018 ◽  
Vol 14 (05) ◽  
pp. 31
Author(s):  
Hongqiang Zhang ◽  
Chunhong Wang
Keyword(s):  
High Precision ◽  
Particle Swarm ◽  
New Method ◽  
Wireless Sensor ◽  
Sensor Location ◽  
Font Size ◽  
Newton Algorithm ◽  
Position Errors ◽  
Location Algorithm ◽  
Quasi Newton

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">this paper drills down and systematically analyzes the improved DV-Hop algorithm about the origin of its position errors. A new method is hereby given for modifying such position errors. Beyond that, the particle swarm-quasi-Newton algorithm is improved, intercepting the combined DV-Hop location algorithm according to the number of known nodes which has been defined by a threshold N. The coordinate of unknown nodes is calculated and analyzed by the combined particle swarm-quasi-Newton algorithm. Then this paper makes a validation analysis on the results by the way of emulation. The results show that the improved algorithm is relatively superior with high precision and lower position error than improved DV-Hop as intercepted.</span>

Voltage Stability Enhancement with Optimal Placement of IPFC

2019 ◽  
Vol 8 (4) ◽  
pp. 9465-9471
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Test System ◽  
Optimal Location ◽  
Optimal Placement ◽  
Utilization Factor ◽  
Location Selection ◽  
Multi Objective

This paper presents a novel technique based on Cuckoo Search Algorithm (CSA) for enhancing the performance of multiline transmission network to reduce congestion in transmission line to huge level. Optimal location selection of IPFC is done using subtracting line utilization factor (SLUF) and CSA-based optimal tuning. The multi objective function consists of real power loss, security margin, bus voltage limit violation and capacity of installed IPFC. The multi objective function is tuned by CSA and the optimal location for minimizing transmission line congestion is obtained. The simulation is performed using MATLAB for IEEE 30-bus test system. The performance of CSA has been considered for various loading conditions. Results shows that the proposed CSA technique performs better by optimal location of IPFC while maintaining power system performance

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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