Optimization Problem Research for Machining Accuracy and Cost of CNC Lathe Based on Adaptive Particle Swarm Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 1448-1452
Author(s):  
Wei Wei Liu ◽  
Ye Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Manufacturing Industry ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Machining Accuracy ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Cnc Lathe ◽  
Relationship Of

Based on CNC lathe product’s technical and economic properties demand for manufacturing industry of CNC equipment, this thesis studied the mutual relationship of machining accuracy and cost. According to adaptive particle swarm optimization, a optimization model of the two parts was established. The case of a CNC lathe shows that the optimization model has the advantages of rapid searching, high accuracy, effective and provides a new way for the machining accuracy and cost optimization.

scholarly journals Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yaxiong Li ◽  
Xinglong Sun ◽  
Xinxue Liu ◽  
Jian Wu ◽  
Qingguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Result ◽  
Optimization Models ◽  
Time Interval ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Fuzzy Adaptive

On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

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.

scholarly journals Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

2021 ◽  
Vol 16 ◽  
pp. 155892502110223
Author(s):  
Jie Xu ◽  
Feng Liu ◽  
Zhenglei He ◽  
Zongao Zhang ◽  
Sheng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Sodium Hypochlorite ◽  
Production Cost ◽  
Orthogonal Experiment ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Process Variables ◽  
Swarm Optimization ◽  
Washing Process ◽  
Ensemble Of Surrogates

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

Sign in / Sign up

Export Citation Format

Share Document