scholarly journals Selection of Best Alternative Process Plan in Automated Manufacturing Environment: An Approach Based on Particle Swarm Optimization

10.5772/5112 ◽  
2007 ◽  
Author(s):  
Felix T.S. ◽  
Manoj Kumar ◽  
Y. Dashor
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Automated Manufacturing ◽  
Manufacturing Environment ◽  
Swarm Optimization ◽  
Process Plan ◽  
Alternative Process ◽  
Selection Of

Location Selection of Multiple Logistics Distribution Center Based on Particle Swarm Optimization

2016 ◽  
pp. 651-658 ◽  
Author(s):  
Qingyu Zeng ◽  
Chengqi Li ◽  
Xiangbiao Wu ◽  
Shengjie Long ◽  
Zhuanzhou Zhang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Distribution Center ◽  
Swarm Optimization ◽  
Location Selection ◽  
Logistics Distribution ◽  
Selection Of

A metric to assist the selection of the particle swarm optimization parameters

2014 ◽  
pp. 105-109 ◽  
Author(s):  
C da Silva ◽  
W Saba ◽  
N Abe ◽  
A Passaro
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimization Parameters ◽  
Selection Of

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

scholarly journals Data Mining Optimization uses C4.5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses

2020 ◽  
Vol 874 ◽  
pp. 012024
Author(s):  
Ari Waluyo ◽  
Hendra Jatnika ◽  
Marlina Rahmi Shinta Permatasari ◽  
T Tuslaela ◽  
Indah Purnamasari ◽  
...  
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Location Selection ◽  
Selection Of

On the selection of the optimal topology for particle swarm optimization

2019 ◽  
Author(s):  
Ángel Arturo Rojas-García ◽  
Arturo Hernández-Aguirre ◽  
S. Ivvan Valdez
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Topology ◽  
Swarm Optimization ◽  
Selection Of
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