scholarly journals Application of particle swarm optimization theory in optoelectronic payload installation error correction

2017 ◽  
Author(s):  
Liang Huang ◽  
Pengfei Peng ◽  
Bing Luo ◽  
Chu He
Keyword(s):  
Particle Swarm Optimization ◽  
Error Correction ◽  
Particle Swarm ◽  
Optimization Theory ◽  
Swarm Optimization

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

2021 ◽  
pp. 100868
Author(s):  
Essam H. Houssein ◽  
Ahmed G. Gad ◽  
Kashif Hussain ◽  
Ponnuthurai Nagaratnam Suganthan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Theory ◽  
Theory Analysis ◽  
Swarm Optimization

scholarly journals Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

2017 ◽  
Author(s):  
Minlan Jiang ◽  
Lan Jiang ◽  
Dingde Jiang ◽  
Fei Li ◽  
Houbing Song
Keyword(s):  
Particle Swarm Optimization ◽  
Measurement Error ◽  
Error Correction ◽  
Optimization Algorithm ◽  
Measurement Errors ◽  
Particle Swarm ◽  
Dynamic Measurement ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

Research of Particle Swarm Optimization Algorithm and its Applications in Optimization Theory

2014 ◽  
Vol 926-930 ◽  
pp. 3175-3178 ◽  
Author(s):  
Yan Hui Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Theory ◽  
Simplex Algorithm ◽  
Swarm Optimization ◽  
Nonlinear Characteristics ◽  
Effective Particle ◽  
Long Time

As everyone knows, mathematics is the basis of the optimization technology. With the development and progress of the society for a long time, there are many complicated practical problems during people’s research as well as some binding and nonlinear characteristics. If people still take the traditional optimization ways, such as Newton method and the simplex algorithm, they can not get rid of these problems effective particle swarm optimization algorithm and its applications in optimization theory in order to provide some useful references.

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