Ascent phase trajectory optimization for vehicle with multi-combined cycle engine based on improved particle swarm optimization

2017 ◽  
Vol 140 ◽  
pp. 156-165 ◽  
Author(s):  
Hongyu Zhou ◽  
Xiaogang Wang ◽  
Yuliang Bai ◽  
Naigang Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Phase Trajectory ◽  
Trajectory Optimization ◽  
Particle Swarm ◽  
Combined Cycle ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ascent Phase

scholarly journals Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mu Lin ◽  
Zhao-Huanyu Zhang ◽  
Hongyu Zhou ◽  
Yongtao Shui
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Flight Path Angle ◽  
Gaussian Perturbation

This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

Sign in / Sign up

Export Citation Format

Share Document