Robust optimal control of connected and automated vehicle platoons through improved particle swarm optimization

2022 ◽  
Vol 135 ◽  
pp. 103488
Author(s):  
Guoqi Ma ◽  
Binquan Wang ◽  
Shuzhi Sam Ge
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Robust Optimal Control ◽  
Automated Vehicle ◽  
Vehicle Platoons

scholarly journals The optimal control of semi-active suspension based on improved particle swarm optimization

2018 ◽  
Vol 4 (3) ◽  
pp. 157-163
Author(s):  
Haijing Yan ◽  
Jubin Qiao ◽  
Sen Zhang ◽  
Ting Zhao ◽  
Zhongchang Wang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Active Suspension ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals An Improved Particle Swarm Optimization algorithm for Optimal Control of ASP Flooding

2021 ◽  
Vol 2076 (1) ◽  
pp. 012007
Author(s):  
Meiling Lang ◽  
Liang Ge
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Oil Recovery ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Flow In Porous Media ◽  
Identification System ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Asp Flooding

Abstract An optimization for injection of alkali/surfactant/polymer (ASP) flooding in oil recovery is considered in this paper. This optimal control problem (OCP) is formulated as a parameter identification system, where the objective function is revenue maximization and the governing equation is multiphase flow in porous media. We use the ASP concentrations and slug size as the control and give the pointwise constraint for the control. An improved particle swarm optimization (IPSO) which is a particle swarm optimization (PSO) with second-order oscillatory in velocity, is applied to solve the OCP. Finally, an example of the OCP for ASP flooding is exposed and the results show that the IPSO method is effective and feasible.

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.

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