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

2022 ◽  
Vol 135 ◽  
pp. 103488
Guoqi Ma ◽  
Binquan Wang ◽  
Shuzhi Sam Ge
2021 ◽  
Vol 2076 (1) ◽  
pp. 012007
Meiling Lang ◽  
Liang Ge

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.

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li

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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