Cooperative particle swarm optimization

Liu, Huai-liang; Su, Ruijuan; Xu, Ruoning; Gao, Ying

doi:10.3724/sp.j.1087.2009.03068

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Aiming at these problems, many improved PSO algorithms are put forward. Multi-swarm Cooperative Particle Swarm Optimization [7], which is a multi-swarm improved algorithm with center communication. This algorithm divides the swarm into several parts, and different parts communicate and help with each other, reducing the possibility of getting into the local optimum.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

Yang

Zhao

Chen

et al. 2011

AMR

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Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling into local optimum caused by single population. It also includes the idea of perturbing the swarms beside the global best solution, which can escape from local optimum. Experiments show that the proposed algorithm MpPSO has better performance, better convergence and stability when comparing with the traditional and the recently improved particle swarm optimization.

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Section: Introductionmentioning

confidence: 99%

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

Yang

Zhao

Chen

et al. 2011

AMR

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Chaos Cooperative Particle Swarm Optimization Based Water Level Control for Nuclear Steam Generator

Sheng

2016

MATEC Web of Conferences

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Abstract. The Stability of SG (Steam Generator) water level plays an important role in the safety of nuclear power plants, but it is difficult to tune the parameters of water level PID controller. A proposed novel algorithm, CCPSO (chaos cooperative particle swarm optimization), is used for tuning PID controller parameters. The (chaos particle swarm optimization)CPSO algorithm has the ability to avoid falling into local minimum and the (cooperative particle swarm optimization)CPSO-S k has fast convergence in certain functions, so CCPSO algorithm is proposed to utilize the advantages of CPSO and CPSO-S k. Therefore, half of the particles are updated in the CPSO-S k , and the other half are updated in the CPSO. The information exchange of the optimal solutions obtained after the end of each iteration is the performance of CPSO-S k and CPSO collaboration.The simulation results: compared with the PID controller whose parameters are tuned by ZN method, CCPSO show smaller overshoot, better stability, and shorter adjustment time. The simulation results show that the proposed method is effective for tuning PID parameters.

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Co-evolutionary particle swarm optimization based on population entropy

Zhao

Wang

2008

2008 27th Chinese Control Conference

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Cooperative particle swarm optimization

Cited by 5 publications

References 7 publications

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

Chaos Cooperative Particle Swarm Optimization Based Water Level Control for Nuclear Steam Generator

Co-evolutionary particle swarm optimization based on population entropy

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