2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 2017
DOI: 10.1109/itcosp.2017.8303154
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Cited by 6 publications
(3 citation statements)
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“…23 To overcome PI tuning problems, a nature-inspired evolutionary particle swarm optimization (PSO) technique has been used by various authors. [24][25][26][27] The PSO algorithm technique [24][25][26][27] has been adopted for the outcome of PI controller gains at an optimal value. The PSO algorithm has the following advantages: the memory of particles can preserve information of superior particles 28 ; information can be shared with other particles 29 ; constructive cooperation can be made between neighboring particles; it is suitable for any design parameters and non-linear functions; it has less computational complexity and is easy to implement 30,31 in comparison with other evolutionary techniques.…”
Section: Introductionmentioning
confidence: 99%
“…23 To overcome PI tuning problems, a nature-inspired evolutionary particle swarm optimization (PSO) technique has been used by various authors. [24][25][26][27] The PSO algorithm technique [24][25][26][27] has been adopted for the outcome of PI controller gains at an optimal value. The PSO algorithm has the following advantages: the memory of particles can preserve information of superior particles 28 ; information can be shared with other particles 29 ; constructive cooperation can be made between neighboring particles; it is suitable for any design parameters and non-linear functions; it has less computational complexity and is easy to implement 30,31 in comparison with other evolutionary techniques.…”
Section: Introductionmentioning
confidence: 99%
“…When compared to traditional PI, simulation findings demonstrate that PI-PSO provides a significant improvement. [28] Describes a three-phase SAPF that uses DPC based on PSO to improve power quality. The gains of the proportional and integral controllers of the direct current connected voltage loop are tuned using particle swarm optimization in this work.…”
Section: Introductionmentioning
confidence: 99%
“…In 1995, Dr. Kennedy and Dr. Eberhart proposed a particle swarm optimization algorithm for the optimization of nonlinear functions [ 11 ]. The algorithm has the advantages of simple operation and easy implementation, and is widely used in the fields of neural network training, deep learning, and system optimization [ 12 , 13 , 14 ]. The operation of the algorithm requires some parameters such as inertia weight and the acceleration coefficient.…”
Section: Introductionmentioning
confidence: 99%