A Multi-population Coevolution Multi-objective Particle Swarm Optimization Algorithm

He, Jiawei; Zhang, Huifeng; Cui, Xingyu

doi:10.23919/ccc52363.2021.9549666

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…The particle swarm optimisation (PSO) algorithm is an intelligent optimisation algorithm based on the predatory behaviour of birds. Compared to other algorithms, it requires fewer parameters to be modified, is easy to implement, and is computationally efficient, and the best particles can be searched based on individual and global information [25], which plays an important role in the application of multiobjective optimisation. The optimisation process itself is as shown in Figure 9.…”

Section: Multiobjective Optimisation Based On Improved Particle Swarm...mentioning

confidence: 99%

Springback Control in Complex Sheet-Metal Forming Based on Advanced High-Strength Steel

Cao

et al. 2023

Coatings

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Sheet-metal forming is one of the most important manufacturing processes in the automotive industry. This study proposes a multiobjective optimisation scheme that controls both sheet-metal formability and springback. First, the mechanical properties of DP590 steel were characterised to obtain the mechanical parameters and forming limit diagram (FLD) of DP590. Then, the FLD, thinning rate, and average springback were selected as forming quality evaluation indices. Response surface tests were then conducted for different process parameters for the A-pillar side-stiffener drawing process to analyse the DP590 steel’s formability and springback. The optimal process parameters for the drawing process were obtained using a multiobjective optimisation algorithm based on an improved particle swarm method. Finally, a springback compensation scheme was proposed based on the results of the multiprocess simulation. The scheme was applied experimentally to the A-pillar side-stiffener drawing process, and the formability and springback compensation performances verified that the scheme successfully and efficiently eliminated springback and rupture in formed DP590 steel.

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Section: Multiobjective Optimisation Based On Improved Particle Swarm...mentioning

confidence: 99%

Springback Control in Complex Sheet-Metal Forming Based on Advanced High-Strength Steel

Cao

et al. 2023

Coatings

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Adaptive Weighted Grouping PSO Based Multi-Objective Optimization for Power Dispatch with Power Flow Limits

Zhang,

Tian

et al. 2023

2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)

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A Multi-population Coevolution Multi-objective Particle Swarm Optimization Algorithm

Cited by 2 publications

References 16 publications

Springback Control in Complex Sheet-Metal Forming Based on Advanced High-Strength Steel

Springback Control in Complex Sheet-Metal Forming Based on Advanced High-Strength Steel

Adaptive Weighted Grouping PSO Based Multi-Objective Optimization for Power Dispatch with Power Flow Limits

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