scholarly journals Self-adapting control parameters in particle swarm optimization

2019 ◽  
Vol 83 ◽  
pp. 105653 ◽  
Author(s):  
Mewael Isiet ◽  
Mohamed Gadala
2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Zhenzhou An ◽  
Xiaoyan Wang ◽  
Xinling Shi

The sociological concept of family has been introduced in the particle swarm optimization (PSO) and the family PSO (FPSO) has been proposed, in which the particle swarm consisted of different families, each family consisted of different members, and there were different constraint relationships between family members. To further study the sensitivity of FPSO to the control parameters, this paper proposed a special model of FPSO and analyzed the convergence of FPSO theoretically. This model offered a new view to research the particle trajectory and divided the position sequence of particle into the even and odd subsequences. By mathematical analysis, the condition of two subsequences convergence was obtained and the related convergent theories and corollaries were proved. Simulations for benchmark functions showed that the convergence behavior of model and experimental results provided a valuable guideline for selecting control parameters.


Author(s):  
Shouyan Chen ◽  
Tie Zhang

Purpose The purpose of this paper is to reduce the strain and vibration during robotic machining. Design/methodology/approach An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state. Findings The proposed intelligent approach can significantly reduce robotic machining strain and vibration. Originality value The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.


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