Dynamic neighbourhood particle swarm optimisation algorithm for solving multi-root direct kinematics in coupled parallel mechanisms

The direct kinematics equations of coupled parallel mechanisms (PMs) can be transformed into nonlinear equation systems (NESs) with multiple roots. Obtaining the multiple roots of NESs in a single operation presents a significant numerical computation challenge. This paper proposes a dynamic neighbo...

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Bibliographic Details
Published in:Expert systems with applications Vol. 268; p. 126315
Main Authors: Wen, Shikun, Gharbi, Yassine, Xu, Youzhi, Liu, Xuefei, Sun, Yi, Wu, Xiaoyong, Lee, Heow Pueh, Che, Linxian, Ji, Aihong
Format: Journal Article
Language:English
Published: Elsevier Ltd 05.04.2025
Subjects:
Distance-based dynamic neighbourhood mechanism
Dynamic discrete crossover
Multi-stage velocity update mechanism
Nonlinear equation systems
Parallel mechanism
Parallel mechanism
Multi-stage velocity update mechanism
Distance-based dynamic neighbourhood mechanism
Nonlinear equation systems
Dynamic discrete crossover
ISSN:0957-4174
Online Access:Get full text
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Summary:The direct kinematics equations of coupled parallel mechanisms (PMs) can be transformed into nonlinear equation systems (NESs) with multiple roots. Obtaining the multiple roots of NESs in a single operation presents a significant numerical computation challenge. This paper proposes a dynamic neighbourhood particle swarm optimisation (DNPSO) algorithm to resolve this issue. The DNPSO algorithm incorporates four improvement strategies. First, a distance-based dynamic neighbourhood mechanism is introduced to create a suitable neighbourhood around particles based on their properties. Second, a multi-stage velocity update mechanism is introduced to balance local and global search capabilities. Then, a dynamic discrete crossover strategy is integrated into the lbestPSO algorithm to enhance population diversity. Finally, an external archiving strategy is introduced to conserve computational resources. Furthermore, 20 benchmark functions and four direct kinematics equations for coupled PMs with varying degrees of freedom (DOFs) are utilised to evaluate the performance of the DNPSO algorithm. The experimental results demonstrate that this method achieves higher success and root ratios compared with other methods, both for the benchmark functions and the direct kinematics equations of the PMs.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.126315