Multi-body dynamic evolution sequence-assisted PSO for interval analysis

Xuanlong Wu; Peng Zhong; Weihao Lin; Jin Deng; Xuanlong Wu; Peng Zhong; Weihao Lin; Jin Deng

doi:10.3934/math.20241504

AIMS Mathematics

2024, Volume 9, Issue 11: 31198-31216. doi: 10.3934/math.20241504

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Multi-body dynamic evolution sequence-assisted PSO for interval analysis

1.
State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China
2.
Yunnan Branch of China Academy of Machinery Co., Ltd

Received: 03 September 2024 Revised: 16 October 2024 Accepted: 18 October 2024 Published: 01 November 2024
MSC : 68M15, 68T37

To enhance the efficiency and accuracy of response analysis in practical multivariable complex engineering problems, we introduced a new interval analysis method—multi-body dynamic evolution sequence-assisted particle swarm optimization (DES-PSO) is introduced in this research. This method optimizes the heterogeneous comprehensive learning particle swarm optimization algorithm (HCLPSO) by incorporating a dynamic evolution sequence (DES), addressing the difficulty of HCLPSO in covering the search space, which makes this method suitable for solving multivariable interval analysis problems. The results of two numerical examples prove that both DES-PSO and HCLPSO can give the accurate upper and lower bounds of the response interval. Compared with HCLPSO, DES-PSO improves the computing speed by about 50%.
Citation: Xuanlong Wu, Peng Zhong, Weihao Lin, Jin Deng. Multi-body dynamic evolution sequence-assisted PSO for interval analysis[J]. AIMS Mathematics, 2024, 9(11): 31198-31216. doi: 10.3934/math.20241504

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Abstract

To enhance the efficiency and accuracy of response analysis in practical multivariable complex engineering problems, we introduced a new interval analysis method—multi-body dynamic evolution sequence-assisted particle swarm optimization (DES-PSO) is introduced in this research. This method optimizes the heterogeneous comprehensive learning particle swarm optimization algorithm (HCLPSO) by incorporating a dynamic evolution sequence (DES), addressing the difficulty of HCLPSO in covering the search space, which makes this method suitable for solving multivariable interval analysis problems. The results of two numerical examples prove that both DES-PSO and HCLPSO can give the accurate upper and lower bounds of the response interval. Compared with HCLPSO, DES-PSO improves the computing speed by about 50%.

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