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

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%.

Keywords

• interval analysis
• heterogeneous comprehensive learning particle swarm optimization algorithm
• dynamic evolution
• low discrepancy sequence