Improved particle swarm optimization algorithms for aerodynamic shape optimization of high-speed train. (November 2022)

• Record Type: Journal Article
• Authors: He, Zhao; Liu, Tanghong; Liu, Hui
• Is Part Of: Advances in engineering software. Volume 173(2022)
• Publication Date: 2022-11
• Subjects: Optimal LSSVR model -- Elite-evolved multi-objective particle swarm optimizer -- Hybrid particle swarm optimization -- Aerodynamic shape optimization -- High-speed train; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103242
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A hybrid particle swarm optimization algorithm is first proposed for constructing an optimal least squares support vector regression model. An elite-evolved multi-objective particle swarm optimization algorithm is proposed. Cases verify the effectiveness of the proposed algorithms. The proposed algorithms can effectively improve the aerodynamic optimization efficiency. Abstract: In this paper, improved particle swarm optimization algorithms are presented for improving the aerodynamic optimization efficiency of a high-speed train head shape. A hybrid particle swarm optimization algorithm, which employs an artificial fish swarm algorithm (AFSA) and a backward learning strategy in particle swarm optimization, is first proposed for constructing an optimal least squares support vector regression (OPT-LSSVR) model. The prediction accuracy of various surrogate models was evaluated, and the results indicate that the OPT-LSSVR model has the smallest prediction errors, where the prediction error for the total aerodynamic drag coefficient is reduced to about 0.45% and reduced to within 5% for the lift coefficient. Besides, an elite-evolved multi-objective particle swarm optimizer (EMPSO), which employs a grouping-based stochastic elite competition mechanism and elite gathering behavior, is proposed to improve the multi-objective optimization efficiency. The verified results indicate that the EMPSO algorithm is more efficient and capable of tackling complex multi-objective problems. Based on the OPT-LSSVR model and the EMPSO algorithm, the aerodynamic shape optimization of the high-speed train is performed. After optimization, the aerodynamic drag coefficient and the aerodynamic lift coefficient are reduced by 3.63% and 10.59%, respectively. Improved algorithms are simple yet efficient, and they have significant implications for the research and design of high-speed trains at higher speeds.