Title: Intelligent selection of parameters for air-floating piston based on improved multi-objective grey wolf optimisation algorithm

Authors: Yifan Jia; Shu Qian; Zhihao Zhang; Hengyang Zhou; Lijiao Liu; Xiang Li; Luis Miguel Ruiz Páez; Pengfei Qian

Addresses: School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China ' Zhejiang XingChen Pneumatic Co., Ltd., Yueqing, 325600, China ' School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China ' School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China ' National Quality Inspection and Testing Center of Pneumatic Products, Ningbo, 315500, China ' School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China ' School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China; School of Mechatronic Engineering, Polytechnic University of the Metropolitan Zone of Guadalajara, Tlajomulco de Zúñiga 45670, Mexico ' School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, China

Abstract: The air-floating piston serves as the core component of an air floating frictionless cylinder, which significantly impacts the cylinder's performance. However, since the radial bearing force and gas consumption of the piston are influenced by numerous factors, designing a piston with excellent performance through numerical simulation and empirical data is impractical. For this reason, a design method of air-floating piston is proposed to intelligently select the parameters of the piston using intelligent optimisation algorithm. To achieve efficient parameter selection, mathematical models are developed for bearing capacity and gas consumption. In view of the weaknesses of the multi-objective grey wolf optimiser (MOGWO) such as imbalance between exploration and exploitation, poor local search capability, an enhanced approach which integrates the golden sine algorithm, chaotic mapping, and t-distribution mutation is proposed. The appropriate parameters of the piston are selected to machine the prototype from the Pareto optimal solution. The test results indicate that the designed air-floating piston exhibits excellent performance.

Keywords: air-floating piston; multi-objective grey wolf optimisation algorithm; golden sine algorithm; chaotic mapping.

DOI: 10.1504/IJHM.2025.145777

International Journal of Hydromechatronics, 2025 Vol.8 No.2, pp.121 - 147

Received: 06 Sep 2024
Accepted: 28 Oct 2024
Published online: 23 Apr 2025 *

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