Title: Multi-objective optimisation of vehicle body parameters based on improved sparrow search algorithm

Authors: Xiuzhen Guo; Yannan Wei; Xianzhen Zhang

Addresses: Department of Mechanical Engineering, Hunan Industry Polytechnic, Changsha, 410000, China ' Department of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan, 467000, China ' Department of Automotive Engineering, Shandong Polytechnic College, Jining, 272000, China

Abstract: In order to solve the problems of low structural strength, high total manufacturing cost and long optimisation time of vehicle body parameters optimisation methods, a multi-objective optimisation method of vehicle body parameters based on improved sparrow search algorithm is proposed. The multi-objective optimisation model of vehicle body parameters is constructed by analysing the multi-objectives and related constraints such as minimising body mass, maximising structural stiffness and minimising vibration and noise, and combining with Gaussian process regression. Through tent mapping strategy, reverse learning strategy, Levy flight strategy, inertia weight factor and random walk strategy, the improved sparrow search algorithm is used to solve the model and the multi-objective optimisation of vehicle body parameters is realised. The experimental results show that the average strength of the vehicle body structure is 323.4 MPa, the total cost of vehicle body manufacturing is 107,113 yuan, and the optimisation time range is 30~53 min.

Keywords: improved sparrow search algorithm; vehicle; body parameters; multi-objective optimisation; tent mapping strategy; reverse learning strategy; Levy flight strategy; inertia weight factor.

DOI: 10.1504/IJMPT.2024.143446

International Journal of Materials and Product Technology, 2024 Vol.69 No.1/2, pp.39 - 55

Received: 12 Mar 2024
Accepted: 09 Aug 2024
Published online: 20 Dec 2024 *

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