Title: Improvement of NSGA-II algorithm and multi-objective optimisation of suspension kinematics

Authors: Zhiyong Zhang; Hui Yu; Caixia Huang; Jie Zhang

Addresses: School of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, 960, 2nd Section, Wanjiali RD (S), Changsha, 410004, Hunan, China ' School of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, 960, 2nd Section, Wanjiali RD (S), Changsha, 410004, Hunan, China ' College of Mechanical Engineering, Hunan Institute of Engineering, No. 88, Fuxing East Road, Xiangtan, 411104, Hunan, China ' School of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, 960, 2nd Section, Wanjiali RD (S), Changsha, 410004, Hunan, China

Abstract: Multi-objective optimisation of suspension kinematics based on the non-dominated sorting genetic algorithm II (NSGA-II) is a widely adopted technical approach. However, the NSGA-II algorithm has several limitations. Therefore, this study aims to address these limitations and improve the algorithm. The improvement begins by introducing chaotic mapping for population initialisation, adaptive crossover and mutation rates, and a dynamic elitism retention mechanism, resulting in the proposed chaotic adaptive non-dominated sorting genetic algorithm II (CA-NSGA-II). The performance of the CA-NSGA-II algorithm is then compared with other algorithms to validate its overall performance. Lastly, the CA-NSGA-II algorithm is applied to the multi-objective optimisation of the kinematics in a double wishbone composite rear suspension system. The results show that compared with other comparison algorithms, the CA-NSGA-II algorithm has better comprehensive performance. When solving the multi-objective optimisation problem of suspension kinematics, it is superior to NSGA-II algorithm and can provide better solutions.

Keywords: non-dominated sorting genetic algorithm II; NSGA-II algorithm; multi-objective optimisation; suspension; kinematics; improve the algorithm; chaotic mapping; crossover and mutation; elitism retention; CA-NSGA-II algorithm; compared with other algorithms.

DOI: 10.1504/IJVP.2026.152831

International Journal of Vehicle Performance, 2026 Vol.12 No.1, pp.73 - 97

Received: 30 Dec 2024
Accepted: 02 Mar 2025
Published online: 13 Apr 2026 *

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