In order to improve the ride comfort of vehicles, a 9-degree-of-freedom (DoF) nonlinear suspension model was established based on an SUV. The validity of the model was demonstrated by comparing the vibration acceleration responses near the seat position at constant forward speeds. To address the slow convergence and local optima issues of the Carnivorous Plant Algorithm (CPA), an improved Double Chaos Carnivorous Plant Algorithm (DCCPA) was proposed, incorporating chaos initialization and chaos search methods. The root mean square values of the vertical, pitch, and roll accelerations of the vehicle, as well as vertical acceleration of the seat, were selected as evaluation factors for ride comfort. Based on the DCCPA, the vehicle suspension model was optimized. The optimization results indicate that DCCPA reduces the objective function values and improves vehicle ride comfort. Furthermore, compared with the CPA, the DCCPA exhibits better convergence performance.
SunWLiYNHuangJY, et al. Efficiency improvement of vehicle active suspension based on multi-objective integrated optimization. J Vib Control2017; 23(4): 539–554.
2.
LiuXASunJWShiW, et al. Modeling and analysis of high-frequency dynamic characteristics of double isolation rubber mounts. Proc Inst Mech Eng Part D: J Automob Eng2024; 238(4): 620–632.
3.
MahmoodiKMJavanshirIAsadiK, et al. Optimization of suspension system of off-road vehicle for vehicle performance improvement. J Cent South Univ2013; 20(4): 902–910.
4.
SilvaRRdinaldoILMontenegroDP, et al. Optimization of vehicle suspension parameters based on ride comfort and stability requirements. Proc Inst Mech Eng Part D: J Automob Eng2021; 235(7): 1920–1929.
5.
DrehmerLRCMartinsGHCasasP, et al. An interval-based multi-objective robust design optimization for vehicle dynamics. Mech Based Des Struct Mach2023; 51(12): 7076–7101.
6.
LiangYJLuYJGaoDX, et al. Applications of approximate optimal control to nonlinear systems of tracked vehicle suspensions. Int J Comput Intell Syst2021; 14(1): 174.
7.
MiaoYYRuiXTWangPX, et al. Nonlinear dynamic modeling and analysis of magnetorheological semi-active suspension for tracked vehicles. Appl Math Model2024; 125: 311–333.
8.
SeifiAHassanneiadR. Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation. Proc Inst Mech Eng Part D: J Automob Eng2022; 236(12): 2725–2744.
9.
ZhaoJWongPKLiWF, et al. Reliable fuzzy sampled-data control for nonlinear suspension systems against actuator faults. IEEE ASME Trans Mechatron2022; 27(6): 5518–5528.
10.
VahediAJamaliA. Constraint optimization of nonlinear McPherson suspension system using genetic algorithm and ADAMS software. J Vib Control2022; 28(21–22): 3140–3151.
11.
KumarSMedhaviAKumarR. Optimization of nonlinear passive suspension system to minimize road damage for heavy goods vehicle. Int J Acoust Vib2021; 26(1): 56–63.
12.
DeprezKMoshouDRamonH. Comfort improvement of a nonlinear suspension using global optimization and in situ measurements. J Sound Vib2005; 284(3–5): 1003–1014.
13.
SeifiAHassannejadRHamedMA. Use of nonlinear asymmetrical shock absorbers in multi-objective optimization of the suspension system in a variety of road excitations. Proc Inst Mech Eng Part K: J Multi-Body Dyn2017; 231(2): 372–387.
14.
ZhaoLLZhouCCYuYW, et al. Hybrid modelling and damping collaborative optimization of Five-suspensions for coupling driver-seat-cab system. Veh Syst Dyn2016; 54(5): 667–688.
15.
FossatiGGMiguelLFFCasasWJP. Multi-objective optimization of the suspension system parameters of a full vehicle model. Optim Eng2019; 20(1): 151–177.
16.
GeorgiouGVerrosGNatsiavasS. Multi-objective optimization of quarter-car models with a passive or semi-active suspension system. Veh Syst Dyn2007; 45(1): 77–92.
17.
YangLWangRCSunZY, et al. Multi-objective optimization design of hydropneumatic suspension with gas-oil emulsion for ride comfort and handling stability of an articulated dumper truck. Eng Optim2021; 55(2): 291–310.
18.
KwonKSeoMKimH, et al. Multi-objective optimisation of hydro-pneumatic suspension with gas–oil emulsion for heavy-duty vehicles. Veh Syst Dyn2020; 58(7): 1146–1165.
19.
ChenQBaiXXFZhuAD, et al. Influence of balanced suspension on handling stability and ride comfort of off-road vehicle. Proc Inst Mech Eng Part D: J Automobile Engineering2021; 235(6): 1602–1616.
20.
FuYMZhouXJ. Multi-objective optimization of multi-axle heavy duty vehicle ride comfort and handling stability based on analytical target cascading method. J Mech Sci Technol2023; 37(11): 5661–5682.
21.
ZhouCXuFXChengDQ. A multi-objective optimization method for hydraulically interconnected suspension system. Proc Inst Mech Eng Part D: J Automob Eng2024; 238(5): 1234–1248.
22.
GaoJQiXP. Relevance analysis of the bushing stiffness of rear multi-link suspension to handling stability and ride comfort of vehicle and optimization research to improve both performances. Proc Inst Mech Eng Part D: J Automob Eng2024; 238(6): 1480–1496.
23.
PrabakarRSSujathaCNarayananS. Response of a half-car model with optimal magnetorheological damper parameters. J Vib Control2016; 22(3): 784–798.
24.
IssaMSamnA. Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm. Math Comput Simul2022; 191: 328–345.
25.
ChenSMShiTZWangDF, et al. Multi-objective optimization of the vehicle ride comfort based on Kriging approximate model and NSGA-II. J Mech Sci Technol2015; 29(3): 1007–1018.
26.
JabeenSDMukherjeeRNSahaJ. DART algorithm for constrained optimization with applications in suspension design. Int J Heavy Veh Syst2019; 26(2): 136–157.
27.
MengOKPaulineOKiongSC. A carnivorous plant algorithm for solving global optimization problems. Appl Soft Comput2021; 98: 106833.
28.
BeskirliADagI. An improved carnivorous plant algorithm for solar photovoltaic parameter identification problem. Biomimetics2023; 8(8): 569.
29.
YangYFZhangCS. A multi-objective carnivorous plant algorithm for solving constrained multi-objective optimization problems. Biomimetics2023; 8(2): 136.
30.
WangYWangWChenY. Carnivorous plant algorithm and BP to predict optimum bonding strength of heat-treated woods. Forests2022; 14(1): 51.
31.
ShoukatAMughalMAGondalSY, et al. Optimal parameter estimation of transmission line using chaotic initialized time-varying PSO algorithm. Comput Mater Continua2022; 71(1): 269–285.
32.
Rizk-AllahRMEldesokyIMAboaliEA, et al. Heap-based optimizer algorithm with chaotic search for nonlinear programming problem global solution. Int J Comput Intell Syst2023; 16(1): 149.
