Multi-Objective Optimization of Vehicle Passive Suspension System Using NSGA-II, SPEA2 and PESA-II

This paper focuses on the design and the optimization of a semi-active suspension system used in a full dynamic model of a two-wheeled vehicle. The two-wheeled vehicle is considered as a multibody system. The equations of motion are obtained by applying an approach used widely in the robotic modeling field. Two basic strategies, called the continuous skyhook and the modified skyhook, are used to control the semi-active suspension system. Using the developed model, a multi-objective optimization procedure, based on Genetic Algorithms (NSGA-II), is proposed. The objective is to optimize the parameters of the two control laws of the semi-active suspension systems, in order to improve the ride comfort and the safety. To study the effectiveness of this approach, the results of the optimization are used in different simulations and the results are compared with those obtained from a simulation of a two-wheeled vehicle equipped with a passive suspension system. The results show that both control strategies of the semi-active suspension system give an improvement compared to the passive suspension system. Moreover, the multi-objective optimization results show that the simplified law “Modified Skyhook” ensures a higher ride safety, whereas the “Continuous Skyhook” is more effective in obtaining a higher level of ride comfort.

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Multi-Objective Optimal Design of Passive Suspension System With Inerter Damper

Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control ◽

10.1115/dscc2018-9011 ◽

2018 ◽

Author(s):

Xiaotian Xu ◽

Yousef Sardahi ◽

Chenyu Zheng

Keyword(s):

Optimal Design ◽

Suspension System ◽

Design Parameters ◽

Head Acceleration ◽

Crest Factor ◽

Nsga Ii ◽

Passive Suspension ◽

Trade Offs ◽

Passive Suspension System ◽

Unsprung Mass

This paper presents a many-objective optimal design of a four-degree-of-freedom passive suspension system with an inerter device. In the optimization process, four objectives are considered: passenger’s head acceleration (HA), crest factor (CF), suspension deflection (SD), and tire deflection (TD). The former two objectives are important for the health and comfort of the driver and the latter two quantify the suspension system performance. The spring ks and damping cs constants between the sprung mass and unsprung mass, the inertance coefficient B, and the tire spring constant ky are considered as design parameters. The non-dominated sorting genetic algorithm (NSGA-II) is used to solve this optimization problem. The results show that there are many optimal trade-offs among the design objectives that could be applicable to suspension design in the industry.

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Multi-objective optimization of a hybrid electromagnetic suspension system for ride comfort, road holding and regenerated power

Journal of Vibration and Control ◽

10.1177/1077546315585219 ◽

2016 ◽

Vol 23 (5) ◽

pp. 782-793 ◽

Cited By ~ 20

Author(s):

Mansour Ataei ◽

Ehsan Asadi ◽

Avesta Goodarzi ◽

Amir Khajepour ◽

Mir Behrad Khamesee

Keyword(s):

Pareto Front ◽

Ride Comfort ◽

Optimization Method ◽

Suspension System ◽

Multi Objective Optimization ◽

Damping Force ◽

Nsga Ii ◽

Electromagnetic Suspension ◽

Multi Objective ◽

Electromagnetic Component

This paper reports work on the optimization and performance evaluation of a hybrid electromagnetic suspension system equipped with a hybrid electromagnetic damper. The hybrid damper is configured to operate with hydraulic and electromagnetic components. The hydraulic component produces a large fail-safe baseline damping force, while the electromagnetic component adds energy regeneration and adaptability to the suspension. For analyzing the system, the electromagnetic component was modeled and integrated into a 2DOF quarter-car model. Three criteria were considered for evaluating the performance of the suspension system: ride comfort, road holding and regenerated power. Using the genetic algorithm multi-objective optimization (NSGA-II), the suspension design was optimized to improve the performance of the vehicle with respect to the selected criteria. The multi-objective optimization method provided a set of solutions called Pareto front in which all solutions are equally good and the selection of each one depends on conditions and needs. Among the given solutions in the Pareto front, a small number of cases, with different design purposes, were selected. The performances of the selected designs were compared with two reference systems: a conventional and a nonoptimized hybrid suspension system. The results show that the ride comfort and road holding qualities of the optimized hybrid system are improved, and the regenerated power is considerably increased.

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Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽

10.15388/informatica.2015.37 ◽

2015 ◽

Vol 26 (1) ◽

pp. 33-50 ◽

Cited By ~ 16

Author(s):

Ernestas Filatovas ◽

Olga Kurasova ◽

Karthik Sindhya

Keyword(s):

Evolutionary Algorithm ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

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Comparison of Control Strategies Applied to Nonlinear Quarterly Car Passive Suspension System

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v8i3.5833 ◽

2015 ◽

Vol 8 (3) ◽

pp. 203

Author(s):

Muhammad Sani Gaya ◽

Amir Bature ◽

Lukman A. Yusuf ◽

I. S. Madugu ◽

Ukashatu Abubakar ◽

...

Keyword(s):

Control Strategies ◽

Suspension System ◽

Passive Suspension ◽

Passive Suspension System

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Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽

10.3390/machines9060107 ◽

2021 ◽

Vol 9 (6) ◽

pp. 107

Author(s):

Rongchao Jiang ◽

Zhenchao Jin ◽

Dawei Liu ◽

Dengfeng Wang

Keyword(s):

Ride Comfort ◽

Lightweight Design ◽

Dynamic Performance ◽

Vehicle Dynamic ◽

Multi Objective Optimization ◽

Original Design ◽

Dynamic Simulations ◽

Nsga Ii ◽

Torsion Beam ◽

Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

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Design and characterization of effective solar cells

Energy Systems ◽

10.1007/s12667-021-00451-x ◽

2021 ◽

Author(s):

Varun Ojha ◽

Giorgio Jansen ◽

Andrea Patanè ◽

Antonino La Magna ◽

Vittorio Romano ◽

...

Keyword(s):

Solar Cells ◽

Quantum Efficiency ◽

Cost Benefit Analysis ◽

Cost Minimization ◽

Cell Structure ◽

Cost Benefit ◽

Structure Design ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

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Multi-objective optimization of energy efficiency and thermal comfort in an existing office building using NSGA-II with fitness approximation: a case study

Journal of Building Engineering ◽

10.1016/j.jobe.2021.102440 ◽

2021 ◽

pp. 102440

Author(s):

Mohammadamin Ghaderian ◽

Farzad Veysi

Keyword(s):

Energy Efficiency ◽

Thermal Comfort ◽

Office Building ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Fitness Approximation

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Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406221995863 ◽

2021 ◽

pp. 095440622199586

Author(s):

Qianhao Xiao ◽

Jun Wang ◽

Boyan Jiang ◽

Weigang Yang ◽

Xiaopei Yang

Keyword(s):

Flow Rate ◽

Optimization Design ◽

Volume Flow Rate ◽

Kriging Model ◽

Volume Flow ◽

Design Parameters ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

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