scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

Performance Comparison of Particle Swarm Optimization and Genetic Algorithm in Rolling Fin-Tube Heat Exchanger Optimization Design

2008 ◽  
Author(s):  
Wutao Han ◽  
Linghong Tang ◽  
Gongnan Xie ◽  
Qiuwang Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Pressure Drop ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Volume Weight ◽  
Fin Tube

A method for optimization designs of rolling fin-tube heat exchangers was put forward with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. The length of tube bundles, the row numbers of tubes, the width of heat exchanger core and fin pitch were used as the optimization variables. The allowable pressure drop and heat exchange requirements were considered as restrictive conditions. According to specific design requirements, the volume, weight or pressure drop may be chosen as the optimization objective function. In the same design parameters, ranges of the search variables and restrictive conditions, optimization results compared with GA, the minimum volume, weight and pressure drop PSO could decrease by 3.34%, 4.31% and 14.04%, respectively, and corresponding CPU time could be reduced by 32.39%, 40.23% and 33.45%, respectively. In the fields of optimization designs of heat exchanger, Particle Swarm Optimization is a promising optimization method.

scholarly journals Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9772
Author(s):  
Xueli Shen ◽  
Daniel C. Ihenacho
Keyword(s):  
Particle Swarm Optimization ◽  
Research Effort ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Design Parameters ◽  
Initial Population ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Sensitivity Approach

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/second.

Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm

2021 ◽  
pp. 1-36
Author(s):  
Amirhossein Amiri ◽  
Ali Salmasnia ◽  
Meraj Zarifi ◽  
Mohammad Reza Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Control Chart ◽  
Adaptive Sampling ◽  
Control Charts ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Inference System ◽  
Scale Parameters ◽  
Adaptive Control Charts

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size ([Formula: see text]), sampling interval ([Formula: see text]), and control limit coefficients ([Formula: see text] and [Formula: see text])) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria.

Shape Optimization Design of Gravity Buttress of Arch Dam Based on Asynchronous Particle Swarm Optimization Method

2014 ◽  
Vol 662 ◽  
pp. 160-163
Author(s):  
Lei Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Particle Swarm ◽  
Optimization Method ◽  
Arch Dam ◽  
Swarm Optimization ◽  
Safety Coefficient ◽  
Parametric Description ◽  
Particle Swarm Optimization Method

The optimization design method was rarely used to design the gravity buttress of arch dam in the past. With this in mind, the parametric description of gravity buttress is given, and the auto-calculation of its exerting loads and the safety coefficient of anti-slide stability are realized subsequently. Then, the optimization design model of gravity buttress and the procedures of optimization design are presented using the asynchronous particle swarm optimization method. Finally, ODGB software, which is short for Optimization Design of Gravity Buttress software, is developed and verified.

A 6-DOF Ship-Borne Antenna Platform With Large Orientation Workspace

2020 ◽  
Author(s):  
Yuhang He ◽  
Weijia Li ◽  
Yaozhong Wu ◽  
Jinbo Wu ◽  
Zhiyuan Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Weight Ratio ◽  
Stewart Platform ◽  
Design Parameters ◽  
Parallel Mechanisms ◽  
Swarm Optimization ◽  
Pitch Range ◽  
Orientation Workspace ◽  
Load Weight

Abstract Compared with traditional antenna platform with two axes, Stewart platform can search airspace with no tracking blind district. And the advantages of high accuracy, high stiffness and high load-weight ratio also make it be a better solution for antenna platforms. This paper designed a 6-DOF ship-borne antenna platform based on the Stewart platform to overcome the difficulties that to realize a large orientation workspace (azimuth range is from 0° to 360°, pitch range is from 0° to 100°) under the compact dimensions of parallel mechanisms. A novel joint structure has been proposed which can provide a larger rotation angle than common Hooke joints to realize the large orientation workspace without the inter-mechanism interference. In addition, this paper defined the concept of working height and working radius then proposed a trajectory based on that to obtain the complete pose (translation and orientation) of antenna platform by azimuth and pitch angles. After that, the particle swarm optimization algorithm is employed to seek the optimal geometrical design parameters. A prototype of the 6-DOF ship-borne antenna platform adopted the particle swarm optimization results has been constructed. And the results show that it not noly meets the design requirements, but also provides a good performance.

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