scholarly journals Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II

2020 ◽  
Vol 10 (5) ◽  
pp. 1646 ◽  
Author(s):  
Jun Fu ◽  
Haikuo Yuan ◽  
Depeng Zhang ◽  
Zhi Chen ◽  
Luquan Ren
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Loss Rate ◽  
Optimization Method ◽  
Operational Performance ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Damage Rate

Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.

scholarly journals Mechanical properties optimization of the steering column based on multi-objective optimization design

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668294 ◽  
Author(s):  
Si Chen ◽  
Zhaohui Wang ◽  
Mi Lv
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Optimization Design ◽  
Strain Difference ◽  
Optimization Method ◽  
Equivalent Strain ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Multi Objective ◽  
Initial Results

The mechanical properties of the steering column have a significant influence on the comfort and stability of a vehicle. In order for the mechanical properties to be improved, the rotary swaging process of the steering column is studied in this article. The process parameters, including axial feed rate, hammerhead speed, and hammerhead radial reduction, are systematically analyzed and optimized based on a multi-objective optimization design. The response surface methodology and the genetic algorithm are employed for optimal process parameters to be obtained. The maximum damage value, the maximum forming load, and the equivalent strain difference obtained with the optimal process parameters are, respectively, decreased by 30.09%, 7.44%, and 57.29% compared to the initial results. The comparative results present that the quality of the steering column is improved. The torque experiments and fatigue experiments are conducted with the optimal steering column. The maximum torque is measured to be 260 NM, and the service life is measured to be 2 weeks (40 NM, 2500 times), which are, respectively, increased by 8.3% and 8.69% compared to the initial results. The above results display that the mechanical properties of the steering column are optimized to verify the feasibility of the multi-objective optimization method.

scholarly journals Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

2019 ◽  
Vol 11 (23) ◽  
pp. 6728 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Liu ◽  
Li
Keyword(s):  
Fuel Consumption ◽  
High Efficiency ◽  
Optimization Method ◽  
Pollutant Emissions ◽  
Multi Objective Optimization ◽  
Optimization Scheme ◽  
Nsga Ii ◽  
Multi Objective ◽  
International Airport ◽  
And Control

High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

scholarly journals Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Traffic Congestion ◽  
Control Method ◽  
Optimization Method ◽  
Speed Limit ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

Multi-Objective Optimization of the Impeller Geometry for an Automotive Torque Converter Using RBF and NSGA-II Methods

2020 ◽  
Author(s):  
Jie Chen ◽  
Guangqiang Wu
Keyword(s):  
Great Influence ◽  
Optimization Method ◽  
Torque Converter ◽  
Numerical Characteristic ◽  
Multi Objective Optimization ◽  
Peak Efficiency ◽  
Nsga Ii ◽  
Characteristic Curves ◽  
Multi Objective ◽  
Impeller Geometry

Abstract Since impeller shape has great influence on hydraulic performance of a torque converter, a multi-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) has been used to redesign the impeller geometry. Radial basis function (RBF) is attempted to establish the surrogate models for performance responses in impeller design. A sophisticated automotive torque converter case is exemplified, which demonstrates that RBF provides a better surrogate accuracy and NSGA-II is more effective than the other methods studied. To verify the optimization results, the complete numerical characteristic curves of the torque converter with the optimized impeller are compared to the validated numerical characteristic curves of the initial torque converter. The numerical results show that the stall torque ratio and peak efficiency are increased by 3.18% and 1.4%, respectively. The results indicate a reasonable improvement in the optimal design of torque converter impeller and a higher performance using the NSGA-II method.

scholarly journals A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3065 ◽  
Author(s):  
Ying Liu ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Computational Cost ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Uwb Antenna ◽  
Nsga Ii ◽  
Multi Objective ◽  
Iot Applications ◽  
Comparison Results ◽  
Pareto Fronts ◽  
Better Than

In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.

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

2016 ◽  
Vol 23 (5) ◽  
pp. 782-793 ◽  
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.

scholarly journals Efficient multi-objective optimization methods for computationally intensive urban water resources models

2014 ◽  
Vol 17 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Mohammad Mortazavi-Naeini ◽  
George Kuczera ◽  
Lijie Cui
Keyword(s):  
Ant Colony Optimization ◽  
Water Resource ◽  
Optimization Methods ◽  
Optimization Method ◽  
Ant Colony ◽  
Urban Water ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

Sign in / Sign up

Export Citation Format

Share Document