scholarly journals Depth Optimization Analysis of Articulated Steering Hinge Position Based on Genetic Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 55 ◽  
Author(s):  
Bing-wei Cao ◽  
Xin-hui Liu ◽  
Wei Chen ◽  
Yong Zhang ◽  
Ai-min Li
Keyword(s):  
Genetic Algorithm ◽  
Pressure Fluctuation ◽  
Steering System ◽  
Cylinder Pressure ◽  
Objective Functions ◽  
Pressure Function ◽  
Steering Mechanism ◽  
Hinge Position ◽  
Hinge Points ◽  
Force Difference

Articulated steering is affected by the position of the articulated points of the steering cylinder. When the two steering cylinders turn, there is a stroke difference and arm of force difference. The existence of the above differences causes the pressure fluctuation of the steering system. Firstly, the mathematical model of the steering mechanism is established through theoretical analysis. Then, the coordinates of the hinge points of the steering cylinder are optimized using genetic algorithm (GA) with the stroke difference function and cylinder pressure function as the objective functions. The curves of the stroke difference and the arm of force difference of the steering cylinder are obtained by mathematical modeling, and the correctness of the GA is verified. According to the optimization results, the wheel loader prototype was reconstructed, and the reconstruction verified by corresponding sensors. The experimental curves show that the steering system has no obvious pressure fluctuation. Finally, the arm of force difference and stroke difference curves were analyzed, and it was concluded that the arm of force difference was the main cause of pressure fluctuation. The objective function was improved, and the arm of force function and cylinder pressure function were taken as the objective functions to continue the optimization by GA. The arm of force difference and stroke difference after optimization were reduced, which provides a constructive reference for the design of articulated steering systems in the future.

PROJECT ON ELECTRONIC STEERING SYSYTEM

2018 ◽  
Vol 4 (5) ◽  
pp. 7
Author(s):  
Shivam Dwivedi ◽  
Prof. Vikas Gupta
Keyword(s):  
Steering System ◽  
Essential Elements ◽  
Variable Pressure ◽  
Passenger Cars ◽  
Control Techniques ◽  
Steering Mechanism ◽  
Dynamics And Control ◽  
Active Research ◽  
Electronic Steering ◽  
And Control

As the four-wheel steering (4WS) system has great potentials, many researchers' attention was attracted to this technique and active research was made. As a result, passenger cars equipped with 4WS systems were put on the market a few years ago. This report tries to identify the essential elements of the 4WS technology in terms of vehicle dynamics and control techniques. Based on the findings of this investigation, the report gives a mechanism of electronically controlling the steering system depending on the variable pressure applied on it. This enhances the controlling and smoothens the operation of steering mechanism.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

A critical review on automation of steering mechanism of load haul dump machine

2019 ◽  
Vol 234 (2) ◽  
pp. 160-182
Author(s):  
Sreeharsha Rowduru ◽  
Niranjan Kumar ◽  
Ajit Kumar
Keyword(s):  
Critical Review ◽  
Tracking Error ◽  
Steering System ◽  
Pictorial Representation ◽  
Research Gaps ◽  
Steering Mechanism ◽  
Different Types ◽  
Automatic Steering ◽  
Complete Automation ◽  
Future Work

This article presents a brief note on the evolution of steering mechanisms and more emphasized on articulated steering system of the load haul dump machine. In this respect, pictorial representation of the evolution of steering mechanisms for on-road and articulated steering mechanisms of the load haul dump machine is made from the available literature. Critical review on basic elements required for the complete automation of the load haul dump vehicle steering system is presented. Different types of controllers for path tracking error minimization of the scale-modeled or simulated model of the load haul dump steering system are tabulated. Few case studies stimulating the complete automation of the load haul dump steering system employed on the field are also discussed. Challenges and some research gaps in making fully automated steering system of the load haul dump machine are identified in this review article. At the end, based on the critical review, some novel methods for making the fully automated steering system of the load haul dump machine is provided, which is the potential future work for the design and development of feasible automatic steering system.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

2018 ◽  
Vol 9 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Mohamed Abdou Bouteldja ◽  
Mohamed Baadeche ◽  
Mohamed Batouche
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Multilevel Thresholding ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cellular Genetic Algorithm ◽  
Kapur’S Entropy ◽  
Computationally Expensive ◽  
Segmentation Problem ◽  
Nature Inspired Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

scholarly journals Pareto Design of Decoupled Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
M. J. Mahmoodabadi ◽  
A. Bagheri ◽  
N. Nariman-zadeh ◽  
A. Jamali ◽  
R. Abedzadeh Maafi
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
Fourth Order ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Objective Functions ◽  
Software Environment ◽  
Multiobjective Genetic Algorithm ◽  
Sliding Mode Controllers

This paper presents Pareto design of decoupled sliding-mode controllers based on a multiobjective genetic algorithm for several fourth-order coupled nonlinear systems. In order to achieve an optimum controller, at first, the decoupled sliding mode controller is applied to stablize the fourth-order coupled nonlinear systems at the equilibrium point. Then, the multiobjective genetic algorithm is applied to search the optimal coefficients of the decoupled sliding-mode control to improve the performance of the control system. Considered objective functions are the angle and distance errors. Finally, the simulation results implemented in the MATLAB software environment are presented for the inverted pendulum, ball and beam, and seesaw systems to assure the effectiveness of this technique.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

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