Genetic Algorithm optimization of initial blank shape in deep drawing of a stepped work piece

2016 ◽  
Vol 231 (5) ◽  
pp. 1063-1074
Author(s):  
Yasser Rostamiyan ◽  
Mohammad Abbasi
Keyword(s):  
Genetic Algorithm ◽  
Rolling Direction ◽  
Work Piece ◽  
Low Carbon ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Initial Shape ◽  
Expansion Level ◽  
Blank Shape ◽  
St14 Steel

This study considers the effect of forging direction on the initial shape of sheet to create a stepped work piece. The purpose of this study is to consider rolling direction in 0°, decreasing the waste while producing workpieces and so decreasing total cost of process. To this end, the assumed workpiece was made of a low carbon and anisotropic st14 steel sheet. To find the most appropriate direction and the shortest modification steps for final shape, the expansion level of the sheet was first imaged in the rolling direction and then the piece was shaped by the geometry. This approach was based on the coupling between the simulation and Genetic Algorithm. A Genetic Algorithm based approach is developed to optimize dimensions through integrating a finite element code running to compute the objective functions for each generation. Those points with a few materials modified through Genetic Algorithm yielded better results.

scholarly journals Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 726
Author(s):  
Mohd Saufi Ahmad ◽  
Dahaman Ishak ◽  
Tiang Tow Leong ◽  
Mohd Rezal Mohamed
Keyword(s):  
Genetic Algorithm ◽  
Performance Enhancement ◽  
Optimization Technique ◽  
Analytical Calculation ◽  
Harmonic Distortion ◽  
Torque Ripple ◽  
Development Stage ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Slot Opening

<span lang="EN-US">This paper describes the performance enhancement of double stator permanent magnet synchronous machines (DS-PMSM) based on genetic algorithm optimization (GAO). Generally, throughout the development stage, an analytical calculation is implemented to build the initial model of the DS-PMSM since the analytical calculation can provide the initial parameters based on the types and materials used in the machine design. For further improvement, GAO might potentially be applied to provide the optimization technique in searching the optimal motor parameters iteratively and intelligently with specific objective functions. For this aim, a three-phase, DS-PMSM with different number of slots between the outer and inner stators is first designed by using analytical parameter estimation and then later optimized by GAO. The outer and inner stators have 12-slots and 9-slots respectively, while, the rotor carries 10 magnetic poles. Four main input motor parameters, i.e. outer stator slot opening, outer magnet pole arc, inner stator slot opening and inner magnet pole arc are varied and optimized to achieve the design objective functions, i.e. high output torque, low torque ripple, low cogging torque and low total harmonic distortion (THDv). The results from the optimized GAO are compared with the initial motor model and further validated by finite element method (FEM). The results show a good agreement between GAO and FEM. GAO has achieved very significant improvements in enhancing the machine performance.</span>

GIS-based genetic algorithm optimization tool for supporting land use and land management restructuring

2007 ◽  
Vol 13 (1s) ◽  
pp. 33-37
Author(s):  
V. Makarenko ◽  
◽  
G. Ruecker ◽  
R. Sommer ◽  
N. Djanibekov ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Management ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

Genetic algorithm optimization for storing arbitrary multimode transverse images in thermal atomic vapor

2021 ◽  
Vol 118 (23) ◽  
pp. 234001
Author(s):  
Yun Chen ◽  
Chengyuan Wang ◽  
Ya Yu ◽  
Zibin Jiang ◽  
Jinwen Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Atomic Vapor ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

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.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals PGA4genomics for comparative genome assembly based on genetic algorithm optimization

Genomics ◽  
2009 ◽  
Vol 94 (4) ◽  
pp. 284-286 ◽  
Author(s):  
Fangqing Zhao ◽  
Huabin Hou ◽  
Qiyu Bao ◽  
Jinyu Wu
Keyword(s):  
Genetic Algorithm ◽  
Genome Assembly ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Comparative Genome
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