Genetic Algorithm for Optimized Design of Flyback Transformers

2020 ◽  
Author(s):  
Angel Marinov ◽  
Emilian Bekov ◽  
Firgan Feradov ◽  
Toncho Papanchev
Keyword(s):  
Genetic Algorithm ◽  
Optimized Design

scholarly journals Optimized design of concrete-filled steel columns

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jéssica Salomão Lourenção ◽  
Paulo Augusto Tonini Arpini ◽  
Gabriel Erlacher ◽  
Élcio Cassimiro Alves
Keyword(s):  
Genetic Algorithm ◽  
Cost Efficiency ◽  
Optimization Problem ◽  
Optimized Design ◽  
Composite Columns ◽  
Steel Columns ◽  
Normal Forces ◽  
Steel Bars ◽  
Genetic Algorithm Method ◽  
Filled Composite

Abstract The objective of this paper is to present the formulation of the optimization problem and its application to the design of concrete-filled composite columns with and without reinforcement steel bars, according to recommendations from NBR 8800:2008, NBR 16239:2013 and EN 1994-1-1:2004. A comparative analysis between the aforementioned standards is performed for various geometries considering cost, efficiency and materials in order to verify which parameters influence the solution of the composite column that satisfies the proposed problems. The solution of the optimization problem is obtained by using the genetic algorithm method featured in MATLAB’s guide toolbox. For the examples analyzed, results show that concretes with compressive strength greater than 50MPa directly influence the solution of the problem regarding cost and resistance to normal forces.

Design improvement and fatigue analysis for a bicycle handlebar stem system using uniform design method and genetic algorithm

2020 ◽  
Vol 234 (11) ◽  
pp. 2266-2278
Author(s):  
Cho-Pei Jiang ◽  
Ching-Wei Wu ◽  
Yung-Chang Cheng
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Safety Factor ◽  
Uniform Design ◽  
Optimization Procedure ◽  
Bending Test ◽  
Von Mises Stress ◽  
Kriging Interpolation ◽  
Optimized Design ◽  
Von Mises

An integrating optimization procedure is presented to improve the von Mises stress and fatigue safety factor for a handlebar stem system in a bicycle system. The optimization procedure involves uniform design of experiment, Kriging interpolation, genetic algorithm, and nonlinear programming method. Using ANSYS/Workbench software and the ISO 4210 bicycle handlebar stem testing standard, the von Mises stress for the lateral bending test simulation and the fatigue safety factor for the fatigue test simulation is calculated. The von Mises stress and fatigue safety factor are combined into a single and integrated objective function, and Kriging interpolation is then used to create the surrogate model of the integrated objective function. When the integrating optimization procedure is used, the integrated objective function demonstrates that the von Mises stress for the optimized handlebar stem is reduced to 225 MPa and the fatigue safety factor increases to 1.796. This shows that the optimized design increases the strength of the handlebar stem. The proposed technique yields a handlebar stem with an optimized shape.

Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm

1998 ◽  
Vol 124 (5) ◽  
pp. 551-559 ◽  
Author(s):  
Charles Camp ◽  
Shahram Pezeshk ◽  
Guozhong Cao
Keyword(s):  
Genetic Algorithm ◽  
Optimized Design ◽  
Two Dimensional

Optimization Study of a Hybrid Solar Air Conditioner

2013 ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimized Design ◽  
Air Conditioner ◽  
Air Conditioners ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Optimization Study ◽  
Systems Simulation

In order for the solar air conditioners (A/Cs) to become a real alternative to the conventional systems, their performance and total cost has to be optimized. In this study, an innovative hybrid solar A/C was simulated using the transient systems simulation (TRNSYS) program, which was coupled with MATLAB in order to carry out the optimization study. Two optimization problems were formulated with the following design variables: collector area, collector mass flow rate, storage tank volume, and number of batteries. The Genetic Algorithm (GA) was selected to find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, a Multi-Objective Genetic Algorithm (MOGA) was used to find the Pareto front within the design variables’ bounds while satisfying the constraints. The optimized design was also compared to a standard vapor compression cycle. The results show that coupling TRNSYS and MATLAB expands TRNSYS optimization capability in solving more complicated optimization problems.

A Structure Optimization for Hooklift Arm Device Based on the Genetic Algorithm

2012 ◽  
Vol 455-456 ◽  
pp. 1504-1508
Author(s):  
Huan Ming Chen ◽  
Da Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Parameter Optimization ◽  
Structure Optimization ◽  
Optimization Method ◽  
Optimized Design ◽  
Design Decision ◽  
Original Design ◽  
Multi Objective ◽  
Engineering Constraint

Based on the theory of FEM, the hooklift arm is modeled with the FEM software, and the structure of the device is optimized with genetic algorithm in a multi-objective/multi-parameter optimization environment, which results in an optimal design decision of the hooklift arm device under the engineering constraint. Comparison between optimized design decision and original design decision shows that the optimization is correct and the proposed multi-objective/multi-parameter optimization method is effective in improving the hooklift arm device.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

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