Optimal Design of Motorcycle Rear Suspension Systems Using Genetic Algorithms

2012 ◽  
pp. 181-189 ◽  
Author(s):  
J. J. Castillo ◽  
P. Giner ◽  
A. Simón ◽  
J. A. Cabrera
Keyword(s):  
Genetic Algorithms ◽  
Optimal Design ◽  
Rear Suspension ◽  
Suspension Systems

Optimal design of electric machine using genetic algorithms coupled with direct method

1999 ◽  
Vol 35 (3) ◽  
pp. 1742-1745 ◽  
Author(s):  
Yong-Hwan Oh ◽  
Tae-Kyung Chung ◽  
Min-Kyu Kim ◽  
Hyun-Kyo Jung
Keyword(s):  
Genetic Algorithms ◽  
Optimal Design ◽  
Direct Method ◽  
Electric Machine

Implementation of Eurocode Load Cases in Optimization Problems of Steel Frames, Based on Genetic Algorithms

2013 ◽  
Vol 310 ◽  
pp. 609-613
Author(s):  
Ioana D. Balea ◽  
Radu Hulea ◽  
Georgios E. Stavroulakis
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Global Optimization ◽  
Optimal Design ◽  
Optimization Problems ◽  
Steel Frames ◽  
Global Optimization Algorithm ◽  
Planar Structures ◽  
Discrete Global Optimization ◽  
Steel Sections

This paper presents an implementation of Eurocode load cases for discrete global optimization algorithm for planar structures based on the principles of finite element methods and genetic algorithms. The final optimal design is obtained using IPE sections chosen as feasible by the algorithm, from the available steel sections from industry. The algorithm is tested on an asymmetric planar steel frame with promising results.

scholarly journals Optimal design parameters of air suspension systems for semi-trailer truck. Part 2: results and discussion

2020 ◽  
Vol 33 ◽  
pp. 147-152
Author(s):  
Le Van Quynh ◽  
Nguyen Van Tuan ◽  
Vi Thi Phuong Thao ◽  
Le Quang Duy
Keyword(s):  
Optimal Design ◽  
Design Parameters ◽  
Suspension Systems ◽  
Air Suspension

Optimal Design of IC Engine Cooling Fins by Using Genetic Algorithm

2014 ◽  
Author(s):  
Terry Yan ◽  
Jason Yobby ◽  
Ravindra Vundavilli
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithms ◽  
Optimal Design ◽  
Degrees Of Freedom ◽  
Combustion Engine ◽  
Local Heat Transfer ◽  
System Component ◽  
Cylinder Wall ◽  
Ic Engine ◽  
Engine Cooling

The analysis for optimal design of an air-cooled internal combustion engine cooling fin array by using genetic algorithms (GA) is presented in this study. Genetic Algorithms are robust, stochastic search techniques which are also used for optimizing highly complex problems. In this study, the fin array is of the traditional circular fin type, which is subject to ambient convective heat transfer. The parameters (degrees of freedom) selected for the analysis include the cylinder wall thickness-to-radius ratio, fin thickness, fin length, the number of fins, and the local heat transfer coefficient. By using a single objective GA procedure, the heat transfer through the fin arrays is set as the objective function to be optimized with each parameter varied within the physical ranges. Proper population size is selected and the mutations, cross-over and selection are conducted in the GA procedure to arrive at the optimal set of parameters after a certain number of generations. The GA proves to be an effective optimization method in the thermal system component designs when the number of independent variables is large.

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