Initial Design of Cross-section Using Genetic Algorithms

Abstract The hybridization of different optimization methods have been used to find the optimum solution of design problems. While random search techniques, such as genetic algorithms and simulated annealing, have a high probability of achieving global optimality, they usually arrive at a near optimal solution due to their random nature. On the other hand direct search methods are efficient optimization techniques but linger in local minima if the objective function is multi-modal. This paper presents the optimization of C-frame cross-section using a hybrid optimization algorithm. Real coded genetic algorithms are used as a random search method, while Nelder-Mead is used as a direct search method, where the result of the genetic algorithm search is used as the starting point of direct search. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal, circular and rectangular sections. The cross-sectional shape is represented by a non-uniform rational B-Splines (NURBS) in order to give it a kind of shape flexibility. The results showed that the use of Nelder-Mead with Real coded Genetic Algorithms has been very significant in improving the optimum shape of a solid C-frame cross-section subjected to a combined tension and bending stresses. The hybrid optimization method could be extended to more complex shape optimization problems.

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Optimization of the cross section of car lift column under pressure load using genetic algorithms

Indian Journal of Science and Technology ◽

10.17485/ijst/2011/v4i6.13 ◽

2011 ◽

Vol 4 (6) ◽

pp. 622-626

Author(s):

H. Goodarzian

Keyword(s):

Genetic Algorithms ◽

Cross Section ◽

Pressure Load ◽

The Cross

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Optimization of Predesign of Switched Reluctance Machines Cross Section Using Genetic Algorithms

2007 7th International Conference on Power Electronics and Drive Systems ◽

10.1109/peds.2007.4487780 ◽

2007 ◽

Cited By ~ 7

Author(s):

Satit Owatchaiphong ◽

Christian Carstensen ◽

Rik W. De Doncker

Keyword(s):

Genetic Algorithms ◽

Cross Section ◽

Switched Reluctance ◽

Switched Reluctance Machines

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Design of C-Frames Using Real-Coded Genetic Optimization Algorithms and NURBS

Volume 2: 19th Computers and Information in Engineering Conference ◽

10.1115/detc99/cie-9138 ◽

1999 ◽

Author(s):

Ashraf O. Nassef ◽

Hesham A. Hegazi ◽

Sayed M. Metwalli

Keyword(s):

Genetic Algorithms ◽

Cross Section ◽

Cross Sections ◽

Design Parameters ◽

Binary Representation ◽

Cross Sectional ◽

Search Spaces ◽

The Cross ◽

Sectional Shape ◽

Real Coded Genetic Algorithms

Abstract C-frames constitute a large portion of machine tools that are currently used in industry. Examples of these frames include drilling machines, presses, punching and stamping machines, clamps, hooks, etc. The design parameters of these frames include the dimensions of their cross-sections, which should be chosen to withstand the applied loads and minimize the element’s overall weight. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal and rectangular sections. This paper introduces a new methodology for designing the frame’s cross-section. The cross-sectional shape is represented using non-uniform rational B-Spline (NURBS) in order to give it a form of shape flexibility. A special form of genetic algorithms known as real-coded genetic algorithms is used to conduct the search for the design objectives. Real-coded genetic algorithms are known to outperform the simple binary representation genetic algorithms when dealing with continuous search spaces. The results showed that the optimal shape was a semi I/T-section with the material bulk related to the applied load.

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