scholarly journals WEIGHT MINIMIZATION OF SPATIAL TRUSSES WITH GENETIC ALGORITHM

2019 ◽  
pp. 238-243
Author(s):  
Maksym Grzywiński ◽  
Jacek Selejdak
2019 ◽  
Vol 1 (1) ◽  
pp. 238-243
Author(s):  
Maksym Grzywiński ◽  
Jacek Selejdak

Abstract A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.


2011 ◽  
Vol 11 (2) ◽  
pp. 2565-2575 ◽  
Author(s):  
Tayfun Dede ◽  
Serkan Bekiroğlu ◽  
Yusuf Ayvaz

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan

Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.


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