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Weight Optimization of Steel Frames Using Genetic Algorithm
Genetic Algorithm (GA) is a new technique in optimization procedure that works best in design problems with discrete variables. It employs the survival of the fittest philosophy in determining the optimum combination. GA optimization procedure is applied to weight optimization of steel plane frames subjected to different load cases. Database of steel beam sizes is provided as the discrete variables. Both elitist and non-elitist search procedures are used to optimize the total weight of steel frames. Crossover types used are 20- and 50-percent uniform. Optimization result using population sizes 10, 20, and 40 are compared. Elitist search procedure showed superior results when compared to non-elitist for higher population sizes search because of its faster convergence rate. Performance of non-elitist is superior when using lesser population sizes. To examine the performance of genetic algorithms, case studies are conducted by varying material groups and the results are compared with the results from other optimization techniques. Genetic optimization showed superior results when compared to other techniques especially to problems with few material groupings.
Weight Optimization of Steel Frames Using Genetic Algorithm
Genetic Algorithm (GA) is a new technique in optimization procedure that works best in design problems with discrete variables. It employs the survival of the fittest philosophy in determining the optimum combination. GA optimization procedure is applied to weight optimization of steel plane frames subjected to different load cases. Database of steel beam sizes is provided as the discrete variables. Both elitist and non-elitist search procedures are used to optimize the total weight of steel frames. Crossover types used are 20- and 50-percent uniform. Optimization result using population sizes 10, 20, and 40 are compared. Elitist search procedure showed superior results when compared to non-elitist for higher population sizes search because of its faster convergence rate. Performance of non-elitist is superior when using lesser population sizes. To examine the performance of genetic algorithms, case studies are conducted by varying material groups and the results are compared with the results from other optimization techniques. Genetic optimization showed superior results when compared to other techniques especially to problems with few material groupings.
Weight Optimization of Steel Frames Using Genetic Algorithm
Torregosa, Ribelito F. (author) / Kanok-Nukulchai, Worsak (author)
Advances in Structural Engineering ; 5 ; 99-111
2002-04-01
13 pages
Article (Journal)
Electronic Resource
English
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