AI SearchOptimization of steel frames using genetic algorithms
Call Number: AIT Thesis no.ST-98-05 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ST-98-05Publication details: Bangkok : Asian Institute of Technology, 1998Description: 50, A4, B35, C3, D3 leavesSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1998 Summary: 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. Genetic algorithm program was developed using Fortran Programming Language. Test was conducted in order to determine the reliability of the program by optimizing a beam using both exact and GA. Genetic algorithm were used to optimize plane frames under different load cases. Database of steel sizes was provided as the discrete variables. Three different search procedures were tested in the study. These are the elitist without mutation, non-elitist and elitist with mutation search. Different crossover types, population sizes, and crossover probabilities were used and the best method was determined. To examine the performance of genetic algorithms, case studies were conducted and the results obtained were compared with the results from other optimization techniques reported by other authors to some engineering journals.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Civil Engineering
Thesis (M.Eng.) - Asian Institute of Technology, 1998
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. Genetic algorithm program was developed using Fortran Programming Language. Test was conducted in order to determine the reliability of the program by optimizing a beam using both exact and GA. Genetic algorithm were used to optimize plane frames under different load cases. Database of steel sizes was provided as the discrete variables. Three different search procedures were tested in the study. These are the elitist without mutation, non-elitist and elitist with mutation search. Different crossover types, population sizes, and crossover probabilities were used and the best method was determined. To examine the performance of genetic algorithms, case studies were conducted and the results obtained were compared with the results from other optimization techniques reported by other authors to some engineering journals.
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