Abstract
Teaching Learning Based Optimization (TLBO) is one of the non-traditional techniques to simulate natural phenomena into a numerical algorithm. TLBO mimics teaching learning process occurring between a teacher and students in a classroom. A parameter named as teaching factor, TF, seems to be the only tuning parameter in TLBO. Although the value of the teaching factor, TF, is determined by an equation, the value of 1 or 2 has been used by the researchers for TF. This study intends to explore the effect of the variation of teaching factor TF on the performances of TLBO. This effect is demonstrated in solving structural optimization problems including truss and frame structures under the stress and displacement constraints. The results indicate that the variation of TF in the TLBO process does not change the results obtained at the end of the optimization procedure when the computational cost of TLBO is ignored.
Highlights
Optimization tools emerged as obtaining the optimum solution of optimization problems try to maximize or minimize a real function within a domain which contains the acceptable values of variables while some restrictions are to be satisfied
Genetic algorithms (GAs) based on the principle of survival of the fittest as a computational procedure [2,3,4,5,6,7] seems to be commonly employed to obtain the optimum solution of structural design problems, many metaheuristic optimization tools occurred in recent years, which were developed inspiring the different process and phenomena from the nature
The design process, that is explained with the implementation steps given above, of a Teaching Learning Based Optimization (TLBO) technique is properly applied to the example designs such as 52 bar truss, 3-bay 15-story frame, and 582 bar space truss in order to exhibit the effecting of varying the value of TF on the performance of TLBO algorithm
Summary
Optimization tools emerged as obtaining the optimum solution of optimization problems try to maximize or minimize a real function within a domain which contains the acceptable values of variables while some restrictions are to be satisfied. Genetic algorithms (GAs) based on the principle of survival of the fittest as a computational procedure [2,3,4,5,6,7] seems to be commonly employed to obtain the optimum solution of structural design problems, many metaheuristic optimization tools occurred in recent years, which were developed inspiring the different process and phenomena from the nature. This effect is demonstrated solving structural optimization problems including truss and frame structures under the stress and displacement constraints
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