Optimization design of reinforced concrete frame based on improved genetic algorithm
To solve the prematurity problem of standard genetic algorithm(SGA)in structural optimiza-tion design with discrete variables,a new adaptive crossover and mutation operator was proposed,com-bined with the penalty function improvement measures,to solve the shortcomings of SGA.The adap-tive improvement measures are jointly controlled by the number of iterations and individual excellence degree,so as to achieve a large crossover probability and enrich the population in the early stage and a large mutation probability in the late stage to enhance local optimization,and the optimal control pa-rameters of the improved SGA are obtained through the orthogonal test.The improved SGA is used to verify different reinforced concrete frame structures,and the results show that the calculation results of the improved SGA are more excellent than those of the traditional design method,SGA and pseudo-full internal force algorithm,respectively.This shows the applicability and effectiveness of improving SGA.From the analysis of the overall results,it can be seen that the improved SGA can give better play to its own global optimization performance,and can solve the optimization problem of reinforced concrete frame structure with multiple working conditions and multiple units,which is a relatively effi-cient method.
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