Application Of Genetic Algorithm In The Optimization Design Of Building Structure

With the rapid development of the national economy, construction industry is very activeand has made great achievements. But the traditional designs are carried out only by virtue ofthe designer’s personal experience and vary greatly. Mostly designs are only at a reasonablerange and did not find the optimal indexes, causing a great deal of waste. In the premise ofconsidering a range of constraints, the current regulatory requirements, structuralrequirements and how to quickly find both safe and economical design is the structuraloptimization problem to be solved currently.Genetic algorithm is a good choice for the optimum design of building structures whichhave many elements, complex and larger calculation. It can solve the complex problem thatthe traditional optimization methods can not solve. This paper describes the basic theory ofgenetic algorithms in detail and improves the genetic algorithms by self-adaptive crossoverand mutation strategy, best individual retention policies and "elite" competition strategy ofgenetic algorithm with taking into the characteristics of genetic algorithms. Improving geneticalgorithms can not only improve the efficiency, but also can improve the solution qualityeffectively.This paper builds a mathematical model of frame structure optimum design, programs animproved genetic algorithm for structural optimization procedures which is based on C++Builder6.0and selects the total cost of the structure as the objective function to optimize thecross-section of reinforced concrete frame structure and the reinforcement. According to thelatest standard, the optimum design of reinforced concrete frame structure is designed. Afterverification, improved genetic algorithm compared with conventional design methods cansignificantly reduce the cost of structure. Compared with general genetic algorithmoptimization, the optimization effect and speed of improved genetic algorithm are moreperfect. It shows that application of the improved genetic algorithm in optimal design ofbuilding structures is effective.