Study On Design Of Structural Optimization Based On Genetic Algorithm

Genetic Algorithm (GA) is one of the main directions in today's compute science. Its application in structural catches more and more attention. Based on a great many literature, this paper analyzes and summarizes the genetic algorithm for the design of structural optimization. The basic conceptions and primary characteristics are introduced in this paper. This paper discuss some primary factor when genetic algorithm applied on optimal design, for example the mathematical model,the constrained problem,the initial population and the controls parameter of genetic algorithmAimed at the characteristic of structural optimization and finite-element analysis, this paper accomplishes the optimal program based on the simple genetic algorithm. This program is present for optimizing structural systems with discrete design variables. The result shows that the program is feasible and effective.Because simple genetic algorithm is good at the global search and short of the local search, searching the best optimized solution need to consume more time. Aimed at the shortage of GA, this paper improves the simple genetic algorithm and accomplishes the optimal program based on the adaptive genetic algorithm. The result shows that the adaptive genetic algorithm improves the convergence rate.Generally, the genetic algorithm randomly generates the initial population. The coding of the variables that describe the problem is always large. In order to find the best optimal result, it needs the larger population and the longer course of optimization. That will spend much time and money, so the appropriate population size is a factor that affects the efficiency of genetic algorithm. Because no literature discusses that how to confirm the appropriate population size, this paper discusses how the population size affects GA's optimal course based on three examples. This paper confirms the better population size.