Research on building optimization design based on genetic algorithm

Abstract

In response to the drawbacks of traditional genetic algorithms, such as premature convergence, low optimization efficiency, and inadequate precision, improvements have been made to traditional genetic algorithms from two aspects: calibration of the fitness function and diversification of the population. This has prevented traditional genetic algorithms from prematurely converging to local optimal solutions and broadened the optimization space. The improved genetic algorithm has been applied to architectural structural optimization design by establishing an optimization mathematical model with the objective of minimizing mass, addressing discrete variable structural optimization problems with stress and cross-sectional size constraints. Comparative optimization design results have been conducted between the improved genetic algorithm and standard genetic algorithms. The results indicate that the evolutionary generations of the improved genetic algorithm are fewer than those of the standard genetic algorithm, with significantly better convergence performance. This has enhanced the computational speed and optimization effectiveness of genetic algorithms in structural optimization applications.

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