Research on building optimization design based on genetic algorithm

Hongping Shen; Ling Wu

doi:10.1117/12.3051667

18 November 2024 Research on building optimization design based on genetic algorithm

Hongping Shen, Ling Wu

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340308 (2024) https://doi.org/10.1117/12.3051667
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongping Shen and Ling Wu "Research on building optimization design based on genetic algorithm", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340308 (18 November 2024); https://doi.org/10.1117/12.3051667

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