Discrete swarm intelligence optimization algorithms applied to steel-concrete composite bridges

Composite bridge optimization might be challenging because of the significant number of variables involved in the problem. The optimization of a box-girder steel-concrete composite bridge was done in this study with cost and CO2 emissions as objective functions. Given this challenge, this study proposes a hybrid algorithm that integrates the unsupervised learning technique of k-means with continuous swarm intelligence metaheuristics to strengthen the latter's performance. In particular, the metaheuristics sine-cosine and cuckoo search are discretized. The contribution of the k-means operator regarding the quality of the solutions obtained is studied. First, random operators are designed to use transfer functions later to evaluate and compare the performances. Additionally, to have another point of comparison, a version of simulated annealing was adapted, which has solved related optimization problems efficiently. The results show that our hybrid proposal outperforms the different algorithms designed.

Automated summary

This study proposes a hybrid algorithm that integrates the unsupervised learning technique of k-means with continuous swarm intelligence metaheuristics to optimize composite bridges. The results show that the hybrid proposal outperforms different algorithms designed.