TY - JOUR
T1 - Discrete swarm intelligence optimization algorithms applied to steel–concrete composite bridges
AU - Martínez-Muñoz, D.
AU - García, J.
AU - Martí, J. V.
AU - Yepes, V.
N1 - Funding Information:
Grant PID2020-117056RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.Grant FPU-18/01592 funded by MCIN/AEI/10.13039/501100011033 and by “ESF invests in your future”Grant CONICYT/FONDECYT/INICIACION/11180056
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - Bridge
KW - Combinatorial optimization
KW - Composite structures
KW - K-means
KW - Metaheuristics
UR - http://www.scopus.com/inward/record.url?scp=85133884205&partnerID=8YFLogxK
U2 - 10.1016/j.engstruct.2022.114607
DO - 10.1016/j.engstruct.2022.114607
M3 - Article
AN - SCOPUS:85133884205
VL - 266
JO - Engineering Structures
JF - Engineering Structures
SN - 0141-0296
M1 - 114607
ER -