Discrete swarm intelligence optimization algorithms applied to steel–concrete composite bridges. (1st September 2022)

• Record Type: Journal Article
• Authors: Martínez-Muñoz, D.; García, J.; Martí, J.V.; Yepes, V.
• Is Part Of: Engineering structures. Volume 266(2022)
• Publication Date: 2022-09-01
• Subjects: Combinatorial optimization -- Bridge -- Metaheuristics -- Composite structures -- K-means; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114607
• Languages: English
• ISSNs: 0141-0296

Abstract

Abstract: 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 CO 2 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. Highlights: A cost and CO2 emissions optimization a three-span steel–concrete composite bridge has been performed. The optimization considers 35 design variables on average 55 possible choices for each variable. The performance and robustness of a hybrid k-means swarm intelligence metaheuristic is studied for this optimization problem. Hybrid k-means algorithm results are compared with other discrete trajectory based and swarm algorithms.