Sizing populations for serial and parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Modeling and application of MR dampers in semi-adaptive structures
Computers and Structures
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Identification of Bouc-Wen type models using multi-objective optimization algorithms
Computers and Structures
Identification of structural models using a modified Artificial Bee Colony algorithm
Computers and Structures
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In this paper, two variants of the particle swarm optimization (PSO) algorithm are employed for the identification of Bouc-Wen hysteretic systems. The first variant is simple while the other is enhanced, as it implements additional operators. The algorithms are utilized for the identification of a Bouc-Wen hysteretic system that represents a full scale bolted-welded steel connection. The purpose of this work is to assess their comparative performance against other evolutionary algorithms in a highly non-linear identification problem on various levels of computational budget. The enhanced PSO algorithm outperforms its competitors in terms of both accuracy and robustness.