TY - GEN

T1 - Finite element model selection using particle swarm optimization

AU - Mthembu, Linda

AU - Marwala, Tshilidzi

AU - Friswell, Michael I.

AU - Adhikari, Sondipon

PY - 2011

Y1 - 2011

N2 - This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has the least number of updated parameters and has the smallest parameter variable variation from the mean material properties. Two different objective functions are used to compare performance of the PSO algorithm.

AB - This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has the least number of updated parameters and has the smallest parameter variable variation from the mean material properties. Two different objective functions are used to compare performance of the PSO algorithm.

UR - http://www.scopus.com/inward/record.url?scp=79960302098&partnerID=8YFLogxK

U2 - 10.1007/978-1-4419-9831-6_6

DO - 10.1007/978-1-4419-9831-6_6

M3 - Conference contribution

AN - SCOPUS:79960302098

SN - 9781441998309

T3 - Conference Proceedings of the Society for Experimental Mechanics Series

SP - 41

EP - 52

BT - Dynamics of Civil Structures - Proceedings of the 28th IMAC, A Conference on Structural Dynamics, 2010

PB - Springer New York LLC

ER -