Finite element model selection using Particle Swarm Optimization
  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.
