Shape optimization of arch dams by metaheuristics and neural networks for frequency constraints

The main aim of this paper is to propose an efficient soft computing based methodology to achieve optimal shape design of arch dams subjected to natural frequency constraints. genetic algorithm (ga) and particle swarm optimization (pso) as two popular metaheuristics are employed to perform optimization task. as in the present paper fluid–structure interaction is considered, computing the natural frequencies by finite element analysis (fea) during the optimization process is time consuming. in order to reduce the computational burden, back propagation (bp) and radial basis function (rbf) neural networks are used to predict the arch dam natural frequencies. the numerical results show that pso incorporating bp provides the best results.