Application of improved PSO algorithm and wavelet analysis in foundation settlement prediction

Abstract

The authors used the improved Particle Swarm Optimization(PSO) algorithm that has two subgroups and a mutation operator binding wavelet analysis to optimize neural network parameter to forecast the foundation settlement.Since the basic particle swarm optimization easily falls into the local minimal value,the authors divided the particle swarm into two subgroups.In one subgroup the inertia weight of the particle swarm optimization algorithm decreased when the iterations increased.And in the other subgroup the particle swarm optimization algorithm adopted big inertia weight to do the overall situation search.And the authors used this improved algorithm binding wavelet analysis to optimize the neural network parameter to forecast foundation settlement.The experimental result indicates that this method has strong global and local search ability,and has high forecast precision.