Application of PSO and QPSO algorithm to estimate parameters from kinetic model of glutamic acid batch fermetation

Estimation of parameters from kinetic model of batch fermentation is a tough searching problem. Unfortunately, the traditional approaches easily get stuck in a local minimum. So particle swarm optimization (QPSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm were used to estimate parameters from kinetic model of batch fermentation in this paper. The result compared with artificial neural networks (ANN) and genetic algorithm (GA) shows that the estimation precision of PSO is higher than ANNpsilas and GApsilas, the estimation precision of QPSO is highest. QPSO algorithm is an effective way to estimate such kind of parameters with complex nonlinear model from kinetic model of batch fermentation.