An improved bacteria foraging optimization algorithm and its application in soft measurement modeling

Demand for model parameter optimization in soft measurement modeling,on the basis of analyzing bacteria foraging optimization algorithm(BFOA) and particle swarm optimization(PSO)algorithm,a novel bacterial foraging particle swarm based hybrid optimization algorithm(BSOA) is proposed by taking advantage of both BFOA and PSO.The new algorithm introduces particle moving inspiration of PSO into BFOA,which effectively solves the blindness of the location update in BFOA.The new method is used for typical function optimization and optimization of the parameters of least squares support vector machine(LSSVM) model in research octane number(RON).Simulation results show that this method enhances the global optimization capability and convergence rate of the algorithm,to some extent,improves the prediction precision and generalization ability of the model too.