Melt index (MI) is considered as one of the important quality variables of propylene (PP) polymerization

which determines the products specifications. Thus

a reliable estimation of MI is crucial in quality control. Addressing the deficiency of the particle swarm optimization (PSO) algorithm

whose particles are easy to sink into premature convergence and run into local optimization in the iterative process

this article introduces the selection strategy

the immune clone PSO(ICPSO) algorithm

which makes the particles of ICPSO maintain the diversity during the iterative process so as to overcome the premature convergence of PSO. The ICPSO was used to optimize the parameters of weighted least support vector machine (WLSSVM) to predict the melt index of polypropylene

so the optimized model ICPSO_WLSSVM was obtained. Researches on the optimized model were illustrated with the real plant of propylene polymerization

and the results showed that the proposed approach had great prediction accuracy and validity.