关键词: 免疫粒子群优化, 多样性, 支持向量机, 熔融指数预报, 参数寻优

Abstract: 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.

Key words: immune clone particle swarm optimization, diversity, support vector machine, melt index prediction, parameters optimization