基于改进混沌粒子群优化算法的聚丙烯生产调优

Optimization of polypropylene production based on improved chaotic particle swarm algorithms

通过算法混合提出了一种改进混沌粒子群优化算法。将混沌搜索融入到粒子群优化算法中,建立了早熟收敛判断和处理机制,显著提高了优化算法的局部搜索效率和全局搜索性能。将改进混沌粒子群优化算法应用于聚丙烯生产调优中,首先建立了聚丙烯最优牌号切换模型,然后采用改进混沌粒子群优化算法求解该最优牌号切换模型。优化结果:表明,与常规混沌粒子群优化算法相比,改进混沌粒子群优化算法具有更佳的优化效率和全局性能。

An improved chaotic particle swarm optimization algorithm is proposed through algorithm hybrid. Chaotic searching is integrated into particle swarm optimization algorithm. Judgment and handling mechanism of local convergence is developed. It greatly enhances the local searching efficiency and global searching performance of algorithm. Optimization of polypropylene production based on improved chaotic particle swarm algorithms is studied. Firstly, a model of grade transition for polypropylene production process is developed. And the model of grade transition is solved by using improved chaotic particle swarm optimization algorithm. The results show that the proposed optimization algorithm is superior to the traditional chaotic particle swarm optimization algorithm one in the optimization efficiency and global performance.