Abstract: Particle Swarm Optimization（PSO） algorithm and Differential Evolution（DE） algorithm are analyzed.A novel Differential Evolution-Particle Swarm Optimization based hybrid optimization algorithm（DEPSO） is proposed by taking advantage of both PSO and DE.The procedure of optimization is divided into two phases and the particles are divided into two sub-swarms.One sub-swarm searches via PSO and the other searches via DE at the same time.Evolution speed factor of the swarm is introduced in this algorithm and two sub-swarms exchange information in each iteration to avoid local optimum.Experiments on four complex functions with high dimension show that the improved algorithm outperforms traditional PSO and DE in robustness，converging speed and precision，global searching ability.The improved algorithm is applied to construct a soft sensor model for real-time measuring the ethylene yield.Application results show that this model has high prediction precision as well as good generalization ability.

Key words: particle swarm optimization, differential evolution, hybrid optimization algorithm, soft sensing

摘要： 对基本粒子群算法（PSO）和差分进化算法（DE）进行了分析，有机结合两种进化算法提出了一种新型差分进化粒子群混合优化算法，该算法将优化过程分成两阶段，两分群分别采用PSO算法和DE算法同时进行。迭代过程中引入进化速度因子并通过群体间的信息交流阻止算法陷入局部最优。对4个高维复杂函数寻优测试表明算法的鲁棒性、收敛速度和精度，全局搜索能力均优于常规PSO和DE。将提出的改进算法用于乙烯收率软测量建模，应用结果表明模型精度较高、泛化性能较好。

关键词: 粒子群优化, 差分进化, 混合优化算法, 软测量