基于改进粒子群算法的诺西肽发酵过程优化

Nosiheptide Fermentation Process Optimization Based on Improved PSO

在诺西肽补料分批发酵动力学模型的基础上建立了诺西肽发酵过程产量优化模型,根据发酵工艺选取了决策变量,并确定了变量的边界约束范围.针对标准粒子群算法在求解复杂优化问题时易于陷入局部最优的问题,利用混沌序列具有随机性和遍历性的特点,引入混沌迁移算子,提出了一种改进的粒子群算法.利用改进算法对所建立的诺西肽发酵优化模型进行求解,大大提高了最终产物的产量,证明了所提改进粒子群算法的有效性.

A production optimization model of nosiheptide fermentation process was built on the basis of the kinetic models of nosiheptide fed-batch fermentation. Decision variables were selected according to the technical flow, and the scope of the variable boundary constraints was decided. PSO （particle swarm optimization） algorithm is easy to fall into local optimum when solving complex optimization problems. To solve this problem, according to the randomicity and ergodicity of chaotic sequences, an improved PSO algorithm was proposed by introducing chaotic migration operator into PSO. The improved PSO algorithm was used to solve the production optimization model of nosiheptide fermentation, and the end time fermentation production was greatly improved. The results showed the effectiveness of the proposed algorithm.