基于改进PSO算法的酒精发酵过程补料优化

Optimization of Feeding Rate for Alcohol Fermentation Based on Improving Particle Swarm Optimization

针对基本粒子群优化算法存在的早熟收敛问题,提出了一种保持粒子活性的改进PSO算法:当粒子失活时,对粒子进行变异或扰动操作,重新激活粒子,使粒子能够有效地进行全局和局部搜索,并将改进的PSO算法应用到酒精流加发酵过程的补料优化。实验结果表明,运用改进的PSO算法在进行流加发酵过程的补料优化时,它的寻优性能良好,而且寻优速度很快,可以提高最终产物14%的产量。

To overcome the problem of premature convergence on particle swarm optimization（PSO）,the paper proposes an improved particle swarm optimization （IPSO） called keeping particles active PSO,which keep the diversity of the particle swarm.When particles lose activity, a special mutation or perturbation was used to activate particles and to make particles explore the space more efficiently.The IPSO is used to optimize the feeding rate of alcohol fermentation.The experimental results show that the IPSO can find better feeding rate quickly and the yield increases by 14%.