Abstract: Two general approaches are adopted in solving dynamic optimization problems in biochemical processes,namely,the analytical and numerical methods.The numerical method based on heuristic algorithms has been widely used,but it is likely to converge to local optimum at a slow convergence speed. An improved differential evolution algorithm(IDEA)was proposed to solve dynamic optimization problems in this paper.In IDEA,a novel representation of the control variables was proposed for effectively solving dynamic optimization problems.A local search vector was designed in IDEA to enhance the local search ability of the algorithm.The efficiency and robustness of the algorithm was illustrated by solving several challenging case studies regarding the optimal control of fed-batch bioreactors.In order to fairly evaluate their advantages,a careful and critical comparison with several other direct approaches was provided.The results indicated that the proposed approach presented the best compromise between robustness and efficiency.

Key words: differential evolution algorithm, dynamic optimization, fed-batch bioreactor

摘要： 动态优化是生物化工过程中的重要课题,求解动态优化问题通常有两种方法:解析法和数值法。基于智能进化算法的数值方法在动态优化中的应用越来越广泛,但是这些方法局部寻优能力不强,容易陷入局部最优,并且求解速度相对较慢。针对这些方法的不足,提出了一种改进的差分进化算法,设计了新的局部寻优算子来增强算法的局部寻优能力,并且采用一种新的控制策略表示方法来求解动态优化问题。通过求解补料分批式生化反应器的动态优化实例,证明了算法的有效性和鲁棒性。通过与其他几种方法进行对比,实验结果表明,所提出的方法在优化结果和计算代价方面都有优势。

关键词: 差分进化算法, 动态优化, 补料分批式生化反应器