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.