Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process

A new hybrid multi-objective differential evolution (MODE) algorithm is proposed that combines the
MODE algorithm for the global space search with a dynamical local search (DLS) method for the local space search.
HMODE-DLS algorithm was validated using the tri-objective DTLZ7 test problem and the results were compared with
MODE algorithm with respect to four performance metrics. In addition to HMODE-DLS, another three algorithms
were used to solve two multi-objective optimization cases in an industrial lysine bioreactor at different feeding conditions.
Case 1 considers maximizing lysine’s productivity and yield. While case 2 studies the maximization of productivity
along with minimization of total operating time. In all cases, theoretical and industrial, HMODE-DLS showed a
better performance with a better quality Pareto set of solutions. The Pareto front of case 1 found by HMODE-DLS was
compared with a recent study trade-off, and the current non-dominated solutions values were found to be improved.
This indicates that the lysine production process is enhanced. For case 2, the switching time from fed-batch to batch
was found to be the key decision variable. Generally, these findings indicate the effectiveness of HMODE-DLS for the
studied cases and its potential in solving real world complex problems.