Nested Hybrid Differential Evolution For Bi-Level Mixed-Integer Optimization In Metabolic Networks
Numerous bi-level optimization methods have been used to determine optimal strain designs for the genome-scale metabolic networks of bacteria. Such bi-level optimization problems are generally reduced to single-level problems using strong duality theory. However, this approach can exponentially increase computation time because the number of decision variables is increased, and that a growth-coupled production strain cannot be guaranteed. This study is to introduce the two-population nested hybrid differential evolution algorithm that can easily solve the bi-level optimization problem to achieve a set of growth-coupled production strains. It is tested through the simulation of the iAF1260 metabolic network of E. coli.
Keywords: Bi-level Optimization, Differential Evolution, Metabolic Engineering, Evolutionary optimization