Paper Title
Nested Hybrid Differential Evolution For Bi-Level Mixed-Integer Optimization in Metabolic Networks
Abstract
Numerous bi-level optimization methods have been used to determine optimal strain designs for the genomescale
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