Paper Title :Nested Hybrid Differential Evolution For Bi-Level Mixed-Integer Optimization in Metabolic Networks

Author :Feng-Sheng Wang

Article Citation :Feng-Sheng Wang , (2017 ) " Nested Hybrid Differential Evolution For Bi-Level Mixed-Integer Optimization in Metabolic Networks " , International Journal of Advances in Science, Engineering and Technology(IJASEAT) , pp. 52-55, Volume-5, Issue-4, Spl. Iss-1

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

Type : Research paper

Published : Volume-5, Issue-4, Spl. Iss-1

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