Metabolic engineering is a technique that modifies genes that have the potential to produce desired amounts of metabolites via reconstruction of the metabolic networks. Nowadays, gene knockout strategy, a genetic engineering method that has received much attention. Although there are traditional methods of producing metabolites, the production often does not meet the demand level. Furthermore, there were few previous works on relational modelling framework, for instance OptKnock and OptGene but these frameworks faced problems because of their inefficiencies in handling multivariable and multimodal functions optimization algorithms. This paper pro-poses a hybrid of the bacterial foraging optimization algorithm (BFO) and dynamic flux balance analysis (DFBA) to identify the best set of genes to be knocked out in order to enhance the production of succinate by using Escherichia coli core model. The experimental outcomes consist of the growth rate, production rate and a list of knockout genes. BFODFBA enhanced the production rate of succinate when compared to previous works.