Fed-batch optimization of αα-amylase and protease-producing Bacillus subtilis using genetic algorithm and particle swarm optimization

Genetic algorithm (GA) and particle swarm optimization (PSO) were implemented to select sets of decision variables for optimal feeding profiles of fed-batch culture of recombinant Bacillus subtilis   ATCC 6051a. Both GA and PSO were employed to optimize the volumetric production of recombinant extracellular αα-amylases as desirable products and native proteases as undesirable products. The model contains higher-order model equations (14 state variables). The optimization methodology for the dual-enzyme system was coupling Pontryagin's optimum principle with the Luedeking–Piret equation reflecting experimental observations. The optimal solutions attained by using GA and PSO were comparable. Specifically, the maximum specific αα-amylase productivity was 18% and 3.5% higher than that of the experimental results and a simplified Markov chain Monte Carlo (MCMC) method, respectively. Nevertheless, GA consumed computational time approximately 17% lower than in case of PSO.