Evolutionary and swarm intelligence-based approaches for optimization of lipase extraction from fermented broth

Evolutionary and swarm intelligence-based optimization approaches, namely genetic algorithm (GA) and particle swarm optimization (PSO), were utilized to determine the optimal conditions for the lipase extraction process. The input space of the nonlinear response surface model of lipase extraction served as the objective function for both approaches. The optimization results indicate that the lipase activity was significantly improved, more than 20 U/g of dry substrate (U/gds), in both approaches. PSO (133.57 U/gds in the 27th generation) outperforms GA (132.24 U/gds in the 320th generation), slightly in terms of optimized lipase activity and highly in terms of convergence rate. The simple structure associated with the effective memory capability of PSO renders it superior over GA. The proposed GA and PSO approaches, based on a biological phenomenon, are considered as natural and thus may replace the traditional gradient-based optimization approaches in the field of downstream processing of enzymes.