Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

Abstract

Almost all optimization techniques are restricted by the problems' dimensions and large search spaces. This research focuses on a special hybrid method combining two meta-heuristic techniques, genetic algorithms (GA) and particle swarm optimization (PSO) that aims at overcoming this issue. This method investigates the potential impact of constraints (or the feasible regions) on the search pattern of GA and PSO. The proposed algorithm was applied for parameter estimation of batch fermentation process for alkaline protease production by Bacillus licheniformis in submerged culture. Furthermore, a comparison of proposed hybrid GA/PSO with pure GA and pure PSO was carried out. The results revealed that combination of these two meta-heuristic algorithms speeds up the search (about two-fold faster) in comparison to pure algorithms, since it benefits from synergy. Hence, the proposed method can be considered as an applicable method for parameter estimation of biological models in particular for large search space problems. Also, it was concluded that PSO has a slightly better performance and possesses better convergence and computational time than GA.

Keywords

• Kinetic parameters
• parameter estimation
• protease
• genetic algorithm
• particle swarm optimization
• penalty function