Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm

There exist several optimization strategies such as sequential quadratic programming (SQP), iterative dynamic programing (IDP), stochastic-based methods such as differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSA), and ant colony optimization (ACO) for finding optimal feeding profile(s) during fed-batch fermentations. Here in the present study, flower pollination algorithm (FPA) which is inspired by the pollination process in terrestrial flowering plants has been used for the first time to find the optimal feeding profile(s) during fed-batch fermentations. Single control variable, two control variables and state variable bounded problems were chosen to test the robustness of the FPA for optimal control problems. It was observed that FPA is computationally less intensive in comparison with other stochastic strategies. Thus, obtained results were compared to other studies and it has been found that the FPA converged either to newer optima or closer to the established global optimum for the cases studied.