Bioprocess and Biosystems Engineering

, Volume 41, Issue 11, pp 1679–1696 | Cite as

Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm

  • Sarma MutturiEmail author
Research Paper


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.

Graphical abstract


Optimal control Dynamic optimization Flower pollination algorithm Fed-batch bioreactor 



Ant colony optimization


Artificial neural networks


Central processing unit


Control vector parameterization


Differential algebraic equation


Differential evolution


Deviation index


Flower pollination algorithm


Genetic algorithm


Iterative dynamic programming


Karush–Kuhn–Tucker (conditions)


Multi-objective optimization differential evolution


Multi-objective flower pollination algorithm


Numerical differentiation formula


Non-dominated sorting genetic algorithm -II


Optimal control problem


Ordinary differential equation


Objective function evaluations


Performance index


Particle swarm algorithm


Particle swarm optimization


Sequential quadratic programming


Vector evaluated genetic algorithm



The Director, CSIR—Central Food Technological Research Institute (CFTRI), Mysore, India, is also acknowledged for supporting this work.

Supplementary material

449_2018_1992_MOESM1_ESM.doc (130 kb)
Supplementary material 1 (DOC 130 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Microbiology and Fermentation Technology DepartmentCSIR-Central Food Technological Research InstituteMysoreIndia
  2. 2.Academy of Scientific and Innovative ResearchGhaziabadIndia

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