Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm.

By D. Sarkar, J. Modak. 2005

166 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0b1e94a9d012e7b7738a0aaa821d1b3c549e8b89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Moritz Diehl
2. Boris Houska
3. Kok Lay Teo
4. Jan F. M. Van Impe
5. L. Caccetta
6. P. V. Erdeghem
7. Filip Logist
8. David Q. Mayne

In Control and Systems Engineering

1. Boris Houska
2. Moritz Diehl
3. Kok Lay Teo
4. Jan F. M. Van Impe
5. P. V. Erdeghem
6. Filip Logist
7. Zhaohua Gong
8. Chongyang Liu

In Molecular Biology

1. Jan F. M. Van Impe
2. Moritz Diehl
3. Boris Houska
4. P. V. Erdeghem
5. Filip Logist
6. Zhaohua Gong
7. Chongyang Liu

Citations in the corpus

• Efficient deterministic multiple objective optimal control of (bio)chemical processes (2009, methodology): “…routines (Muske and Peyton Jones, 2006; Logist et al., 2007), or (ii) stochastic genetic algorithms in which a population of solutions is updated based on repeated cost computations (and, hence, model simulations) in order to evolve gradually to the Pareto set (Sarkar and Modak, 2004, 2005).”
• Robust multi-objective optimal control of uncertain (bio)chemical processes (2011, background): “, the amount of product over the amount of substrate fed, was added as 64 a competing objective by Sarkar and Modak (2005) for the fed-batch pro65 duction of lysine.”
• Robust multi-objective optimal switching control arising in 1,3-propanediol microbial fed-batch process (2017, background): “On the other hand, muti-objective optimal control (MOC) problems often arise in bioprocesses and have been extensively investigated; see, for example [27, 28, 29].”