A comparison of algorithms for the optimization of fermentation processes

Abstract

The optimization of biotechnological processes is a complex problem that has been intensively studied in the past few years due to the economic impact of the products obtained from fermentations. In fed-batch processes, the goal is to find the optimal feeding trajectory that maximizes the final productivity. Several methods, including Evolutionary Algorithms (EAs) have been applied to this task in a number of different fermentation processes. This paper performs an experimental comparison between Particle Swarm Optimization, Differential Evolution and a real-valued EA in three distinct case studies, taken from previous work by the authors and literature, all considering the optimization of fed-batch fermentation processes.