Memetic algorithm based microbial fermentation control and optimization method

Abstract

The present invention discloses a Memetic algorithm based microbial fermentation control and optimization method comprising the following steps: establishing a microbial fermentation dataset, constructing a back-propagation (BP) neural network, using the training dataset to train the BP neural network, performing binary coding on microbial fermentation control parameters, executing a crossover operator, adopting a hill-climbing algorithm to execute a local search operator, executing a mutation operator, executing the local search operator again, taking the selected preferred individuals as a current solution, taking the BP neural network that is qualified in the training in step four as a fitness function to calculate the fitness value of each individual in a population, then updating the population by virtue of a selected operator, finding optimum individuals from a new population and recording an optimum control parameter combination. The Memetic algorithm based microbial fermentation control and optimization method can be used for obtaining the optimum control parameter combination according to the existing fermentation data, and the redesign an experiment is not needed.

Description

technical field [0001] The invention relates to the field of microbial fermentation, in particular to a method for optimizing microbial fermentation control based on a Memetic algorithm. Background technique [0002] The microbial fermentation process is a highly complex and nonlinear process, and it is difficult to model the fermentation process with an accurate mathematical model. In recent years, with the development of computational intelligence algorithms, more and more computational intelligence algorithms have been applied to the modeling and optimization control of microbial fermentation processes. For example, BP neural network, genetic algorithm, etc. are used to model and optimize the fermentation process. In the prior art, the BP neural network and the genetic algorithm are often applied alone to the modeling and optimization control of the fermentation process. There is also a joint application of BP neural network and genetic algorithm to model and optimize t...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above-mentioned technical problems, the present invention provides a microbial fermentation control optimization method based on the Memetic algorithm, which is used to solve the problem that the existing microbial fermentation control process requires pre-designed experiments, and it is difficult to obtain the optimal individual for fermentation, so as to improve the search rate of individual fermentation and convergence

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