Microbial fermentation optimizing method based on artificial fish school algorithm

Abstract

The invention discloses a microbial fermentation optimizing method based on an artificial fish school algorithm. The method includes steps of establishing a microbial fermentation data set, dividing the microbial fermentation data set into a training data set and a testing data set randomly, constructing a BP neural network, using the training data set for training the BP neural network, performing binary encoding on each microbial fermentation control parameter according to the precision and data range of the fermentation control parameters, calculating the fitness value of each artificial fish by taking the BP neural network as the fitness function of the artificial fish school algorithm, applying a bunching operator or a rear-end operator on the current fish school, judging whether the fitness value is improved or not after the bunching operator and the rear-end operator are applied to the current artificial fish school, applying a foraging operator on the artificial fish school, calculating the fitness value of the artificial fish school and recording the optimal individual. According to the invention, the optimal control parameter combination can be acquired based on the current fermentation data and no experiment redesign is required.

Description

technical field [0001] The invention relates to the field of microbial fermentation control, in particular to a microbial fermentation optimization method based on an artificial fish swarm 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 a...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above technical problems, the present invention provides a microbial fermentation optimization method based on the artificial fish swarm algorithm, which is used to solve the need to pre-design fermentation experiments in the existing microbial fermentation control process, and it is not easy to obtain the optimal fermentation individual and optimal fermentation control parameters The problem

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