Particle swarm algorithm based optimization method for microbial fermentation

Abstract

The present invention discloses a particle swarm algorithm based optimization method for microbial fermentation. The method comprises the following steps of: establishing a microbial fermentation data set; randomly dividing the microbial fermentation data set into two parts: a training data set and a test data set; building up a BP neural network; training the BP neural network; executing a binary encoding of a control parameter of the microbial fermentation to obtain an initial particle swarm; calculating a fitness value of each particle by using the BP neural network as a fitness function; executing a self-extremum operator of a particle; executing a global extremum operator of the particle; executing a speed-displacement model operating operator; and calculating the fitness value of a new-generation particle after changing location by using the BP neural network as the fitness function, to obtain an optimal control parameter combination. The method disclosed by the invention can obtain the optimal control parameter combination according to the existing fermentation data without redesigning an experiment.

Description

technical field [0001] The invention relates to the field of microbial fermentation, in particular to a method for optimizing microbial fermentation based on a particle 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 and optimize th...

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Problems solved by technology

[0007] In order to solve the above-mentioned technical problems, the present invention provides a microbial fermentation optimization method based on particle swarm optimization 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. question

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