Application Of Hybrid Membrane Optimization Algorithm Based On Membrane Computation To Grain Temperature Prediction

Food is an important strategic material that bears on the national economy and people’s livelihood.It is the most basic material foundation for sustainable economic development.Ensuring food security is always a top priority and should not be neglected at any time.In many measures to ensure food security,food temperature is one of the key factors,so the prediction of food temperature is particularly important.This paper takes the temperature of each monitoring point of the granary in Wuhan Rice Trading Center and the humidity inside and outside the granary as the research object and establishes a prediction model for grain pile temperature based on BP neural network.In this paper,the mixed film evolution algorithm for grain temperature prediction is studied.The main work is as follows:(1)Firstly,the improved genetic algorithm(IGA),improved particle swarm optimization(IPSO),and improved Fruit fly optimization algorithm(IFOA)are proposed.In the IGA algorithm,firstly,to balance the exploration ability and development ability of the algorithm,a combination selection operator,adaptive mutation operator,and adaptive crossover operator are designed to improve the search efficiency of the algorithm.In the IPSO algorithm,to improve the convergence speed and balance global search and layout search,concave function decreasing inertia weight and adaptive learning factor are added.In the IFOA algorithm,a threshold is set to detect whether the current algorithm is trapped in local optimality to avoid it from falling into local optimality,and different methods are selected to update the individual positions of fruit flies according to the threshold.Finally,the effectiveness of the proposed algorithm is verified by comparison experiments.(2)Based on the previous three optimization models,an organization-type P system with degree 4 is proposed.A hybrid membrane optimization algorithm(HMEA)based on membrane computation was established by analyzing three elements of the membrane computation model: object,reaction rule,and membrane structure.This algorithm not only retains the basic characteristics of conventional membrane computation but also absorbs the research results of the genetic algorithm,particle swarm optimization algorithm,and Fruit fly optimization algorithm.To balance the exploration ability and development ability of the whole system reasonably,the genetic algorithm is used as the main sub-algorithm,the particle swarm optimization algorithm is mainly used for local search,and the Fruit fly optimization algorithm is mainly used for global search.In this system,crossover operators and mutation operators of the genetic algorithm are redesigned according to the characteristics of the organizationtype P system,and improved genetic algorithm,improved particle swarm optimization algorithm,and improved Fruit fly optimization algorithm are used as sub-algorithms to evolve objects.Different cell communication rules are set for information transmission and sharing of objects in the system.Finally,the effectiveness of the HMEA algorithm and the improvement of the accuracy of the prediction model is verified by simulation experiment and comparison experiment.