Optimization Of Steel Sintering Batching Model Based On Genetic Algorithm

Since the 1980 s,some intelligent optimization algorithms such as genetic algorithm,particle swarm algorithm and BP neural network algorithm have been born.These intelligent algorithms provide a new perspective for the optimization work of all walks of life.The inventors are all inspired by some evolutionary and life phenomena in nature to propose these optimization algorithms.The purpose of this paper is to apply the genetic algorithm in the intelligent algorithm and its improved algorithm to optimize the modeling of steel sintering ingredients,and propose a new way to improve the genetic algorithm.This paper focuses on the process of genetic algorithm and multi-population genetic algorithm to optimize steel sintering ingredients,and introduces the principle of these two algorithms and the steps of the operation.Firstly,the principle of traditional genetic algorithm and its application field are introduced.Then,a variety of genetic algorithm is introduced and this improved genetic algorithm is applied to optimize the modeling of steel sintering batching.In the past,the modeling of steel sintering batching was optimized by traditional genetic algorithm.The authors have searched many websites and related books and found that scholars applied multi-group genetic algorithm to optimize the modeling of sintering ingredients.The author applied multi-group genetic algorithm to optimize sintering batching modeling calculation is an application field innovation,and applied MATLAB programming to empirically demonstrate traditional genetic algorithm and multi-population genetic algorithm.Due to the increasingly complex modeling of sintering ingredients and the increasing number of constraints,the application of traditional genetic algorithms for optimization is likely to fall into local optimum.In order to solve the defects of traditional genetic algorithms,the author proposes a new elite retention strategy for this problem,which can ensure that the final output of the algorithm in the optimization of sintering modeling reaches the global optimal solution and provides the researchers who will study the genetic algorithm elites in the future.A new idea.