Research On Improvement And Application Of Adaptive Genetic Algorithm Model

Genetic algorithm is a general-purpose optimization algorithm.Its coding technology and genetic operation are relatively simple,and the restrictive conditions for optimization problems are very low.Therefore,it is widely used to solve practical engineering problems.However,its theory and method are not yet mature,and the algorithm itself has some shortcomings to be further improved.Firstly,this paper analyzes the principle,flow and basic genetic operators of genetic algorithm in detail,and clarifies the different application backgrounds should choose the appropriate operation mode to lay the foundation for the follow-up work.Secondly,in view of the problem that the traditional adaptive genetic algorithm is easy to fall into the local extremum,it is found that the strategy of adaptively adjusting the value difference is difficult to reflect the individual difference in the later stage of the algorithm due to the decrease of the value difference.This paper proposes an adaptive genetic algorithm based on individual ordering,namely AGA-SNS.The improved algorithm uses the sorting number of the individual fitness value instead of the specific fitness value to adaptively adjust.This method can increase the crossover rate and the mutation rate in the middle and late stages of the population,and help the algorithm to jump out of the local optimum.The results show that the improved algorithm is superior to the other two adaptive improved algorithms in terms of convergence speed and convergence accuracy.Thirdly,for the traditional path planning algorithm,such as path unreachability and large-scale optimization calculation,the algorithm has large computational complexity and low convergence precision.It is proposed to use AGA-SNS to optimize the intermediate nodes and combine Dijkstra to find the shortest path algorithm to complete the nodes.The path between the two forms a complete path,ensuring that the paths in the genetic operation are all feasible paths.Compared with the traditional genetic algorithm,the experimental results show that the improved algorithm has achieved obvious results in both convergence accuracy and optimization ability.Finally,this paper analyzes the basic theory and properties of non-uniform rational B-Splines(NURBS)curves,and finds the influence of node vectors on the shape of NURBS curves.A method based on AGA-SNS and least squares is proposed.Combined node optimization algorithm.For the data points to be fitted,the mathematical optimization model of curve fitting under unconstrained,normal and tangential constraints is established respectively,and the optimal vector combination is found through the algorithm.The simulation results show the feasibility and effectiveness of the algorithm.