Research Of Gear Defect Recognition Based On Genetic Hill-climbing Algorithm

In this paper,the mountain climbing and genetic algorithm are studied from theory and experiment two aspects,and the improved algorithm is applied to improve the gear defect recognition rate.This subject used the PCA algorithm to count the distribution regularity of each generation individual and reconstruct several invaded individual and came up with the method which improves the convergence rate of hill-climbing algorithm by using the adaptive step size,which has good theoretical significance and practical value for advancing the genetic algorithm.First of all,this paper introduces the overall model of gear classification system,including image preprocessing、feature selection methods、feature reduction methods、the classification of gear.The basic principle of SVM algorithm is seriously studied,and the selection of parameters in SVM training algorithm is discussed.Secondly,in view of the premature convergence of traditional genetic algorithm,using the PCA algorithm to count the distribution regularity of each generation individual and reconstruct several invaded individual in the projection space,then using the projection matrix to project the invaded individuals onto the original space again,which effectively increases the individual distribution range and reduces the possibility of local convergence.Then,in view of the problem of slow convergence speed of the climbing algorithm with restricted step size,which can only optimize unit function,this paper proposes a climbing algorithm based on adaptive regulation step size.First of all,in the stage of random solutions generation,generating random solutions in a given constraint range to promise restraining to the present peak of wave,and using the difference value as the step size generates the next solution,step as a solution by,until the current solution is better than new solution;Secondly,detecting the norm of difference value of a fixed sequence number solution during the process of the generating of new solutions adjusting the range of step size adaptively according to whether the norm value meet certain precision and generating new constrainted precision;Finally,the algorithm will not finishuntil the new solution meet the finial constrainted precision.Finally,the paper uses the improved hill-climbing algorithm to optimize results of genetic algorithm.The detailed study result is obtained by analyzing and sorting the experimental data of the improved hill-climbing genetic algorithm.