Optimization of free-form surface measurement path based on improved genetic algorithm

In order to improve the detection efficiency of the CMM for free-form surface measurement points, in view of the slow convergence of traditional genetic algorithms and easy to fall into the local optimal solution, the adaptive adjustment mechanism is introduced, from the fitness distribution of the population and the individual adaptation. The two aspects of the degree value realize the adaptive parameter adjustment of the crossover and mutation probability, which improves the efficiency of the algorithm and reduces the probability of prematurity; the use of the greedy crossover operator and the greedy inversion mutation operator accelerates the convergence speed of the algorithm. The experimental results show that the improved genetic algorithm can optimize the free-form surface measurement path more efficiently and with high quality.