In the initial stage of the traditional ant colony algorithm,pheromone distribution is uniform,which leads to high randomness in path selection probability and slow search speed.To overcome the problem,an improved ant colony algorithm using mixed parameters was designed.At the beginning of the algorithm,a genetic algorithm was introduced,and the fitness function of the genetic algorithm was improved.An evaluation point was set to determine that the genetic algorithm entered the ant colony algorithm at the right time,the pheromone volatilization factor of the algorithm was adjusted adaptively.For the crossover rate and mutation rate of the genetic algorithm and the information factor and expectation factor of ant colony algorithm,the mutation mixed parameters were adopted,to give full play to the advantages of the four-parameter factors in the algorithm.An evaluation point of the path evolution rate was set behind the ant colony algorithm to determine whether to perform the genetic mutation operation again,the purpose was to make the ant colony algorithm jump out of the local optimum.Finally,the B-spline curve smoothing mechanism was introduced into the algorithm.Experimental results show that using the improved algorithm,path length and path turning point number are reduced significantly in simple and complex environments.It can find the global optimum faster and more accurately than the other three algorithms.