An improved ant colony optimization algorithm is proposed

aiming at the problems that traditional ant colony optimization algorithm is easy to fall into the local optimal solution and the search time is too long. Firstly

by establishing a grid map model

the state transition rule is combined with the method of roulette to select the next node

and the fallback strategy is adopted for the ants that are deadlocked to avoid falling into local optimum. At the same time

improve the pheromone enhancement coefficient

improve the pheromone volatilization factor

establish the interlock relationship between the pheromone factor and the required heuristic function factor

shorten the length of the shortest path

reduce the iteration number of the algorithm

and improve the convergence speed of the algorithm. Finally

using MATLAB to simulate

the simulation results show that the improved ant colony optimization algorithm is significantly better than the traditional ant colony algorithm.