Abstract
This paper proposes an improved ant colony optimization (IACO) algorithm to improve the optimization ability in AGV path planning. Firstly, aiming at the problem of initial parameter selecting relying on historical experience in the ant colony optimization (ACO) algorithm, the particle swarm optimization (PSO) algorithm is investigated to optimize the initial parameters in the ant colony algorithm by taking the shortest path length as its fitness to obtain the optimal initial parameters. Secondly, in view of the slow convergence speed of the traditional ant colony optimization algorithm, this paper studies the elite ant principle and the expelling behavior of the cockroach algorithm to make the pheromone updating mode better. With the above method, the new pheromone distribution on the map is more conducive for AGV to find the shortest path faster, which can significantly improve the convergence speed of the ACO algorithm. Ultimately, the experimental results show that the path length and speed of AGV path planning based on IACO algorithm is better than traditional ACO algorithm.
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