Improved Ant Colony Algorithm Based on Parameters Optimization for AGV Path Planning

Abstract

Path planning is a key technology in the research of Automatic Guided Vehicle (AGV). To solve the problem that the traditional Ant Colony Optimization (ACO) algorithm has poor convergence, low search efficiency, and easy to fall into the local optimality problems in AGV path planning, some improved methods are proposed in this paper. Through initial pheromones non-uniform and directed distribution to determine the search direction in the early stage, the search efficiency and convergence speed of the algorithm is improved. By adding the adaptive adjustment strategy of the iterations number, the computation amount and time complexity of the algorithm are greatly reduced. The parameters of ACO have an important influence on the convergence and the optimization effect, but there are no scientific bases to decide the values. To solve the above problem the improved ACO parameters are optimized by using the genetic algorithm (GA) which has good global search ability and easy fusion with other algorithms to find the parameters combination for the best performance of ACO. Simulation experiments in different environments show that the improved ACO has a better optimization effect and higher search efficiency compared with the traditional ACO.