关键词: 自动导引小车, 路径规划, 蚁群优化算法, 信息素负反馈, 分层化选择

Abstract: The existing ant colony algorithm is applied to the path planning of automated?guided vehicle, which is slow in the convergence and easily fails into the local optimum. To solve these problems, this paper proposes a Hyper Heuristic Ant Colony Optimization algorithm based on pheromone negative feedback（ACONhh） for path planning of mobile robots. The algorithm makes full use of historical search information and continues to gain error experience, thus further leads ant colony to explore optimal path. Hierarchical selection of feasible nodes is adopted to accelerate the initial convergence rate of the algorithm. Meanwhile, the volatility factor changes constantly with an analogous?parabola, and pheromone update mechanism is adjusted to improve the randomness of global search. The convergence of ACONhh algorithm is strictly proved. Simulation and experimental results show that the convergence speed and global search performance of the proposed algorithm are outperform those of popular ACO, ACOhh and ACOihh algorithms.

Key words: automated guided vehicle, path planning, ant colony optimization, pheromone negative feedback mechanism, hierarchical selection