基于自适应阈值蚁群算法的路径规划算法

为了克服传统蚁群算法容易陷入局部最优的问题，提高环境适应能力和收敛速度，提出了一种基于自适应阈值的蚁群算法．在优化过程早期，通过阈值对蚂蚁寻优过程进行干预避免其陷入局部最优解．随着迭代次数的增加，阈值对蚂蚁寻优过程的影响不断减小，直至完全由信息素和启发信息来指导蚂蚁寻优．仿真实验验证了优化算法的可行性和有效性．与现有蚁群算法进行比较，实验结果表明：在不同的环境下，文中提出的算法都能快速的规划出一条较优的路径，并且收敛速度和环境适应能力令人满意．

Ant Colony Optimization Based on Self-Adaption Threshold for Path Planning

In order to overcome the traditional ant colony algorithm easy to drop into local optimum, and improve the environmental adaptability and convergence speed of the path planning algorithm, an improved ant colony algorithm based on self-adaption threshold has been proposed in this paper. In the early stages of the optimization process, it uses self-adaption threshold to intervene the optimization process to avoid it dropping into local optimum. With the increase of the number of iterations, the threshold continues the impact on the optimization process, until the optimization process is guided by pheromone and heuristic information completely. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm. Compared with existing ant colony algorithms, the proposed algorithm can plan an optimal path quickly in different environments with satisfactory convergence speed and environment adaptability.