无线电工程

针对传统蚁群(Ant Colony Optimization, ACO)算法在路径规划中存在收敛速度慢、易陷入局部最优解和算法能耗高等问题，提出了一种改进的ACO算法。针对算法迭代初期盲目搜索的问题，改进了初始信息素的分配方式，进行不均匀分配；考虑到路径成本，将多目标函数用于改进启发式函数；采用改进伪随机转移策略，加快算法的收敛速度；改进了信息素更新规则和自适应挥发系数，平衡了收敛速度和全局寻优；将算法得出的最优路径进行节点优化，减少路径长度和转弯次数，充分发挥移动机器人的灵活性和机动性。通过仿真和对比实验验证了算法的可行性和有效性。

To solve the problems of slow convergence, easy to fall into local optimal solution and high energy consumption of traditional Ant Colony Optimization(ACO) algorithm in path planning, an improved ACO algorithm is proposed. In order to solve the problem of blind search in the early stage of algorithm iteration, the distribution of initial pheromone is improved and uneven distribution is carried out. Considering the path cost, multiple objective functions are used to improve the heuristic function. An improved pseudo-random transfer strategy is used to accelerate the convergence of the algorithm. The pheromone updating rule and adaptive volatilization coefficient are improved, and the convergence speed and global optimization are balanced. Finally, the optimal path obtained by the algorithm is optimized, which reduces the path length and the number of turns, and gives full play to the flexibility and maneuverability of the mobile robot. The feasibility and effectiveness of the algorithm are verified by simulation and comparison experiments.