基于改进混合蛙跳算法的移动机器人路径规划

针对混合蛙跳算法(SFLA)进行路径规划时易陷入局部最优且寻优效果较差的问题，提出一种改进的SFLA。改进算法在原算法的更新策略中引入欧氏距离和种群最优蛙，并提出一种带可调控制参数的产生新个体的方法代替原本的随机更新操作。把路径规划问题转换为最小化问题，基于环境中目标和障碍物的位置定义青蛙的适应度，机器人依次到达每次迭代中最好蛙的位置，从而实现最优路径规划。移动机器人仿真实验中，与其他算法相比，改进后的算法成功次数由82提高到98，规划时间由9.7s减少到5.3s。实验结果表明，改进算法具有较强的安全性和寻优性能。

Path planning for mobile robots based on improved shuffled frog leaping algorithm

To solve the problems that the Shuffled Frog Leaping Algorithm (SFLA) path planning is easy to fall into local optima and optimization effect is poor, an improved SFLA was proposed. Euclidean distance and the best frog of the population were added into the original algorithms update strategy, and a method to generate a new individual with a adjustable control parameter instead of the original random update operation was proposed. This paper firstly transformed the problem of robot path planning into a minimization one, and then defined the fitness of a frog based on the position of target and obstacles in the environment. The robot selected and reached the position of the globally best frog in each iteration successively to realize the optimal path planning. In the mobile robots simulation experiment, compared with other algorithms, the successes number of the improved algorithm was increased from 82 to 98, and the path planning time was decreased from 9.7s to 5.3s. The experimental results show that the improved algorithm has stronger security and better optimization performance.