基于最短距离适应度的随机路径优化问题研究

Research on stochasticpath optimization based on shortest distance fitness

本文针对已有的以曼哈顿距离作为适应度函数而得出的非最佳路径,通过采用以起点到终点间的直线距离作为适应度函数的遗传算法优化,将其优化为效率更高、更为优秀的路径。经实验验证,证实了此方法在普通二维地图路径优化问题中的优越性。

Aiming at the existing non-optimal path obtained by taking the Manhattan distance as the fitness function,the genetic algorithm is optimized by using the straight line distance between the starting point and the end point as the fitness function.it is optimized into a more efficient and better path,and the superiority of this method in the ordinary two-dimensional map path optimization problem is proved by experiments.