论文部分内容阅读
提出了一种k-TSPN算法,把k-TSPN问题分成k-TSP和TSPN两个子问题来处理.首先由随机递归算法生成k个机器人路径,得到了每条路径的传感器访问顺序;然后用遗传算法在每个传感器的通信范围内寻找路径交点对路径进行优化,缩短了路径.交点位置采用角度表示,使优化的变量减小了一半;压缩了交点角度的取值范围,使搜索空间和极值点大大减少,引进小生境技术以及增加杂交个体之间的海明距离对自适应遗传算法进行了改进;提高了全局搜索的速度和搜索全局最优解的概率.仿真得到了较好的结果.
A k-TSPN algorithm is proposed, which divides the k-TSPN problem into two sub-problems, k-TSP and TSPN. Firstly, k robot paths are generated by stochastic recursive algorithm, and the sensor access order of each path is obtained. The algorithm searches for the intersection of paths within the communication range of each sensor to optimize the path and shorten the path.The position of intersection point is expressed in terms of angles and the optimized variable is reduced by half.The compression angle of the intersection point is narrowed so that the search space and the pole The value of points is greatly reduced, the niche technology is introduced, and the Hamming distance between hybrid individuals is increased to improve the adaptive genetic algorithm; the speed of global search and the probability of searching the global optimal solution are improved. The simulation results show good results .