基于杂交退火灰狼算法的移动机器人路径规划

针对灰狼优化算法在移动机器人路径规划时易陷入局部最优且效率低的问题，提出一种杂交退火灰狼算法。采用可调节的非线性收敛因子进行平衡算法的前期搜索和后期寻优；同时采用自适应遗传杂交策略，对灰狼群体以一定概率两两杂交以产生新个体，从而有效增强灰狼群体的多样性；在迭代的后期用模拟退火操作接受候选狼，避免算法陷入局部最优解。将路径长度和路径平滑度作为适应度评估指标并建立评估函数以评估路径规划效果。最后，路径规划实验结果表明，在3种不同尺寸的地图上，本文改进算法的适应度比灰狼优化算法分别优化了2.10、3.15、3.94,路径规划效果明显优于其他相关算法。

Path planning of mobile robot based on hybrid annealing gray wolf algorithm

Aiming at the problem that the gray wolf optimization algorithm is easy to fall into local optimum and low efficiency in the path planning of mobile robots, a genetic simulated annealing gray wolf optimization algorithm was proposed. An adjustable nonlinear convergence factor is used for the early search and the late search of the balance algorithm. At the same time, the adaptive genetic hybridization strategy was used to hybridize the gray wolf population with a certain probability to produce new individuals, so as to effectively enhance the diversity of the gray wolf population. The candidate wolf is accepted by simulated annealing operation at the later stage of iteration to avoid the algorithm falling into local optimal solution. The path length and path smoothness are taken as the fitness evaluation indexes and the evaluation function is established to evaluate the effect of path planning. Finally, the experimental results of path planning show that the fitness of the improved algorithm in this paper is optimized by 2.10, 3.15 and 3.94 respectively compared with the gray wolf optimization algorithm on three maps of different sizes, and the path planning effect is significantly better than other related algorithms.