机器人路径规划的新型头脑风暴优化算法

针对头脑风暴优化算法在求解机器人路径规划问题时存在初始解成功率低、运算代价大且路径不平滑等问题进行了研究，从心理学角度出发，提出了一种新型头脑风暴优化算法及其离散化方案。引入羊群效应下的教与学思想增强个体学习的方向性，并通过基于自我选择效应的步长调节机制扩大后期局部搜索比例，提升算法效率；离散处理阶段采用贪婪移动搜索法取得较优初始解，重新定义运算过程以双向平滑路径。仿真结果表明，新型头脑风暴优化算法在离散化前后均有较优的表现，在不同障碍物环境中均能规划出较优的路径。数值实验验证了所提算法的有效性，该算法在路径规划领域的应用值得进一步探索。

Robot path planning with a novel brain storm optimization algorithm

The brain storm optimization algorithm has the problems of low initial solution success rate, high computational cost and unsmooth path in solving robot path planning problem. Therefore, this paper proposed a novel brain storm optimization algorithm and its discretization scheme from the perspective of psychology. The teaching-learning idea under the herd effect enhanced the directionality of individual learning. The improved algorithm expanded the local search ratio in the later stage and improved efficiency through the step adjustment mechanism based on self-selection effect. In the discrete processing stage, it used a greedy moving search method to obtain better initial solutions. At the same time, the redefined calculation process smoothed the path in two directions. The simulation results show that the novel brain storm optimization algorithm has better performance before and after discretization, and can plan better paths in different environments. Numerical experiments verify the effectiveness of the proposed algorithm, and the application of the algorithm in the field of path planning deserves further exploration.