基于改进适应度函数的麻雀搜索算法路径规划研究

Research on path planning of sparrow search algorithm based on improved fitness function

将一种智能优化算法——麻雀搜索算法应用于移动机器人二维路径规划,并对其进行改进以提升寻路效率。基于该算法的实验环境在栅格地图中实现,采用麻雀搜索算法进行路径规划,为了解决该算法在路径规划应用中原本适应度值不佳,种群易受局部最优个体误导的问题,设计并改进了算法的适应度函数,将本代的全局最优种群作为下一代迭代的评价标准,并将适用于路径规划的思想加入到适应度函数中。在模拟场景中进行了寻路仿真实验,实验结果验证了算法改进的合理性和提升程度。

In this paper,an intelligent optimization algorithm,the sparrow search algorithm,is applied to two-dimensional path planning for mobile robots,and is improved to enhance path finding efficiency.The experimental environment based on this algorithm is implemented in a raster map,and the sparrow search algorithm is used for path planning.In order to solve the problem that the original fitness value of this algorithm in path planning applications is poor and the population is easily misled by locally optimal individuals,the fitness function of the algorithm is designed and improved in this paper,and the global optimal population of the current generation is used as the evaluation criterion for the next generation iteration,and the ideas specifically for path planning are added to the the fitness function.The pathfinding simulation experiments are carried out in a simulated scenario,and the experimental results verify the rationality and the degree of improvement of the algorithm improvement.