沈阳大学学报（自然科学版）2024，Vol.36Issue(2)：141-152,12.
基于多策略麻雀搜索算法的机器人路径规划
Robot Path Planning Based on Multi-Strategy Sparrow Search Algorithm
摘要
Abstract
The basic sparrow search algorithm(SSA)has been improved through various strategies to solve the global optimization accuracy and speed problems caused by the loss of population diversity in the later stage of sparrow search algorithm.First,the population was initialized by the improved infinite folding iterative mappin(ICMIC),and the adaptive segmented step factor was introduced into the position update formula of the sparrow detector,which made the fixed proportion coefficient of the observer of the sparrow search algorithm changed dynamically with the number of iterations.Second,the observer's position was combined with the new formula and sine cosine algorithm(SCA),and the previous observer's step size was interfered.And finally,the convergence and accuracy of the improved sparrow search algorithm(ISSA),sparrow search algorithm(SSA),whale algorithm(WOA),grey wolf algorithm(GWO),improved grey wolf algorithm(CGWO),sine cosine algorithm(SCA)and particle swarm optimization algorithm(PSO)were compared on the benchmark function,and applied to path planning.Experiments showed that the improved sparrow search algorithm had good optimization performance.关键词
麻雀搜索算法/无限折叠迭代混沌映射/自适应惯性权重/正余弦算法/路径规划Key words
sparrow search algorithm/infinite folding iterative chaotic map/adaptive inertia weight/sine cosine algorithm/path planning分类
信息技术与安全科学引用本文复制引用
杨红,杨超..基于多策略麻雀搜索算法的机器人路径规划[J].沈阳大学学报（自然科学版）,2024,36(2):141-152,12.基金项目
辽宁省"兴辽英才计划"(XLYC1807138) （XLYC1807138）
沈阳市"中青年科技创新人才"支持计划(RC200530). （RC200530）
