基于优化麻雀搜索算法的水下机器人路径规划研究  

Research on Path Planning of Underwater Robot Based on Intelligent Sparrow Search Algorithm

作　　者：周凯莉 刘从军[1,2] ZHOU Kaili;LIU Congjun(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003;Jiangsu KeDa Huifeng Technology Co.,Ltd.,Zhenjiang 212003)

机构地区：[1]江苏科技大学计算机学院,镇江212003 [2]江苏科大汇峰科技有限公司,镇江212003

出　　处：《计算机与数字工程》2023年第9期2048-2054,共7页Computer & Digital Engineering

摘　　要：采用新颖的群智能仿生算法的优化麻雀搜索算法(ISSA)对水下机器人进行路径规划研究,并将麻雀算法适当优化:如选择Tent映射初始化麻雀种群提高发现者种群的质量,引入自适应惯性权重策略,可以根据需要动态调整惯性权重值的大小。同时考虑到算法后期的停滞问题,添加柯西变异来对适应度较好的个体进行突变,增加种群的多样性避免陷入局部最优。经过同等参数下的对比实验,论文所提出的改进麻雀搜索算法具有更高的搜索效率和更快的收敛速度,为路径规划问题带来新的思路。The optimization of the sparrow search algorithm(ISSA)using a novel swarm intelligence biomimetic algorithm is applied to underwater robot path planning research.The sparrow algorithm is appropriately enhanced by employing the Tent mapping to initialize the sparrow population,thus improving the quality of the explorer population.Additionally,an adaptive inertia weight strategy is introduced,allowing for dynamic adjustments of the inertia weight value as needed.To address stagnation issues in the later stages of the algorithm,Cauchy mutation is incorporated to mutate individuals with good fitness,increasing population diversity and avoiding local optima.Through comparative experiments under equivalent parameters,the proposed improved sparrow search algorithm demonstrates higher search efficiency and faster convergence speed,providing new insights for path planning problems.

关 键 词：麻雀搜索算法 水下机器人 自适应调整惯性权重 路径规划 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]