基于改进海鸥优化算法的机器人路径规划
Robot Path Planning Based on Improved Seagull Optimization Algorithm
黄训爱 1杨光永 1蔡艳 1徐天奇1
作者信息
- 1. 云南民族大学电气信息工程学院,昆明 650500
- 折叠
摘要
针对海鸥优化算法(SOA)存在收敛精度低以及易陷入局部最优等问题,提出一种多策略融合改进的海鸥优化算法(MFSOA).首先,引入Tent混沌映射初始化种群,增加海鸥种群多样性;基于当前迭代次数t的非线性收敛因子动态调整策略,将海鸥优化算法的线性搜索非线性化,增强算法的寻优速度和寻优精度,避免算法陷入局部最优;在海鸥位置更新时引入Levy飞行策略增强算法的全局搜索能力;最后,通过使用黄金正弦机制引导种群的位置更新,进一步缩小搜索范围,提高算法局部搜索能力.选用6 个基准测试函数对算法性能进行测试,测试结果显示MFSOA收敛速度更快,收敛精度更高;最后将改进算法应用于移动机器人路径规划,仿真结果表明该算法规划的路径长度更短,搜索效率更高.
Abstract
A multi-strategy fusion improved seagull optimization algorithm(MFSOA)is proposed to ad-dress the problems of low convergence accuracy and susceptibility to local optimality of the seagull optimi-zation algorithm(SOA).Tent chaotic mapping is introduced to initialize the population and increase the di-versity of the seagull population;the nonlinear convergence factor based on the current number of iterations t is dynamically adjusted to nonlinearize the linear search of the seagull optimization algorithm,enhance the speed and accuracy of the search,and avoid the algorithm falling into local optimum.The Levy flight strate-gy is introduced to enhance the global search ability of the algorithm during the seagull position update;fi-nally,the position update of the population is guided by using the golden sine mechanism to further narrow the search range and improve the local search ability of the algorithm.Six benchmark test functions are se-lected to test the performance of the algorithm,and the test results show that IMPA converges faster and has higher convergence accuracy;finally,the improved algorithm is applied to mobile robot path planning,and the simulation results show that the algorithm plans a shorter path length and higher search efficiency.
关键词
混沌映射/非线性收敛因子/黄金正弦/Levy飞行/路径规划Key words
chaotic mapping/nonlinear convergence factor/golden sine/Levy flight/path planning引用本文复制引用
基金项目
国家自然科学基金(61761049)
国家自然科学基金(61261022)
云南省教育厅科研项目(2023)(2023Y0502)
云南民族大学硕士研究生科研创新基金(2022)(2022SKY006)
出版年
2024
NETL
NSTL
万方数据