计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (7): 168-177.doi: 10.11896/jsjkx.250500040

• 人工智能 • 上一篇    下一篇

多策略改进的河马优化算法在路径规划中的应用

张嘉伟1, 马占有1,2, 丁贝贝1   

  1. 1 北方民族大学计算机科学与工程学院 银川 750021
    2北方民族大学国家民委图像图形智能处理实验室 银川 750021
  • 收稿日期:2025-05-13 修回日期:2025-09-04 出版日期:2026-07-15 发布日期:2026-07-10
  • 通讯作者: 马占有(mazhany@nmu.edu.cn)
  • 作者简介:(zhangjw1104@126.com)
  • 基金资助:
    国家自然科学基金(61962001);宁夏自然科学基金(AAC020054)

Application of Multi-strategy Improved Hippopotamus Optimization Algorithm in Path Planning

ZHANG Jiawei1, MA Zhanyou1,2, DING Beibei1   

  1. 1 School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China
    2 National Ethnic Affairs Commission Laboratory of Intelligent Image and Graphics Processing,North Minzu University,Yinchuan 750021,China
  • Received:2025-05-13 Revised:2025-09-04 Published:2026-07-15 Online:2026-07-10
  • About author:ZHANG Jiawei,born in 2001,postgra-duate,is a member of CCF(No.Z5231G).His main research interest is intelligent optimization algorithms.
    MA Zhanyou,born in 1979,Ph.D,professor,is a member of CCF(No.C7422M).His main research interests include formal method and intelligent optimization algorithms.
  • Supported by:
    National Natural Science Foundation of China(61962001) and Ningxia Natural Science Foundation(AAC020054).

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摘要： 针对河马优化算法求解路径规划问题时存在易陷入局部最优、搜索精度低等缺陷,提出一种多策略改进的河马优化(Improved Hippopotamus Optimization,IHO)算法。首先,引入拉丁超立方抽样方法初始化河马种群,扩大初始探索范围,使算法在搜索空间分布更均匀。其次,引入动态自适应收敛因子改进雄性河马的位置更新方式,提高全局搜索能力,降低算法陷入局部最优的概率。然后,在河马防御阶段引入可变螺旋搜索策略,平衡算法的开发和探索能力,提升搜索效率。12个基准测试函数的仿真实验结果表明,相较于河马优化算法、灰狼算法、沙猫群算法、天鹰算法等算法,IHO 算法的寻优能力更好,收敛速度更快。最后,将IHO算法应用于移动机器人路径规划,实验结果表明,在15×15,20×20,30×30的栅格地图中,IHO算法相较于河马优化算法在路径上分别缩短了7.7%,1.8%,4.8%,表现出明显的性能优势。

关键词: 河马优化算法, 拉丁超立方抽样, 动态自适应收敛因子, 可变螺旋搜索策略, 路径规划

Abstract: In view of the defects of the hippopotamus optimization algorithm,such as being prone to falling into local optima and having low search accuracy when solving path-planning problems,an improved hippopotamus optimization(IHO) algorithm with multiple strategies is proposed.Firstly,the Latin hypercube sampling method is introduced to initialize the hippopotamus population,which expands the initial exploration scope and enables the algorithm to be more uniformly distributed in the search space.Secondly,a dynamic adaptive convergence factor is introduced to improve the position update method of male hippopotamuses,enhancing the global search ability and reducing the probability of the algorithm falling into local optima.Then,a variable spiral search strategy is introduced in the hippopotamus defense phase to balance the exploitation and exploration abilities of the algorithm and improve the search efficiency.Simulation results of 12 benchmark test functions show that the IHO algorithm has better optimization ability and faster convergence speed compared with the hippopotamus optimization algorithm,grey wolf algorithm,sand cat swarm algorithm,and genetic algorithm.Finally,the IHO algorithm is applied to the path planning of mobile robots.Experimental results show that in 15×15,20×20,and 30×30 grid maps,the IHO algorithm shortens the path by 7.7%,1.8%,and 4.8% respectively compared with the hippopotamus optimization algorithm.It shows significant performance advantages.

Key words: Hippopotamus optimization algorithm, Latin hypercube sampling, Dynamic adaptive convergence factor, Variable spiral search strategy, Path planning

中图分类号: 

  • TP301
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