摘要
针对原始河马优化算法(hippopotamus optimization algorithm,HoA)存在的收敛速度较慢、易陷入局部最优和搜索精度不足等问题,提出一种融合多策略改进的河马优化算法(improved hippopotamus optimizational gorithm,IHOA).首先,引入分层随机初始化策略,以增强种群的初始多样性,扩大解空间的探索范围;其次,设计动态协同搜索机制,通过自适应调整种群搜索方向,提高算法的全局搜索能力和局部开发效率;最后,采用自适应逃逸策略,通过动态跳出局部最优,提升算法的全局寻优能力.为验证IHOA的性能,将IHOA与另外5种算法在6种基准测试函数上进行仿真对比,并通过数值分析和Wilcoxon秩和检验对结果进行严格评估.实验结果表明:IHOA在收敛速度、解的精度和全局搜索能力等方面均有很好表现.随后,将IHOA应用于城市物流无人机三维路径规划问题,实验结果表明,其规划出的路径在长度、转弯角度及飞行安全性上均优于其他算法,验证了IHOA的高效性与实用性.
Aiming at the problems of the original hippopotamus optimization algorithm(HOA),such as slow convergence speed,easy to fall into local optimums,and insufficient search accuracy,this paper proposes an improved hippopotamus optimization algorithm(IHOA)that integrates multiple strategies.Firstly,the Layered Random Initialization(LRI)strategy is introduced to enhance the initial diversity of the population and expand the exploration range of the solution space;secondly,the Dynamic Cooperative Exploration Mechanism(DCEM)is designed,which improves the algorithm's global search capability and local exploitation efficiency by adaptively adjusting the population search direction;and finally,Adaptive Escaping Strategy(AES),which enhances the algorithm's global optimality search capability by dynamically jumping out of the local optimal position,is proposed to be the most effective optimization algorithm.To verify the performance of IHOA,IHOA is simulated and compared with other five algorithms on six benchmark test functions,and the results are critically evaluated by numerical analysis and Wilcoxon rank sum test.Experimental results demonstrate that IHOA exhibits excellent performance in convergence speed,solution accuracy,and global search capability.Subsequently,IHOA is applied to the three-dimensional path planning problem of urban logistics UAVs,and the experimental results show that its planned paths are superior to other algorithms in terms of length,turning angle and flight safety,which verifies the high efficiency and practicality of IHOA.
作者
茹兴旺
RU Xingwang(School of Information Engineering,Anhui Business and Technology College,Hefei Anhui 231131,China)
出处
《太原师范学院学报(自然科学版)》
2025年第4期25-35,共11页
Journal of Taiyuan Normal University(Natural Science Edition)
基金
安徽省质量工程项目(2023xqsj006,2024jyxm0964)
安徽省高校自然科学研究重点项目(2024AH050135,2024AH050140)
安徽工商职业学院校级科学研究重点项目(ZK2024A003,SK2024A019)
2024年安徽省高校理工科教师赴企业挂职实践计划项目(2024jsqygz230)
2024年青年骨干教师境内访学研修资助项目(JNFX2024155)。
关键词
河马优化算法
协同搜索
自适应逃逸策略
路径规划
全局优化
元启发式算法
hippopotamus optimization algorithms
collaborative search
adaptive escape strategies
path planning
global optimization search
metaheuristic algorithm
