计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240400145-8.doi: 10.11896/jsjkx.240400145
任庆欣, 冯锋
REN Qingxin, FENG Feng
摘要： 针对河马优化算法(Hippopotamus Optimization Algorithm,HO)收敛速度慢、易陷入局部寻优以及对算法参数有依赖性等问题,文中提出一种多策略多维度融合改进的河马优化算法(Improved Hippo Optimization Algorithm Based on Multi-strategy and Multi-dimension Fusion,MSMDHO)。首先,利用准反向学习的映射方式生成或者扰动初始化种群,提高种群的空间分布质量。其次,引入了正余弦优化策略,将其应用在HO算法第一阶段中的描述雌性或未成熟河马种群位置更新公式中,利用其震荡性不断检测和扰动,从而达到更好的优化效果。最后,在HO的抵御捕食者阶段和逃离捕食者阶段分别使用切线飞行策略和PID搜索因子,避免种群陷入局部寻优,提高全体收敛速度。利用MSMDHO算法、HO算法、多元宇宙算法(Multi-verse Optimization,MVO)、鹈鹕优化算法(Pelican Optimization Algorithm,POA)、鼠群优化器(Rat Swarm Optimizer,RSO)、旗鱼优化算法(Sailfish Optimizer,SFO)以及粒子群算法(Particle Swarm Optimization,PSO)对8个测试函数分别进行测试,结果表明,MSMDHO算法在全局搜索能力和收敛速度的稳定性和先进性都领先其他算法。
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