基于多种群与协同量子化的哈里斯鹰优化算法
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1. 昆明理工大学 信息工程与自动化学院,昆明 650500;\hspace{3pt};2. 昆明理工大学 云南省计算机技术应用重点实验室,昆明 650500

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E-mail: qianqian_yn@126.com.

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TP301.6

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云南省基础研究计划面上项目(202101AT070082).


Harris Hawks optimization algorithm based on multigroup and collaborative quantization
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1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;2. Yunnan Key Laboratory of Computer Technology Applications,Kunming University of Science and Technology,Kunming 650500,China

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    摘要:

    哈里斯鹰优化算法(Harris Hawks optimization,HHO)存在探索能力和开发能力平衡性不足等问题,导致算法收敛速度较慢,寻优精度较低,容易陷入局部最优.针对这些问题,引入多种群策略解决初始化种群单一的问题,提出基于多种群的多能量策略模拟两只体能不同的猎物的逃跑过程,使两个种群向不同的方向进化,以提高探索阶段与开发阶段的搜索能力.此外,协同量子化策略的加入在迭代前期可避免算法陷入局部极值,在迭代后期可提高算法的寻优精度.最后,通过对测试函数的优化结果进行分析可以得出,与其他一些经典或最新的算法相比,改进后的算法可大大提高最优解的收敛速度和寻优精度,同时具有更强的跳出局部最优的能力.

    Abstract:

    Harris Hawks optimization (HHO) algorithm has some advantages, but it still has some problems, such as insufficient balance between exploration and development abilities, which leads to slow convergence speed, low optimization accuracy and easy to fall into local optimization. Therefore, multi population is introduced and a multi energy strategy is proposed to simulate the escape process of two prey with different physical abilities, so that the two populations evolve in different directions to improve the searching ability of the algorithm. In addition, a cooperative quantization strategy is also proposed to avoid the algorithm from falling into local extremum in the early stage, and to improve optimization accuracy at the later stage of iteration. Finally, based on the optimization results, compared with some other classical or latest algorithms, the improved algorithm greatly improves the convergence speed and optimization accuracy, and has stronger ability to jump out of the local extremum.

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引用本文

李岩,钱谦.基于多种群与协同量子化的哈里斯鹰优化算法[J].控制与决策,2024,39(7):2169-2176

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  • 在线发布日期: 2024-06-06
  • 出版日期: 2024-07-20
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