Abstract:

To address the issues such as slow convergence， insufficient convergence accuracy， and the inability to jump out of the local optimum in Harris Hawks Optimization （HHO） algorithm， an improved algorithm based on Refracted Opposition-Based Learning （ROBL） and adaptive strategy was proposed. By introducing a refracted opposition-based learning strategy， opposition solutions were generated to broaden the search scope， thereby enhancing both the convergence speed and the global exploration capability of the algorithm. Concurrently， adaptive inertia weights and nonlinear energy decay factors were employed to adjust the exploration and exploitation capabilities of the algorithm dynamically. Besides， an improved adaptive t distribution variation was incorporated to mutate the optimal positions， thereby enhancing the algorithm's capacity to jump out of the local optimum. While preserving the population diversity， the improved algorithm accelerated convergence， enhanced global search capability and convergence accuracy. In comparative experiments conducted on 12 benchmark functions， it is validated that compared to swarm intelligence algorithms， the proposed HHO algorithm has the highest convergence accuracy on all the functions. Furthermore， in the benchmark test function experiments， the effectiveness of single improvement strategies was verified， as well as the superiority of employing combinations of multiple strategies over using single strategies alone.

Key words: Harris Hawk Optimization (HHO) algorithm, Refracted Opposition-Based Learning （ROBL）, adaptive strategy, non-linear energy decay strategy, benchmark test function

摘要：

为解决哈里斯鹰优化（HHO）算法的收敛速度较慢、收敛精度不够高和无法跳出局部最优等问题，提出一种基于折射反向学习（ROBL）和自适应策略的改进算法。通过引入ROBL策略，在搜索过程中生成反向解来扩大搜索范围，以提高算法的收敛速度和全局搜索能力。同时，采用自适应惯性权重和非线性能量递减因子动态地调整算法的探索和开发能力。另外，引入改进的自适应t分布变异对最优位置进行变异，以增强算法跳出局部最优解的能力。改进算法在维持种群多样性的同时，提升了收敛速度、全局搜索能力和收敛精度。在12个基准测试函数上的对比实验中，与群体智能算法相比，所提算法均获得了最高的收敛精度；而且，在基准测试函数实验中，验证了单个改进策略的有效性以及多个策略组合使用相较于单策略使用的优越性。

关键词: 哈里斯鹰优化算法, 折射反向学习, 自适应策略, 非线性能量递减策略, 基准测试函数