Li Wuke, Yang Xiong, Yin Yuchen, Wang Qian
School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China.
Zhicheng College, Fuzhou University, Fuzhou 350002, China.
Biomimetics (Basel). 2024 Dec 31;10(1):14. doi: 10.3390/biomimetics10010014.
The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems.
RIME算法是一种新颖的基于物理的元启发式算法，具有很强的解决全局优化问题的能力，能够应对工程应用中的挑战。它通过构建霜冰生长过程来实现探索和利用行为。然而，RIME存在一些缺点：探索能力有限、收敛速度慢以及探索和利用之间存在固有的不对称性。现在，一种更高效、更具适应性的改进版本——混合估计霜冰优化算法（简称为HERIME）出现了，它用于解决这些问题。利用估计分布算法的基于概率模型的采样方法来提高RIME种群的质量并增强其全局探索能力。采用基于轮盘赌的适应度距离平衡选择策略来强化RIME的硬霜阶段，以有效增强优化过程中利用和探索阶段之间的平衡。我们使用IEEE CEC2017和IEEE CEC2022测试套件中的41个函数对HERIME进行验证，并将其优化精度、收敛性和稳定性与四种经典和近期的元启发式算法以及五种先进算法进行比较，结果表明所提出的算法优于所有这些算法。使用Friedman检验和Wilcoxon秩和检验的统计研究也证实了其优异的性能。此外，消融实验分别验证了每种策略的有效性。因此，实验结果表明HERIME具有更好的搜索效率和优化精度，并且在处理全局优化问题方面是有效的。
