Yang Jinghao, Shao Yuanyuan, Fu Bin, Kou Lei
Metropolitan College, Boston University, Boston, MA 02215, USA.
Taizhou Institute, Zhejiang University, Taizhou 318000, China.
Biomimetics (Basel). 2025 Sep 3;10(9):589. doi: 10.3390/biomimetics10090589.
To address the shortcomings of the RIME algorithm's weak global exploration ability, insufficient information exchange among populations, and limited population diversity, this work proposes a distributed data-guided RIME algorithm called DRIME. First, this paper proposes a data-distribution-driven guided learning strategy that enhances information exchange among populations and dynamically guides populations to exploit or explore. Then, a soft-rime search phase based on weighted averaging is proposed, which balances the development and exploration of RIME by alternating with the original strategy. Finally, a candidate pool is utilized to replace the optimal reference point of the hard-rime puncture mechanism to enrich the diversity of the population and reduce the risk of falling into local optima. To evaluate the performance of the DRIME algorithm, parameter sensitivity analysis, policy effectiveness analysis, and two comparative analyses are performed on the CEC-2017 test set and the CEC-2022 test set. The parameter sensitivity analysis identifies the optimal parameter settings for the DRIME algorithm. The strategy effectiveness analysis confirms the effectiveness of the improved strategies. In comparison with ACGRIME, TERIME, IRIME, DNMRIME, GLSRIME, and HERIME on the CEC-2017 test set, the DRIME algorithm achieves Friedman rankings of 1.517, 1.069, 1.138, and 1.069 in different dimensions. In comparison with EOSMA, GLS-MPA, ISGTOA, EMTLBO, LSHADE-SPACMA, and APSM-jSO on the CEC-2022 test set, the DRIME algorithm achieves Friedman rankings of 2.167 and 1.917 in 10 and 30 dimensions, respectively. In addition, the DRIME algorithm achieved an average ranking of 1.23 in engineering constraint optimization problems, far surpassing other comparison algorithms. In conclusion, the numerical optimization experiments successfully illustrate that the DRIME algorithm has excellent search capability and can provide satisfactory solutions to a wide range of optimization problems.
为了弥补RIME算法全局探索能力弱、种群间信息交换不足以及种群多样性有限的缺点，本文提出了一种分布式数据引导的RIME算法，称为DRIME。首先，本文提出了一种数据分布驱动的引导学习策略，该策略增强了种群间的信息交换，并动态引导种群进行利用或探索。然后，提出了一种基于加权平均的软rime搜索阶段，通过与原始策略交替来平衡RIME的开发和探索。最后，利用候选池代替硬rime穿刺机制的最优参考点，以丰富种群的多样性并降低陷入局部最优的风险。为了评估DRIME算法的性能，在CEC - 2017测试集和CEC - 2022测试集上进行了参数敏感性分析、策略有效性分析和两项对比分析。参数敏感性分析确定了DRIME算法的最优参数设置。策略有效性分析证实了改进策略的有效性。在CEC - 2017测试集上与ACGRIME、TERIME、IRIME、DNMRIME、GLSRIME和HERIME相比，DRIME算法在不同维度上的Friedman排名分别为1.517、1.069、1.138和1.06。在CEC - 2022测试集上与EOSMA、GLS - MPA、ISGTOA、EMTLBO、LSHADE - SPACMA和APSM - jSO相比，DRIME算法在10维和第30维度上的Friedman排名分别为2.167和1.917。此外，DRIME算法在工程约束优化问题中获得了1.23的平均排名，远远超过其他对比算法。总之，数值优化实验成功表明DRIME算法具有出色的搜索能力，能够为广泛的优化问题提供令人满意的解决方案。
