Ma Jingjing, Zhao Zhifang, Zhang Lin
Department of Medicine, Division of Gastroenterology, Third Ward, The First Medical Centre of Chinese PLA General Hospital, Haidian District, Beijing, China.
Department of Respiratory and Critical Care Medicine, The First Medical Centre of Chinese PLA General Hospital, Haidian District, Beijing, China.
PLoS One. 2025 Jan 16;20(1):e0317224. doi: 10.1371/journal.pone.0317224. eCollection 2025.
Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. This improvement is achieved by incorporating a Gaussian barebone mechanism, a wormhole strategy, and an elimination strategy into the MFO. To assess the effectiveness of BWEMFO, a series of comparison experiments is conducted, comparing it against conventional metaheuristic algorithms, advanced metaheuristic algorithms, and various MFO variants. The experimental results reveal a significant enhancement in both the convergence speed and the capability to escape local optima with the implementation of BWEMFO. The scalability of the algorithm is confirmed through benchmark functions. Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. Subsequently, BWEMFO-KELM undergoes diagnostic and predictive experimentation on three distinct medical datasets: the breast cancer dataset, colorectal cancer datasets, and mammographic dataset. Through comparative analysis against five alternative machine learning methodologies across four evaluation metrics, our experimental findings evince the superior diagnostic accuracy and reliability of the proposed BWEMFO-KELM model.
蛾火优化算法（MFO）是一种受蛾类夜间飞行模式启发的群体智能算法，因其结构简单、优化效率高而被广泛应用于各个领域。尽管如此，一个显著的局限性是，由于缺乏平衡的开发和探索阶段，它容易陷入局部最优。因此，本文介绍了一种新颖的增强型MFO算法（BWEMFO），旨在提高算法性能。这种改进是通过将高斯裸骨机制、虫洞策略和消除策略融入MFO来实现的。为了评估BWEMFO的有效性，进行了一系列比较实验，将其与传统元启发式算法、先进元启发式算法以及各种MFO变体进行比较。实验结果表明，实施BWEMFO后，收敛速度和逃离局部最优的能力都有显著提高。通过基准函数验证了该算法的可扩展性。我们使用BWEMFO优化核极限学习机的核参数，从而构建用于医学诊断和预测的BWEMFO-KELM方法。随后，BWEMFO-KELM在三个不同的医学数据集上进行诊断和预测实验：乳腺癌数据集、结直肠癌数据集和乳腺X线摄影数据集。通过针对四种评估指标与五种替代机器学习方法进行对比分析，我们的实验结果表明所提出的BWEMFO-KELM模型具有更高的诊断准确性和可靠性。
