In the original Moth-Flame Optimization (MFO), the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame, so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems. Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. In the proposed GCMFO, GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate; crisscross search (CC) is adopted to promote the exploitation and/or exploration ability of MFO. The proposed algorithm’s performance is estimated by organizing a series of experiments; firstly, the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems. Secondly, GCMFO is applied to handle multilevel thresholding image segmentation problems. At last, GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases, including the appendicitis diagnosis, overweight statuses diagnosis, and thyroid cancer diagnosis. Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy. It also indicates that the presented GCMFO has a promising potential for application.

在原始的蛾-火焰优化（MFO）中，蛾的搜索行为依赖于对应的火焰以及蛾与其对应的火焰之间的相互作用，因此在面对多维和高维优化问题。因此，在这项工作中，提出了一种具有交叉策略的广义对立 MFO，名为 GCMFO，以克服上述缺陷。在提出的GCMFO中，基于跳跃率，在初始化和迭代跳跃阶段采用GOBL增加种群多样性并扩大搜索范围；采用交叉搜索（CC）来提升 MFO 的开发和/或探索能力。通过组织一系列实验来估计所提出算法的性能；首先，采用 CEC2017 基准集来评估 GCMFO 在处理高维和多模态问题方面的性能。其次，GCMFO 用于处理多级阈值图像分割问题。最后将GCMFO集成到内核极限学习机分类器中，处理阑尾炎诊断、超重状态诊断、甲状腺癌诊断三个医学诊断案例。实验结果和讨论表明，所提出的方法在收敛速度和准确性方面都优于原始 MFO 和其他最先进的算法。这也表明所提出的 GCMFO 具有广阔的应用潜力。GCMFO 用于处理多级阈值图像分割问题。最后将GCMFO集成到内核极限学习机分类器中，处理阑尾炎诊断、超重状态诊断、甲状腺癌诊断三个医学诊断案例。实验结果和讨论表明，所提出的方法在收敛速度和准确性方面都优于原始 MFO 和其他最先进的算法。这也表明所提出的 GCMFO 具有广阔的应用潜力。GCMFO 用于处理多级阈值图像分割问题。最后将GCMFO集成到内核极限学习机分类器中，处理阑尾炎诊断、超重状态诊断、甲状腺癌诊断三个医学诊断案例。实验结果和讨论表明，所提出的方法在收敛速度和准确性方面都优于原始 MFO 和其他最先进的算法。这也表明所提出的 GCMFO 具有广阔的应用潜力。实验结果和讨论表明，所提出的方法在收敛速度和准确性方面都优于原始 MFO 和其他最先进的算法。这也表明所提出的 GCMFO 具有广阔的应用潜力。实验结果和讨论表明，所提出的方法在收敛速度和准确性方面都优于原始 MFO 和其他最先进的算法。这也表明所提出的 GCMFO 具有广阔的应用潜力。