Abstract: In order to avoid the flower pollination algorithm falling into local optimum in the identification process of the extreme learning machine, an extreme learning machine algorithm based on cloud quantum flower pollination was proposed. Firstly, cloud model and quantum system were introduced into the flower pollination algorithm to enhance the global search ability of the flower pollination algorithm, so that the particles were able to perform optimization in different states. Then, the cloud quantum flower pollination algorithm was used to optimize the parameters of the extreme learning machine in order to improve the identification accuracy and efficiency of the extreme learning machine. In the experiments, six benchmark functions were used to simulate and compare several algorithms. It is verified by the comparison results that the performance of proposed cloud quantum flower pollination algorithm is superior to those of other three swarm intelligence optimization algorithms. Finally, the improved extreme learning machine algorithm was applied to the identification of oil and gas layers. The experimental results show that, the identification accuracy of the proposed algorithm reaches 98.62%, and compared with the classic extreme learning machine, its training time is shortened by 1.680 2 s. The proposed algorithm has high identification accuracy and efficiency, and can be widely applied to the actual classification field.

Key words: extreme learning machine, cloud model, flower pollination algorithm, identification of oil and gas layers, quantum system

摘要： 为了避免花朵授粉算法在极限学习机识别过程中易陷入局部最优，提出了一种基于云量子花朵授粉的极限学习机算法。首先,将云模型和量子系统引入到花朵授粉算法中，增强花朵授粉算法的全局搜索能力，使粒子能在不同状态下进行寻优。然后,采用云量子花朵授粉算法优化极限学习机的参数，提高极限学习机的识别精度和效率。实验中采用6个标准测试函数对多个算法进行仿真对比，对比结果验证了所提云量子花朵授粉算法的性能优于另外3种群智能优化算法。最后，将改进的极限学习机算法应用到油气层识别中，结果表明其识别精度达到98.62%，相较于经典极限学习机，其训练时间缩短了1.680 2 s，该算法具有较高的识别精度和效率，可以广泛应用到实际分类领域中。

关键词: 极限学习机, 云模型, 花朵授粉算法, 油气层识别, 量子系统