Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

中文翻译：

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动态多群差分学习量子鸟群算法及其在随机森林分类模型中的应用。

鸟群算法是最近提出的群体智能算法之一。然而，原有的鸟群算法存在容易陷入局部最优、收敛速度慢等缺点。为了克服这些缺点，建立了一种结合三种混合策略的动态多群差分学习量子鸟群算法。首先，建立动态多群鸟群算法，采用差分进化策略增强觅食行为运动的随机性，使鸟群算法具有更强的全局探索能力。接下来，量子行为被引入到鸟群算法中，以获得更有效的搜索解决方案空间。然后，改进的鸟群算法用于在随机森林分类模型上优化决策树的数量和预测变量的数量。实验中对18个benchmark函数、30个CEC2014函数和8个UCI数据集进行了测试，结果表明改进后的算法和模型具有很强的竞争力，优于其他算法和模型。最后，将有效的随机森林分类模型应用于实际测油预测。实验结果表明，三种策略都可以显着提升鸟群算法的性能，并且所提出的学习方案可以保证随机森林分类模型的稳定性和效率比其他算法更高。对 18 个基准函数、30 个 CEC2014 函数和 8 个 UCI 数据集进行了测试，表明改进的算法和模型非常具有竞争力，并且优于其他算法和模型。最后，将有效的随机森林分类模型应用于实际测油预测。实验结果表明，三种策略都可以显着提升鸟群算法的性能，并且所提出的学习方案可以保证随机森林分类模型的稳定性和效率比其他算法更高。对 18 个基准函数、30 个 CEC2014 函数和 8 个 UCI 数据集进行了测试，表明改进的算法和模型非常具有竞争力，并且优于其他算法和模型。最后，将有效的随机森林分类模型应用于实际测油预测。实验结果表明，三种策略都可以显着提升鸟群算法的性能，并且所提出的学习方案可以保证随机森林分类模型的稳定性和效率比其他算法更高。将有效的随机森林分类模型应用于实际油井预测。实验结果表明，三种策略都可以显着提升鸟群算法的性能，并且所提出的学习方案可以保证随机森林分类模型的稳定性和效率比其他算法更高。将有效的随机森林分类模型应用于实际油井预测。实验结果表明，三种策略都可以显着提升鸟群算法的性能，并且所提出的学习方案可以保证随机森林分类模型的稳定性和效率比其他算法更高。