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Research and application of machine learning method based on swarm intelligence optimization

机译:基于群智能优化的机器学习方法的研究与应用

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摘要

In order to make the swarm intelligence optimization learning method better serve the present agriculture, aiming at the complexity of agricultural production problems, a three-dimensional chaotic Drosophila Optimization Stochastic Forest prediction model is proposed. Firstly, the original Drosophila optimization algorithm is extended from two-dimensional search space to three-dimensional space, and chaos theory is introduced to initialize the population to avoid falling into local optimum. An improved three-dimensional chaotic Drosophila optimization algorithm is proposed. The experimental results show that the proposed method not only has better solution quality, but also has faster convergence speed. Then, the algorithm is introduced into the Stochastic Forest model, and the three-dimensional chaotic Drosophila optimization algorithm is used to train the stochastic forest to establish the optimal calculation model. Finally, the method is tested on rice pest data set. The experimental results show that the model has better prediction accuracy and can more effectively realize the prediction of rice pests.
机译:为了使群体智能优化学习方法更好地服务于本农业,旨在瞄准农业生产问题的复杂性,提出了一种三维混沌果蝇优化随机森林预测模型。首先,原始的果蝇优化算法从二维搜索空间扩展到三维空间,并引入了混沌理论以初始化人口以避免落入局部最佳。提出了一种改进的三维混沌果蝇优化算法。实验结果表明,该方法不仅具有更好的解决方案质量,还具有更快的收敛速度。然后,将该算法引入随机森林模型,三维混沌果蝇优化算法用于训练随机林建立最佳计算模型。最后,该方法在稻瘟病数据集上进行了测试。实验结果表明,该模型具有更好的预测精度,可以更有效地实现水稻害虫的预测。

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