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基于改进狼群算法优化LSTM网络的舆情演化预测

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为提高预测舆情演化趋势的能力,提出了一种基于改进狼群算法(IWPA)优化长短期记忆(LSTM)神经网络的舆情演化预测模型.采用Halton Sequence进行初始化,提高种群多样性;设计步长因子进行高斯-正弦扰动变换,提高狼群探索开发能力;结合鲸鱼优化算法中的螺旋改进围攻机制,增强狼群的局部搜索能力;引入记忆力机制,使用双向记忆种群增加狼群协同合作能力,将改进后的狼群算法应用到LSTM神经网络的超参数预测.采用"新冠疫情"和"食品安全"等关键词作为实例,证明了 IWPA-LSTM神经网络舆情演化预测模型具有良好的准确性和普适性,适用于多种舆情演化的预测.
Public Opinion Evolution Prediction Based on LSTM Network Optimized by an Improved Wolf Pack Algorithm
To improve the ability to predict the evolution trend of public opinion,a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed.Use Halton Sequence to initialization to improve population diversity.Design step factor to perform Gauss-Sine perturbation transformation to im-prove wolf group exploration and development capabilities.Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves.The bidirectional memory population is used to increase the cooperative ability of the wolf pack.The improved wolf pack algorithm(1WPA)is applied to the hyperparameter predic-tion of the LSTM neural network.Using keywords such as"COVID-19"and"Food Safety",the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality.The model is suitable for the prediction of various public opinion evolution trends.

public opinion evolution predictionwolf pack algorithmlong short-term memoryHal-ton sequencesine perturbationwhale spiral siege mechanismmemory mechanism

李若晨、肖人彬

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华中科技大学人工智能与自动化学院,武汉 430074

舆情演化预测 狼群算法 LSTM神经网络 Halton Sequence 正弦扰动 鲸鱼螺旋围攻机制 记忆力机制

科技创新2030—"新一代人工智能"重大项目

2018AAA0101200

2024

10.13306/j.1672-3813.2024.01.001

复杂系统与复杂性科学
青岛大学

复杂系统与复杂性科学

CSTPCD北大核心
影响因子：0.798
ISSN：1672-3813
年,卷(期)：2024.21(1)
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