Bayesian Network Structure Learning Based on Improved Firefly Algorithm
宋楠 1邸若海 1王鹏 2李晓艳 1贺楚超 1王储1
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作者信息
1. 西安工业大学电子信息工程学院,西安 710021
2. 西安工业大学发展规划处,西安 710021
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摘要
贝叶斯网络是目前不确定知识表达和推理领域最有效的理论模型之一,利用贝叶斯网络进行分析和推理前首先需要通过结构学习和参数学习获取其网络模型,其中结构学习是参数学习的基础.针对现有萤火虫算法不符合生物学规则以及学习贝叶斯网络结构存在效率低、容易陷入局部最优等问题,设计了 一种基于互信息与性别机制的萤火虫算法(firefly algo-rithm based on mutual information and gender mechanism,MGM-FA).首先,通过计算节点互信息得到贝叶斯网络骨架图,基于骨架图驱动MGM-FA算法生成初始种群;其次,引入基于性别机制的个性化贝叶斯网络种群更新策略,以保障贝叶斯网络个体的多样性;最后,引入局部优化器和扰动操作符,增强算法的寻优能力.分别在不同规模的标准网络上进行仿真实验,与现有同类型算法相比,该算法精度和效率均有所提升.
Abstract
Bayesian network is currently one of the most effective theoretical models in the field of uncertain knowledge expression and inference.Before utilizing Bayesian networks for analysis and inference,it is first necessary to obtain their network models through structural and parametric learning,and structure learning is the basis for parameter learning.Aiming at the existing firefly algorithm that does not conform to biological rules as well as learning the Bayesian network structure that has low efficiency and is easy to fall into local optimization,MGM-FA(firefly algorithm based on mutual information and gender mechanism)was designed.Firstly,the Bayesian network skeleton graph was obtained by calculating the mutual information of nodes,and the MGM-FA algorithm was driven to generate the initial population based on the skeleton graph.Secondly,a personalized Bayesian network population updating strategy based on the gender mechanism was introduced to safeguard the diversity of the Bayesian network individuals.Lastly,the local optimizer and perturbation operator were introduced to enhance the algorithm's ability of optimality seeking.Simulation experiments were carried out on standard networks of different sizes respectively,and the accuracy and efficiency of the algorithm are improved compared with existing algorithms of the same type.
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