基于改进蚁狮优化的贝叶斯网络结构学习算法  

Bayesian Network Structure Learning Algorithm Based on Improved Ant Lion Optimization

作　　者：陈海洋[1] 尚珊珊 任智芳 刘静 张静 CHEN Haiyang;SHANG Shanshan;REN Zhifang;LIU Jing;ZHANG Jing(School of Electronic Information,Xi’an Polytechnic University,Xi’an 710048)

机构地区：[1]西安工程大学电子信息学院,西安710048

出　　处：《空军工程大学学报》2023年第2期104-111,共8页Journal of Air Force Engineering University

基　　金：国家自然科学基金(51905405)。

摘　　要：为了改善小数据集下BN结构学习中对数据利用不充分的缺陷,提高贝叶斯结构学习算法的寻优效率,提出基于改进蚁狮优化的贝叶斯网络结构学习算法。首先,通过互信息约束初步构建网络,并对蚁狮算法初始化;其次,为了有效利用小数据集,用改进的sigmoid函数对迭代中的矩阵元素进行二值转换;为了进一步提高蚁狮算法的搜索效率,用生物地理算法中的迁移、变异、清除算子抽取更换个别蚂蚁;最后,结合蚁狮算法的更新机制寻找最优解。实验结果表明,文中算法寻优效率高、收敛速度快,能跳出局部最优,具有更高的准确性。In order to improve the defect of insufficient data utilization in BN structure learning under small data sets,meanwhile,to improve the optimization efficiency of the Bayesian structure learning algorithm,the improved Bayesian network based on improved ant lion optimization structure learning algorithm(ISBALO)is proposed.Firstly,the network is Initially constructed through mutual information constraint,and the ant lion algorithm is initialized.Secondly,in order to effectively use the small data set,the matrix elements in the iteration are converted with improved sigmoid function.To further improve the search efficiency of ant lion algorithm,replace individual ants with the migration,variation and clearing operator in the biogeographic algorithm;Finally,combining update mechanism according to ant lion algorithm looking for the optimal solution.The experimental results show that the ISB-ALO has high optimization efficiency and fast convergence speed,which can jump out of the local optima and have higher accuracy.

关 键 词：贝叶斯网络 结构学习 互信息 蚁狮算法 SIGMOID函数 生物地理算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]