基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

刘浩然; 张力悦; 范瑞星; 王海羽; 张春兰

doi:10.11999/JEIT180653

基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

doi: 10.11999/JEIT180653

1.
燕山大学信息科学与工程学院秦皇岛 066004
2.
燕山大学河北省特种光纤与光纤传感重点实验室秦皇岛 066004

基金项目: 国家自然科学基金(51641609)

详细信息

作者简介:
刘浩然：男，1980年生，教授，博士生导师，研究方向为无线传感器网络、工业故障检测及预测

张力悦：男，1994年生，硕士生，研究方向为贝叶斯网络、工业故障检测及预测

范瑞星：男，1993年生，硕士生，研究方向为贝叶斯网络、工业故障检测及预测

王海羽：男，1993年生，硕士生，研究方向为群智能算法、贝叶斯网络、工业故障检测及预测

张春兰：女，1992年生，硕士生，研究方向为工业故障检测及预测

通讯作者:
刘浩然　liu.haoran@ysu.edu.cn

中图分类号: TP18
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- 被引次数: 0
出版历程
- 收稿日期: 2018-07-03
- 修回日期: 2019-01-15
- 网络出版日期: 2019-01-26
- 刊出日期: 2019-06-01

Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (51641609)

摘要

摘要: 针对当前贝叶斯网络结构学习算法易陷入局部最优和寻优效率低的问题，该文提出一种基于改进鲸鱼优化策略的贝叶斯网络结构学习算法。该算法首先提出一种新的方法建立较优的初始种群，然后利用不产生非法结构的交叉变异算子构建适用于贝叶斯网络结构学习的改进捕食行为，同时采用动态调节参数增强算法个体寻优的能力，通过适应度排序更新种群，最终获得最优的贝叶斯网络结构。仿真结果表明，该算法具有全局收敛性，寻优效率高，精确率高于其它同类优化算法。
- 贝叶斯网络结构学习 /
- 改进鲸鱼优化算法 /
- 改进捕食行为 /
- 动态调节参数
Abstract: A Bayesian network structure learning algorithm based on improved whale optimization strategy is proposed to solve the problem that the current Bayesian network structure learning algorithm is easily trapped in local optimal and is of low optimization efficiency. The improved algorithm proposes first a new method to establish a better initial population, and then it uses the cross mutation operator that does not produce the illegal structure to construct an improved predation behavior suitable for Bayesian network structure learning. At the same time, it adopts the dynamic parameter tuning strategy to enhance the individual search ability. The population is updated followed by the fitness order so that the optimal Bayesian network structure is obtained. Simulation results demonstrate that the algorithm has global convergence, high efficiency and higher accuracy than other similar optimization algorithms.
- Bayesian network structure learning /
- Improved whale optimization algorithm /
- Improved hunt behavior /
- Dynamic adjustment parameter

HTML全文

图 1 ALARM网络中IWOA算法不同措施的精确率对比

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图 2 不同网络中各算法精确率对比

下载: 全尺寸图片幻灯片

图 3 不同网络中各算法敏感度对比

下载: 全尺寸图片幻灯片

表 1 贝叶斯网络的标准评价量

评价量	物理意义
TP (True Positive，真正例)	得到的网络结构与标准网络结构相同的边的数量
TN (True Negative，真负例)	得到的网络结构与标准结构相同无边情况的数量
FP (False Positive，假正例)	得到的网络结构与标准结构相比增加的边的数量
FN (False Negative，假负例)	得到的网络结构与标准结构相比丢失的边的数量
Pre (Precision，精确率)	真正类别占正类别的比例
Sen (Sensitivity，敏感度)	真正类别占所有类别的比例
Score	网络结构的BIC得分
t	网络结构的执行时间

下载: 导出CSV

表 2 不同算法在ASIA网络中的对比

数据量	算法	TP	TN	FP	FN	t(s)	Score
500	IWOA	7.0	47.3	0.2	0.9	6.09	–1153.8$ \pm $2.13
	MAK	4.2	44.6	3.8	3.4	3.62	–1170.5$ \pm $4.46
	MMHC	6.7	47.2	1.1	1.2	3.54	–1157.1$ \pm $6.79
	AESL-GA	6.5	46.3	1.7	1.5	4.21	–1165.5$ \pm $9.12
1000	IWOA	7.2	47.6	0	0.8	6.28	–2314.8$ \pm $3.52
	MAK	4.6	45.0	3.4	3.2	3.70	–2317.5$ \pm $7.47
	MMHC	6.9	45.7	0.9	0.8	4.58	–2315.6$ \pm $6.45
	AESL-GA	6.4	46.4	1.5	1.6	6.02	–2317.2$ \pm $2.24
3000	IWOA	7.4	47.8	0	0.6	7.81	–6711.8$ \pm $5.77
	MAK	5.0	46.2	3.4	3.0	4.32	–6723.9$ \pm $5.84
	MMHC	7.0	46.9	0.7	0.6	6.27	–6713.3$ \pm $6.46
	AESL-GA	6.9	47.1	1.2	0.9	8.63	–6719.5$ \pm $7.31
5000	IWOA	7.6	47.9	0	0.4	9.00	–11201.0$ \pm $11.42
	MAK	5.0	46.6	3.4	3.0	5.31	–11212.9$ \pm $12.35
	MMHC	7.2	47.5	0.5	0.6	9.01	–11208.9$ \pm $15.57
	AESL-GA	7.1	47.7	1.0	0.8	9.56	–11210.4$ \pm $14.63

下载: 导出CSV

表 3 不同算法在ALARM网络中的对比

数据量	算法	TP	TN	FP	FN	t(s)	Score
500	IWOA	37.2	1271.6	8.4	1.4	205.72	–5575.61$ \pm $7.28
	NOK2	31.8	1269.5	10.1	5.6	35.91	–5374.76$ \pm $18.05
	MAK	32.2	1261.4	20.5	7.2	314.16	–5580.04$ \pm $10.02
	MMHC	27.6	1244.1	15.3	20.2	2647.78	–5598.67$ \pm $20.33
	AESL-GA	33.3	1274.7	11.3	12.7	2647.78	–5472.37$ \pm $15.35
1000	IWOA	37.6	1274.2	7.8	1.0	223.87	–10752.25$ \pm $5.35
	NOK2	32.2	1270.3	9.8	5.4	37.64	–10538.84$ \pm $20.20
	MAK	33.4	1265.2	19.8	5.8	356.10	–10771.08$ \pm $12.36
	MMHC	30.2	1250.4	10.8	15.2	3637.85	–10824.35$ \pm $15.89
	AESL-GA	35.4	1275.2	10.0	9.2	3637.85	–10964.18$ \pm $8.23
3000	IWOA	39.5	1279.5	5.6	0.4	476.02	–28340.28$ \pm $13.89
	NOK2	35.6	1271.9	7.2	4.0	57.35	–29043.43$ \pm $14.74
	MAK	36.4	1283.4	14.2	4.4	505.57	–29308.01$ \pm $14.20
	MMHC	32.6	1253.5	10.2	12.3	3963.62	–28939.91$ \pm $17.12
	AESL-GA	37.0	1277.1	6.9	7.0	3963.62	–29248.78$ \pm $19.30
5000	IWOA	40.8	1280.6	5.2	0.2	856.10	–47893.12$ \pm $17.66
	NOK2	36.2	1273.4	6.5	3.3	69.67	–48126.57$ \pm $19.58
	MAK	37.7	1270.9	10.7	3.7	948.41	–48259.15$ \pm $20.45
	MMHC	35.1	1277.8	7.5	8.0	5613.55	–48677.04$ \pm $19.52
	AESL-GA	38.3	1278.7	6.3	4.2	5613.55	–48148.56$ \pm $24.79

下载: 导出CSV

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