结合局部结构学习的Bayesian优化算法

在Bayesian优化算法中Bayesian网络的学习是算法应用的关键,而Bayesian网络学习是一个NP-hard问题,并且计算量大。为了能够快速获得较稳定的Bayesian网络,提出了一种新的学习策略,在学习Bayes-ian网络结构时采用对局部结构的贪婪算法,并结合局部搜索利用打分测度选取最优边。对所提算法进行了分析,在算法复杂度较小的情况下,所学习的Bayesian网络可靠性明显提高,算法收敛速度加快,并且避免陷入局部最优。仿真研究表明文章所提出算法寻优能力优于传统Bayesian优化算法。

Improved bayesian optimization algorithm incorporated local structure learning

Learning the Bayesian networks is a key for a successful application of Bayesian optimization algorithm.However it is NP-hard to learn the Bayesian networks.In order to get the reliable Bayesian networks quickly,a novel learning strategy is presented in this paper.Firstly,the stochastic greedy algorithm for local structure is introduced according to the decomposable of scoring metric.The optimal edge is selected by using scoring metric and local search.Secondly,by analysis it is conclude that the reliability of the Bayesian networks which is learned by the proposed algorithm is improved.Due to the reliable network,BOA overcomes deceptive and performs efficiently.Experimental results show the improved algorithm's performances are better than those of traditional BOA.