中文摘要：

贝叶斯网是人工智能中一个重要的理论模型，也是现实世界中不确定性问题建模的重要工具。针对贝叶斯网的结构学习问题，提出了一种将约束满足、蚁群优化和模拟退火策略相结合的混合算法。新算法首先利用阂值自调整的条件测试来动态地压缩搜索空间，在加速搜索过程的同时保证学习的求解质量；然后在基于MDL的蚁群随机搜索中引入模拟退火的优化调节机制，改进了算法的优化效率。实验结果验证了所提策略的有效性，与最新的同类算法相比，新算法在保持较快收敛速度的前提下具有更好的求解质量。

英文摘要：

Bayesian networks （BNs） are an important theory model within the community of artificial intelligence, and also a powerful formalism to model the uncertainty knowledge in the real world. Recently, learning a BN structure from data has received considerable attentions and researchers have proposed various learning algorithms. Especially, there are three efficient approaches, namely, genetic algorithm （GA）, evolutionary programming （EP）, and ant colony optimization （ACO）, which use the stochastic search mechanism to tackle the problem of learning Bayesian networks. A hybrid algorithm, combining constraint satisfaction, ant colony optimization and simulated annealing strategy together, is proposed in this paper. First, the new algorithm uses order-0 independence tests with a self-adjusting threshold value to dynamically restrict the search spaces of feasible solutions, so that the search process for ants can be accelerated while keeping better solution quality. Then, an optimization scheme based on simulated annealing is employed to improve the optimization efficiency in the stochastic search of ants. Finally, the algorithm is tested on different scale benchmarks and compared with the recently proposed stochastic algorithms. The results show that these strategies are effective, and the solution quality of the new algorithm precedes the other algorithms while the convergence speed is faster.