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中文摘要：
针对用Baum-Welch算法训练隐马尔可夫模型用于序列比对算法的搜索空间有限性容易陷入局部最优点的缺陷，提出一种用量子粒子群优化算法训练隐马尔可夫模型的生物多序列比对新方法。该方法克服了Baum-Welch算法在收敛性能上的缺陷，在整个可行解空间中进行搜索。从BaliBASE数据库中选取测试例子进行数值实验，实验结果表明，所提算法优于Baum-Welch算法，对标准例子进行的实验证明了算法的有效性。
英文摘要：
To cope with such limitations of Baum-Welch training HMM in muhiple sequence alignment as finite sampling space, being easy to run into local optima, proposed a new HMM training method for multiple sequence alignment based on quantum-behaved particle swarm optimization （QPSO） algorithm. The new approach avoided the limitations of Baum-Welch training HMM, searched the feasible sampling space for the global optima. Examined the approach by using a set of standard instances taken from the benchmark alignment database, BAliBASE. Numerical simulation results are compared with those obtained by using the Baum-Welch training algorithm, the results of the comparisons show that the proposed algorithm improves the alignment abilities.
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