Multiple sequence alignment based on quantum-behaved particle swarm optimization and hidden Markov model

To cope with such limitations of Baum-Welch training HMM in multiple 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.