Improved binary quantum-behaved particle swarm optimization to protein folding
改进二进制量子粒子群算法在蛋白质折叠中的应用*

The existed protein folding algorithms in hydrophobic-polar model (HP model) are easily being trapped in local optima and can not obtain the minimum energy of protein folding conformation. To overcome the disadvantages,this paper proposed an improved binary quantum-behaved particle swarm optimization algorithm based on mutation operator. In the novel algorithm, introduced the binary coding to code amino acid sequence. Then proposed the mutation strategy to improve the premature phenomena. It adopted the punishing factor to avoid the overlapped protein folding. Tested some benchmark sequences to the proposed algorithm. The results of experiment show that the proposed technique can find the more excellent minimum energy of protein folding conformation than other algorithms. The proposed algorithm is practical and effective.