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中文摘要：
为了改善已有二维HP模型蛋白质折叠算法容易陷入局部最优、找不到理论最低能量构象的缺点,提出一种基于变异算子的改进二进制量子粒子群算法。采用二进制编码蛋白质序列,提出变异策略,并采用惩罚因子避免出现蛋白质重叠,最后将该算法应用于蛋白质序列进行测试。测试结果表明,改进算法能够找到更优的结果,算法具有一定的实用性和有效性。
英文摘要：
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.
同期刊论文项目
同项目期刊论文
A Quantum-behaved Particle Swarm Optimization with Diversity-guided Mutation for Design of Two-Dimen
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