基于量子克隆选择算法的AB非格蛋白质折叠预测  

A Novel Quantum Clonal Selection Algorithm for AB Off-Lattice Model Protein Folding Structure Prediction

作　　者：田萍芳[1] 吴俊[1] 朱红兵[1] 

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430081

出　　处：《武汉大学学报（理学版）》2012年第2期144-148,共5页Journal of Wuhan University:Natural Science Edition

基　　金：国家自然科学基金资助项目(60803160);湖北省自然科学基金重点计划(2009CDA136);湖北省教育厅科研项目(Q20101110);武汉市科技局科技攻关项目(201110821225)资助

摘　　要：将免疫克隆选择算法与量子算法相结合的混合量子免疫算法应用于处理多极值和多变量的蛋白质折叠问题中.在克隆选择算法中引入免疫记忆细胞并加入量子双链编码方式以增加其搜索到全局最优值的概率.由于该算法易陷入局部最优,为改善该算法的性能而跳出局部最优解,将年龄算子引进到该算法中.实验结果表明,改进后的量子免疫算法在最低能量值和计算时间上与之前相比有明显的提高,而且年龄算子的加入在早熟收敛的改善上同样效果显著.A novel hybrid algorithm Quantum Immune（QI）,which combines Quantum Algorithm（QA） and Immune Clonal Selection（ICS） Algorithm,has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice model in the predicting 2D protein folding structure.Clonal Selection Algorithm was introduced into the hyper-mutation operators in the Quantum Algorithm to improve the local search ability,and double chains quantum coded was designed to enlarge the probability of the global optimization solution.It shows that the solution mostly trapped into the local optimum.To escape the local best solution the aging operator is introduced to improve the performance of the algorithm.Experimental results show that the lowest energies and computing-time of the improved Quantum Clonal Selection（QCS） algorithm are better than that of the previous methods,and the QCS is further improved by adding aging operator to combat the premature convergence.

关 键 词：量子算法 克隆选择 蛋白质折叠结构预测 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]