采用生物信息机制的量子免疫克隆算法  

Quantum Immune Clonal Algorithm Based on Biologic Information Mechanism

作　　者：刘士荣[1] 张波涛[1,2] 

机构地区：[1]杭州电子科技大学自动化学院,杭州310018 [2]华东理工大学自动化研究所上海200237

出　　处：《模式识别与人工智能》2011年第3期391-399,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60675043);浙江省科技计划基金项目(No.2007C21051);杭州电子科技大学科研启动基金项目(No.KYS09150543)资助

摘　　要：随机变异会导致多克隆策略的基因进化的无序性,进而降低免疫克隆算法的效率.为解决此问题,文中设计了一种采用生物信息机制的量子免疫算法.这种算法将量子理论引入多克隆策略的变异过程以提高基因操作效率,同时采用一种生物信息机制来提高信息交互能力,加速抗体进化速度.从理论上证明该算法的收敛性.仿真试验结果表明,该基因操作方式能较大地提高免疫克隆算法的优化能力.与传统的量子免疫克隆算法、其它高级免疫克隆算法和进化算法相比,该算法具有较好的搜索能力和稳定性.The efficiency of immune clonal algorithm is greatly reduced by evolution randomness due to random mutations in polyclonal strategies. A quantum immune clonal algorithm based on biologic information mechanism （QICA-BIM） is proposed to solve the problem. In QICA-BIM, quantum computation is introduced into mutations of polyclonal strategies to facihtate genetic operation, and a biologic information mechanism is employed to improve information interaction and accelerate the speed of evolution. The convergence of QICA-BIM is then proved. Simulation results indicate that QICA-BIM significantly improves the optimizing capability of immune clonal algorithm. Besides, it has better search capability and higher stability compared with some other quantum-inspired immune clonal algorithms, advanced immune clonal algorithms and evolution algorithms.

关 键 词：克隆算法 量子计算 基因操作 全局优化 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]