基于Memetic算法的DNA序列数据压缩方法  

DNA Sequence Data Compression Method Based on Memetic Algorithm

作　　者：谭丽[1] 孙季丰[1] 郭礼华[1] 

机构地区：[1]华南理工大学电子与信息学院,广州510641

出　　处：《电子与信息学报》2014年第1期121-127,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60902087);广州市科委新星计划项目(2010A 090100016)资助课题

摘　　要：该文提出一种基于CPMA(Collaborative Particle swarm optimization-based Memetic Algorithm)算法的DNA序列数据压缩方法,CPMA分别采用综合学习粒子群优化(Comprehensive Learning Particle Swarm Optimization,CLPSO)算法和动态调整的混沌搜索算子(Dynamic Adjustive Chaotic Search Operator,DACSO)进行全局搜索和局部搜索。该文采用CPMA寻找全局最优的基于扩展操作的近似重复矢量(Extended Approximate Repeat Vector,EARV)码书,并用此码书压缩DNA序列数据。实验结果表明,CPMA比其它优化算法有很大的改善,对文中采用的大部分测试函数,其解都非常接近全局最优点;对于DNA基准测序序列,与文中所列的经典DNA序列压缩算法相比,基于CPMA算法的压缩性能得到了显著提升。A DNA sequence compression method based on Collaborative Particle swarm optimization-based Memetic Algorithm （CPMA） is proposed. CPMA adopts the Comprehensive Learning Particle Swarm Optimization （CLPSO） as the global search and a Dynamic Adjustive Chaotic Search Operator （DACSO） as the local search respectively. In CPMA, it looks for the global optimal code book based on Extended Approximate Repeat Vector （EARV）, by which the DNA sequence is compressed. Experimental results demonstrate better performance of HMPSO than the other optimization algorithms, and it is very close to the global optimization point in most of the test functions adopted by the paper. The compression performance of the method based on CPMA is markedly improved compared to many of the classical DNA seauence comnression algorithms.

关 键 词：DNA序列压缩 MEMETIC算法 扩展的近似重复矢量(EARV) 粒子群优化(PSO) 动态混沌局部搜索 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]