caa a22 4500 605470340 CHVBK 20210128100327.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1338-1 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1338-1 A hybrid particle swarm optimization based memetic algorithm for DNA sequence compression [Elektronische Daten] [Li Tan, Jifeng Sun, Xueke Tong] Due to the recent advancements in high- throughput sequencing technologies, biomedical research is faced with ever increasing quantities of data, and the storage or transmission of the huge amount of data is one of the concerns. So, we presented a novel hybrid particle swarm optimization based memetic algorithm (HPMA) for DNA sequence compression. In HPMA, within the framework of the memetic algorithm, dynamic comprehensive learning particle swarm optimization method is used for global search, and two adaptive local search operators including center symmetry mutation differential evolution operator and adaptive chaotic search operator work in a cooperative way. HPMA looks for the global optimal code book based on extended approximate repeat vector, by which the DNA sequence will be compressed. Experiments were conducted on 19 high-dimensional functions and 11 real DNA sequences. The results show that HPMA is more competitive in both the performance and scalability, and also attains better compression ability than other representative DNA-specific algorithms on DNA sequence data. Springer-Verlag Berlin Heidelberg, 2014 Extended approximate repeat vector nationallicence Center symmetry mutation differential evolution operator nationallicence Adaptive chaotic search operator nationallicence DNA sequence compression nationallicence Tan Li School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Sun Jifeng School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Tong Xueke School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1255-1268 1432-7643 19:5<1255 2015 19 500 https://doi.org/10.1007/s00500-014-1338-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1338-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tan Li School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Sun Jifeng School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Tong Xueke School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1255-1268 1432-7643 19:5<1255 2015 19 500