caa a22 4500 606159452 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1659-0 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1659-0 A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem [Elektronische Daten] [Ko-Wei Huang, Jui-Le Chen, Chu-Sing Yang, Chun-Wei Tsai] Determining the sequence of a long DNA chain first requires dividing it into subset fragments. The DNA fragment assembly (DFA) approach is then used for reassembling the fragments as an NP-hard problem that is the focus of increasing attention from combinatorial optimization researchers within the computational biology community. Particle swarm optimization (PSO) is one of the most important swarm intelligence meta-heuristic optimization techniques to solve NP-hard combinatorial optimization problems. This paper proposes a memetic PSO algorithm based on two initialization operators and the local search operator for solving the DFA problem by following the overlap-layout-consensus model to maximize the overlapping score measurement. The results, based on 19 coverage DNA fragment datasets, indicate that the PSO algorithm combining tabu search and simulated annealing-based variable neighborhood search local search can achieve the best overlap scores. The Natural Computing Applications Forum, 2014 DNA sequence nationallicence Fragment assembly problem nationallicence Meta-heuristic optimization algorithm nationallicence Particle swarm optimization nationallicence Memetic algorithm nationallicence Huang Ko-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut Chen Jui-Le Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut Tsai Chun-Wei Department of Computer Science and Information Engineering, National Ilan University, 26047, Yilan, Taiwan, ROC aut Neural Computing and Applications Springer London 26/3(2015-04-01), 495-506 0941-0643 26:3<495 2015 26 521 https://doi.org/10.1007/s00521-014-1659-0 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/s00521-014-1659-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Ko-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut NATIONALLICENCE 700 1- Chen Jui-Le Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut NATIONALLICENCE 700 1- Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC aut NATIONALLICENCE 700 1- Tsai Chun-Wei Department of Computer Science and Information Engineering, National Ilan University, 26047, Yilan, Taiwan, ROC aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 495-506 0941-0643 26:3<495 2015 26 521