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A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

  • Advances in Intelligent Data Processing and Analysis
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Abstract

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.

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Acknowledgments

The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions on the paper that greatly improve the quality of the paper. This work was supported in part by the National Science Council, Taiwan, R.O.C., under Grants NSC 102-2219-E-006-001-, NSC 100-2218-E-006-MY3 and NSC 102-2221-E-041-006.

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Authors and Affiliations

  1. Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

    Ko-Wei Huang, Jui-Le Chen & Chu-Sing Yang

  2. Department of Computer Science and Entertainment Technology, Tajen university, Pingtung, Taiwan, ROC

    Jui-Le Chen

  3. Department of Computer Science and Information Engineering, National Ilan University, Yilan, 26047, Taiwan, ROC

    Chun-Wei Tsai

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  1. Ko-Wei Huang
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Huang, KW., Chen, JL., Yang, CS. et al. A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem. Neural Comput & Applic 26, 495–506 (2015). https://doi.org/10.1007/s00521-014-1659-0

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