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A hybrid particle swarm optimization based memetic algorithm for DNA sequence compression

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Abstract

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.

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Acknowledgments

This work is supported by Guangdong Natural Science Foundation of P. R. China (Grant: 91510641 01000037).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Li Tan, Jifeng Sun & Xueke Tong

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  1. Li Tan
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  2. Jifeng Sun
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  3. Xueke Tong
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Correspondence to Li Tan.

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Communicated by V. Loia.

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Tan, L., Sun, J. & Tong, X. A hybrid particle swarm optimization based memetic algorithm for DNA sequence compression. Soft Comput 19, 1255–1268 (2015). https://doi.org/10.1007/s00500-014-1338-1

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