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Neural Computing and Applications

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01.11.2014 | Original Article

A self-adaptive intelligent single-particle optimizer compression algorithm

verfasst von: Jie Zeng

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

This paper presents a self-adaptive intelligent single-particle optimizer (AdpISPO) for DNA sequence data compression codebook design. Featured with the crucial self-adaptive optimization process, AdpISPO is capable of attaining better fitness value than most existing particle swarm optimization variants with no specific parameters required. A novel DNA sequence data compression algorithm, namely BioSqueezer, is proposed in this paper. Introducing all the unique data features in constructing the compression codebook, BioSqueezer compresses DNA sequences by replacing similar fragments with the index of its corresponding code vector. For attaining higher compression ratio, the AdpISPO is employed in BioSqueezer for the codebook design. Experimental results on benchmark DNA sequences demonstrate that BioSqueezer attains better performance than other state-of-the-art DNA compression algorithms.

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Titel: A self-adaptive intelligent single-particle optimizer compression algorithm
verfasst von: Jie Zeng
Publikationsdatum: 01.11.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1609-x

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