A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes
作者：
Highlights：
• This study evaluates additional chaotic maps combined with particle swarm optimization (PSO) to detect SNP barcodes using high-dimensional genomic data.
• We used nine chaotic maps to improve PSO and compared the searching ability amongst all versions of CPSO.
• The efficacy evaluation of both computational methods was based on the statistical values from the chi-square test (χ2).
• The results showed that chaotic maps could improve the searching ability of PSOs that have been trapped in a local optimum.
• Our results indicate that the Sinai chaotic map combined with PSO is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models.
摘要
•This study evaluates additional chaotic maps combined with particle swarm optimization (PSO) to detect SNP barcodes using high-dimensional genomic data.•We used nine chaotic maps to improve PSO and compared the searching ability amongst all versions of CPSO.•The efficacy evaluation of both computational methods was based on the statistical values from the chi-square test (χ2).•The results showed that chaotic maps could improve the searching ability of PSOs that have been trapped in a local optimum.•Our results indicate that the Sinai chaotic map combined with PSO is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models.
论文关键词：Particle swarm optimization,Chaos,Single nucleotide polymorphism,Single nucleotide polymorphism barcode detection
论文评审过程：Received 14 April 2016, Accepted 29 September 2016, Available online 30 September 2016, Version of Record 5 October 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.09.002
