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.