Limits...
Adaptive firefly algorithm: parameter analysis and its application.

Cheung NJ, Ding XM, Shen HB - PLoS ONE (2014)

Bottom Line: Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa.AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa.When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

View Article: PubMed Central - PubMed

Affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.

ABSTRACT
As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

Show MeSH
The kernel smoothing density estimate of (a) TM-Score, (b) GDT-TS-Score, (c) GDT-HA-Score, and (d) RMSD achieved over the native constrains.AdaFa-–AdaFa- were represented by red solid line, black dotted line, blue dotted dashed line, magenta dashed line, and green solid line, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4232507&req=5

pone-0112634-g009: The kernel smoothing density estimate of (a) TM-Score, (b) GDT-TS-Score, (c) GDT-HA-Score, and (d) RMSD achieved over the native constrains.AdaFa-–AdaFa- were represented by red solid line, black dotted line, blue dotted dashed line, magenta dashed line, and green solid line, respectively.

Mentions: As illustrated in Fig. S2 in File S1, the tertiary structures of the proteins listed in Table 3 were predicted by AdaFa with high accuracy from the native constrains of each protein. In the experiments over 30 proteins, the averaged RMSDs of AdaFa-–AdaFa- are Å, Å, Å, Å, and Å, respectively. To validate the robustness of AdaFa, the native constrains with 10% Gaussian white noise were used to predict the tertiary structures. According to the results in Fig. 9 and Fig. S3 in File S1, it can be seen that AdaFa variants (AdaFa-–AdaFa-) are able to construct the tertiary structure of the protein with high accuracy and good robustness, where the averaged RMSDs of AdaFa-–AdaFa- are Å, Å, Å, Å, and Å, respectively.


Adaptive firefly algorithm: parameter analysis and its application.

Cheung NJ, Ding XM, Shen HB - PLoS ONE (2014)

The kernel smoothing density estimate of (a) TM-Score, (b) GDT-TS-Score, (c) GDT-HA-Score, and (d) RMSD achieved over the native constrains.AdaFa-–AdaFa- were represented by red solid line, black dotted line, blue dotted dashed line, magenta dashed line, and green solid line, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232507&req=5

pone-0112634-g009: The kernel smoothing density estimate of (a) TM-Score, (b) GDT-TS-Score, (c) GDT-HA-Score, and (d) RMSD achieved over the native constrains.AdaFa-–AdaFa- were represented by red solid line, black dotted line, blue dotted dashed line, magenta dashed line, and green solid line, respectively.
Mentions: As illustrated in Fig. S2 in File S1, the tertiary structures of the proteins listed in Table 3 were predicted by AdaFa with high accuracy from the native constrains of each protein. In the experiments over 30 proteins, the averaged RMSDs of AdaFa-–AdaFa- are Å, Å, Å, Å, and Å, respectively. To validate the robustness of AdaFa, the native constrains with 10% Gaussian white noise were used to predict the tertiary structures. According to the results in Fig. 9 and Fig. S3 in File S1, it can be seen that AdaFa variants (AdaFa-–AdaFa-) are able to construct the tertiary structure of the protein with high accuracy and good robustness, where the averaged RMSDs of AdaFa-–AdaFa- are Å, Å, Å, Å, and Å, respectively.

Bottom Line: Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa.AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa.When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

View Article: PubMed Central - PubMed

Affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.

ABSTRACT
As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

Show MeSH